Name: Python Machine Learning Course, 1-Days, Online Attendance
Price: 185 GBP
Availability: InStock

<strong>Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis</strong>
        <p>In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.

The course consists of 7 sections that will help you master Python machine learning. You'll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you'll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You'll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You'll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you'll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you'll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.
By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python.

All code and supporting files for this course are available at 
https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.</p>
        <h2>What You Will Learn</h2><p>Harness the power of Anaconda/iPython for practical data science<br>Read data into the Python environment from different sources<br>Carry out basic data preprocessing and wrangling in Python<br>Implement unsupervised/clustering techniques such as k-means clustering<br>Get to grips with dimensionality reduction techniques and feature selection<br>Implement supervised learning/classification techniques such as random forests<br>Explore neural network- and deep learning-based classification<br></p>
        <h2>Audience</h2><p>If you want to build data science applications in the Python environment, this book is for you. You'll also find this book helpful if you want to learn how to implement unsupervised learning on real data using Python, or if you're a student looking to get started with artificial neural networks and deep learning.</p>
        <h2>Approach</h2><p>This course is full of interesting and illustrative examples and easy-to-understand theory to help you implement a real-world concept in every lecture. It covers hands-on methods to simplify and address even the most difficult concepts in Python using minimal jargon.</p>
        <h2>Key Features</h2><p>Explore the most important Python data science concepts and packages, including Pandas * Master the Anaconda framework and use it to implement clustering and classification models on your data * Get to grips with data science fundamentals and understand which models should be used when * </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in Python</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Welcome to Clustering & Classification with Machine Learning in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning?</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: What is Machine Learning?</p></td></tr><tr><td style='padding: 15px;'><p>3. Python Data Science Environment</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Environment</p></td></tr><tr><td style='padding: 15px;'><p>4. For Mac Users</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: For Mac Users</p></td></tr><tr><td style='padding: 15px;'><p>5. IPython in Browser</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: IPython in Browser</p></td></tr><tr><td style='padding: 15px;'><p>6. Python Data Science Packages to Be Used</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Packages to Be Used</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources with Pandas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are Pandas?</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: What are Pandas?</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Data from CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from CSV</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Excel Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in HTML Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in HTML Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Databases</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from Databases</p></td></tr></tbody></table><br><h4>3. Data Cleaning & Munging</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Cleaning & Munging: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. Conditional Data Selection</p><p style='margin: 0'>Data Cleaning & Munging: Conditional Data Selection</p></td></tr><tr><td style='padding: 15px;'><p>3. Data Grouping</p><p style='margin: 0'>Data Cleaning & Munging: Data Grouping</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Sub setting</p><p style='margin: 0'>Data Cleaning & Munging: Data sub setting</p></td></tr><tr><td style='padding: 15px;'><p>5. Ranking & Sorting</p><p style='margin: 0'>Data Cleaning & Munging: Ranking & Sorting</p></td></tr><tr><td style='padding: 15px;'><p>6. Concatenate</p><p style='margin: 0'>Data Cleaning & Munging: Concatenate</p></td></tr><tr><td style='padding: 15px;'><p>7. Merging & Joining Data Frames</p><p style='margin: 0'>Data Cleaning & Munging: Merging & Joining Data Frames</p></td></tr></tbody></table><br><h4>4. Unsupervised Learning in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Unsupervised Classification- Some Basic Concepts</p><p style='margin: 0'>Unsupervised Learning in Python: Unsupervised Classification- Some Basic Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. K-Means Clustering: Theory</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering: Theory</p></td></tr><tr><td style='padding: 15px;'><p>3. Implement K-Means on the Iris Data</p><p style='margin: 0'>Unsupervised Learning in Python: Implement K-Means on the Iris Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Quantifying K-Means Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in Python: Quantifying K-Means Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>5. K-Means Clustering with Real Data</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering with Real Data</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Select the Optimal Number of Clusters?</p><p style='margin: 0'>Unsupervised Learning in Python: How to Select the Optimal Number of Clusters?</p></td></tr><tr><td style='padding: 15px;'><p>7. Gaussian Mixture Modelling (GMM)</p><p style='margin: 0'>Unsupervised Learning in Python: Gaussian Mixture Modelling (GMM)</p></td></tr><tr><td style='padding: 15px;'><p>8. Hierarchical Clustering-theory</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Hierarchical Clustering-practical</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-practical</p></td></tr></tbody></table><br><h4>5. Dimension Reduction & Feature Selection for Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Principal Component Analysis (PCA)-Theory</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)-Case Study 1</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA)-Case Study 2</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 2</p></td></tr><tr><td style='padding: 15px;'><p>4. Linear Discriminant Analysis (LDA) for Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Linear Discriminant Analysis (LDA) for Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>5. t-SNE Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: t-SNE Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>6. Feature Selection to Select the Most Relevant Predictors</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Feature Selection to Select the Most Relevant Predictors</p></td></tr><tr><td style='padding: 15px;'><p>7. Recursive Feature Elimination (RFE)</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Recursive Feature Elimination (RFE)</p></td></tr></tbody></table><br><h4>6. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Concepts Behind Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Concepts Behind Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preparation for Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Data Preparation for Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Pointers on Evaluating the Accuracy of Classification Modelling</p><p style='margin: 0'>Supervised Learning: Classification: Pointers on Evaluating the Accuracy of Classification Modelling</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Logistic Regression as a Classification Model</p><p style='margin: 0'>Supervised Learning: Classification: Using Logistic Regression as a Classification Model</p></td></tr><tr><td style='padding: 15px;'><p>5. kNN- Classification</p><p style='margin: 0'>Supervised Learning: Classification: kNN- Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. Naive Bayes Classification</p><p style='margin: 0'>Supervised Learning: Classification: Naive Bayes Classification</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. SVM- Linear Classification</p><p style='margin: 0'>Supervised Learning: Classification: SVM- Linear Classification</p></td></tr><tr><td style='padding: 15px;'><p>9. Non-Linear SVM Classification</p><p style='margin: 0'>Supervised Learning: Classification: Non-Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>10. RF-Classification</p><p style='margin: 0'>Supervised Learning: Classification: RF-Classification</p></td></tr><tr><td style='padding: 15px;'><p>11. Gradient Boosting Machine (GBM)</p><p style='margin: 0'>Supervised Learning: Classification: Gradient Boosting Machine (GBM)</p></td></tr><tr><td style='padding: 15px;'><p>12. Voting Classifier</p><p style='margin: 0'>Supervised Learning: Classification: Voting Classifier</p></td></tr></tbody></table><br><h4>7. Neural Networks and Deep Learning Based Classification Techniques</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Perceptrons for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Perceptrons for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>2. Artificial Neural Networks (ANN) for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Artificial Neural Networks (ANN) for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-class Classification With MLP</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Multi-class Classification With MLP</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to H20</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Introduction to H20</p></td></tr><tr><td style='padding: 15px;'><p>5. Use H20 for Deep Learning Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Use H20 for Deep Learning Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. H20 Deep Learning for Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: H20 Deep Learning for Classification</p></td></tr></tbody></table>

Packt

Python

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Clustering and Classification with Machine Learning in Python

General Admission

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					<div><p>Python is a powerful and versatile programming language that is used for a wide variety of tasks. In this course, you will learn the basics of Python, including variables, data types, control flow, functions, and modules. You will also learn about object-oriented programming and how to use Python to build web applications. Python is a great language for beginners because it is easy to learn and use. It is also a very popular language, so there are many resources available to help you learn.</p></div>
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                                <h2>Course Curriculum</h2>
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					<td>Classes</td><td><span>🕐 00:09:00</span></td>
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					<td>▶</td>
					<td>Object Initialization</td><td><span>🕐 00:05:00</span></td>
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					<td>▶</td>
					<td>Class Static Members</td><td><span>🕐 00:07:00</span></td>
				</tr>
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					<td>▶</td>
					<td>Classic Inheritance</td><td><span>🕐 00:10:00</span></td>
				</tr>
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					<td>▶</td>
					<td>Data Hiding</td><td><span>🕐 00:07:00</span></td>
				</tr>
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					<td colspan="4"><strong>Mock Exam</strong></td>
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					<td><i></i></td>
					<td>Mock Exam- Python 3 for Beginners</td><td><span>🕐 00:20:00</span></td>
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					<td colspan="4"><strong>Final Exam</strong></td>
				</tr>
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					<td><i></i></td>
					<td>Final Exam- Python 3 for Beginners</td><td><span>🕐 00:20:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Order Your Certificate</strong></td>
				</tr>
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					<td><i></i></td>
					<td>Order Your Certificate</td><td><span>🕐 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Skill Up

Dive into the world of coding! Learn Python from scratch, solve real-world problems, make cool projects. No prior experience needed. Start your coding journey now!

Python for Beginners Online Course

<strong>Master Data Science skills using Python and real time project and go from Beginner to Super Advance level</strong>
        <p>Want to become a good Data Scientist?  Then this is a right course for you.
This course was designed by IT professionals with a Master's in Mathematics and Data Science.  We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.
We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Data-Statistics-with-Full-Stack-Python</p>
        <h2>What You Will Learn</h2><p>Master Data Science on Python<br>Learn to use Numpy and Pandas for Data Analysis<br>Learn all the math you need to understand Machine Learning algorithms<br>Real-world case studies<br>Learn to use Matplotlib for Python plotting<br>Learn to use Seaborn for statistical plots<br>Master end-to-end data science solutions<br>Learn all statistical concepts you need to become a Machine Learning ninja </p>
        <h2>Audience</h2><p>This course is for anyone who wants to become a Data Scientist.</p>
        <h2>Approach</h2><p>Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Master complex theories, algorithms, and coding libraries in a very simple way * Master Data Science in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/data-statistics-with-full-stack-python</p>
        <h2>About the Author</h2><h3>Geekshub Pvt. Ltd. </h3><p>Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.</p>
        <h2>Course Outline</h2><h4>1. Python Fundamentals</h4><p></p><br><h4>2. Numpy</h4><p></p><br><h4>3. Pandas</h4><p></p><br><h4>4. Some Fun With Maths</h4><p></p><br><h4>5. Inferential Statistics</h4><p></p><br><h4>6. Hypothesis Testing</h4><p></p><br><h4>7. Data Visualization</h4><p></p><br><h4>8. Exploratory Data Analysis</h4><p></p><br><h4>9. Simple Linear Regression</h4><p></p><br><h4>10. Real World Problem - Investment Requirement Analysis for a Company</h4><p></p><br><h4>11. Loan Analysis Project</h4><p></p>

Statistics

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

Data Statistics with Full Stack Python

<section>
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					<div><p>Python Programming from Scratch with MySQL Database is a comprehensive course that will teach you the fundamentals of Python programming and MySQL databases. You will learn to create and manipulate data, write functions and classes, and build web applications. By the end of the course, you will have the skills to develop your own database-driven applications using Python and MySQL.</p><p>Unlock the full potential of web development by joining our 'Python Programming from Scratch with MySQL Database' course. This well-structured program will guide you through the realms of HTML, Python, and MySQL, equipping you with the essential tools and knowledge to conquer the digital world. From the roots of HTML to Python's complexities and MySQL's intricacies, we cover it all.</p><p>But that's not all! We've intricately woven lessons on using Python for data science, giving you a competitive edge in today's data-driven environment. And when it comes to database management, our MySQL sections will turn you into a pro, teaching you everything from creating structures and filtering data to utilizing functions and managing databases with precision.</p><p>So, whether you're a beginner eager to step into the world of programming or an experienced coder looking to enhance your skillset, our course has something for everyone. With our expertly crafted modules, practical insights, and a project-based approach, you'll gain knowledge and apply what you've learned in real-world scenarios. Enroll now and embark on a journey that promises to be not just educational, but transformative, equipping you with the skills and confidence to make your mark in the digital universe.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
				</div>
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					<ul>
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										<span>Develop a solid foundation in HTML and Python programming.</span>
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										<span>Understand the intricacies of Python strings, operators, and data structures.</span>
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										<span>Master the usage of Python for data science applications.</span>
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										<span>Gain proficiency in MySQL database management and queries.</span>
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										<span>Learn to create and manage database structures effectively.</span>
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										<span>Acquire the skills to backup and restore MySQL databases.</span>
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										<span>Implement learned concepts through a Python project.</span>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Comprehensive coverage of HTML, Python, and MySQL in one course.</span>
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										<span>Learn from meticulously crafted modules catering to all skill levels.</span>
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										<span>Master essential skills for web development and database management.</span>
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										<span>You will learn from an experienced instructor who is passionate about teaching.</span>
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										<span>You will join a community of students who are also learning Python and MySQL.</span>
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			<h2> <b>Career Path</b></h2>		</div>
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			<h2> <b>Requirements</b></h2>		</div>
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										<span>A computer with internet access.</span>
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										<span>Eagerness to learn and apply new skills.</span>
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		</section>

Python Programming from Scratch with MySQL Database is a beginner-friendly course designed to teach you everything you need to know to start with Python programming and MySQL databases. Using these powerful tools, you'll learn how to build dynamic web applications and websites.

Python Programming from Scratch with My SQL Database

<strong>With this course, you will kick start your journey of creating awesome GUI applications in Python using the Tkinter library. During the process, you will become familiar with the GUI widgets, application layout, and executable files.</strong>
        <p>Have you learned the fundamentals of Python and then asked yourself 'What next?'. If so, consider taking this course, which will help you create your own Python GUI applications.

The course starts with the installation process of Python on your machines and then takes you through the basics of GUI widgets. Once you are settled down, you will learn the concepts of Python by building interesting applications such as Metric Helper, Color Theme Maker, Morse Code Translator, Simon Memory Game, and a lot more. By building these applications, you will learn how to manage the application layout, understand the functionality of the call APIs, and will find out how to create stand-alone executable files that will run on any Windows machine.

By the end of this course, you will be well-versed with the fundamentals of GUI application development using Python's Tkinter library.

The code bundle for this course is available at https://github.com/PacktPublishing/The-Art-of-Doing-Create-10-Python-GUIs-with-Tkinter-Today</p>
        <h2>What You Will Learn</h2><p>Create a NASA Astronomy Picture of the Day (APOD) application<br>Create a Morse Code application to convert and play messages in Morse code<br>Create a calculator application to help with basic mathematics<br>Create a color application to create your own color pallets<br>Create a notepad application to write your own thoughts<br>Package your Python scripts into executable files</p>
        <h2>Audience</h2><p>If you are new to application development, or an intermediate, or a student who is looking to build interesting GUI applications using Python's Tkinter library, this course is for you. To get started with this course, a basic understanding of Python is needed.</p>
        <h2>Approach</h2><p>In this course, you will develop unique, real-world applications to become familiar with the fundamentals of Python's GUI library, Tkinter.</p>
        <h2>Key Features</h2><p>Understand the fundamentals of the Tkinter library * Learn how to design the layout and functionality of modern GUI applications * Get ready to build eleven engaging and purposeful GUI applications</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/The-Art-of-Doing-Create-10-Python-GUIs-with-Tkinter-Today</p>
        <h2>About the Author</h2><h3>Michael Eramo</h3><p>Michael Eramo is a dedicated lifelong learner, experienced educator, and self-taught programmer. With official bachelor's degrees in music, education, and physics, as well as a master's in mathematics, he brings a diverse skill set to his teaching. As a Microsoft certified software developer, Michael has years of experience as a high school physics and computer science teacher, as well as a college mathematics teacher. He is also an esteemed member of the New York State Master-Teacher Program, a network of over 800 outstanding public-school teachers who share a passion for STEM learning and collaborate to inspire the next generation of STEM leaders.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Preview</p><p style='margin: 0'>This video presents a preview of the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing Python</p><p style='margin: 0'>This video explains how to install Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Installing Visual Studio Code</p><p style='margin: 0'>This video explains how to install Visual Studio Code.</p></td></tr></tbody></table><br><h4>2. The Basics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Basics: Windows</p><p style='margin: 0'>This video focuses on Windows with the Python GUI apps.</p></td></tr><tr><td style='padding: 15px;'><p>2. Basics: Labels and The Pack System</p><p style='margin: 0'>This video explains labels and the pack system.</p></td></tr><tr><td style='padding: 15px;'><p>3. Basics: Buttons and the Grid System</p><p style='margin: 0'>This video explains the buttons and the grid system.</p></td></tr><tr><td style='padding: 15px;'><p>4. Basics: Frames</p><p style='margin: 0'>This video focuses on frames.</p></td></tr><tr><td style='padding: 15px;'><p>5. Basics: Entries, Functions, and Lambdas</p><p style='margin: 0'>This video explains the concept of entries, functions, and lambdas.</p></td></tr><tr><td style='padding: 15px;'><p>6. Basics: Radio Buttons</p><p style='margin: 0'>This video focuses on the radio buttons.</p></td></tr><tr><td style='padding: 15px;'><p>7. Basics: Images</p><p style='margin: 0'>This video shows the images we can use in the apps.</p></td></tr></tbody></table><br><h4>3. Application 0: Hello GUI World</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Hello GUI World 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on GUI.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hello GUI World 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on GUI.</p></td></tr><tr><td style='padding: 15px;'><p>3. Hello GUI World 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on GUI.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hello GUI World 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on GUI.</p></td></tr></tbody></table><br><h4>4. Application 1: Metric Helper</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Metric Helper 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Metric Helper application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Metric Helper 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Metric Helper application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Metric Helper 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Metric Helper application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Metric Helper 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Metric Helper application.</p></td></tr></tbody></table><br><h4>5. Application 2: Simple Checklist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Simple Checklist 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simple Checklist application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Simple Checklist 1</p><p style='margin: 0'>BuildiThis video is a part of a series of videos that focuses on the Simple Checklist application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Simple Checklist 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simple Checklist application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Simple Checklist 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simple Checklist application.</p></td></tr></tbody></table><br><h4>6. Application 3: Simple Calculator</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Simple Calculator 0</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Simple Calculator application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Simple Calculator 1</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Simple Calculator application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Simple Calculator 2</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Simple Calculator application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Simple Calculator 3</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Simple Calculator application.</p></td></tr></tbody></table><br><h4>7. Application 4: Notepad</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Notepad 0</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Notepad application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Notepad 1</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Notepad application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Notepad 2</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Notepad application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Notepad 3</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Notepad application.</p></td></tr></tbody></table><br><h4>8. Application 5: Color Theme Maker</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Color Theme Maker 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Color Theme Maker application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Color Theme Maker 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Color Theme Maker application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Color Theme Maker 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Color Theme Maker application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Color Theme Maker 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Color Theme Maker application.</p></td></tr><tr><td style='padding: 15px;'><p>5. Color Theme Maker 4</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Color Theme Maker application.</p></td></tr></tbody></table><br><h4>9. Application 6: Morse Code Translator</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Morse Code Translator 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Morse Code Translator application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Morse Code Translator 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Morse Code Translator application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Morse Code Translator 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Morse Code Translator application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Morse Code Translator 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Morse Code Translator application.</p></td></tr></tbody></table><br><h4>10. Application 7: Simon Memory Game</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Simon Memory Game 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simon Memory Game application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Simon Memory Game 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simon Memory Game application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Simon Memory Game 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simon Memory Game application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Simon Memory Game 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Simon Memory Game application.</p></td></tr></tbody></table><br><h4>11. Application 8: Gravity Simulation</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Gravity Simulation 0</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Gravity Simulation application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Gravity Simulation 1</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Gravity Simulation application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Gravity Simulation 2</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Gravity Simulation application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Gravity Simulation 3</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Gravity Simulation application.</p></td></tr><tr><td style='padding: 15px;'><p>5. Gravity Simulation 4</p><p style='margin: 0'>This video is a part of series of videos that focuses on the Gravity Simulation application.</p></td></tr></tbody></table><br><h4>12. Application 9: Weather Forecast</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Weather Forecast 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Weather Forecast application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Weather Forecast 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Weather Forecast application.</p></td></tr><tr><td style='padding: 15px;'><p>3. Weather Forecast 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Weather Forecast application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Weather Forecast 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Weather Forecast application.</p></td></tr><tr><td style='padding: 15px;'><p>5. Weather Forecast 4</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the Weather Forecast application.</p></td></tr></tbody></table><br><h4>13. App 10: Astronomy Picture of the Day (APOD Viewer)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. APOD Viewer 0</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the APOD Viewer application.</p></td></tr><tr><td style='padding: 15px;'><p>2. APOD Viewer 1</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the APOD Viewer application.</p></td></tr><tr><td style='padding: 15px;'><p>3. APOD Viewer 2</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the APOD Viewer application.</p></td></tr><tr><td style='padding: 15px;'><p>4. APOD Viewer 3</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the APOD Viewer application.</p></td></tr><tr><td style='padding: 15px;'><p>5. APOD Viewer 4</p><p style='margin: 0'>This video is a part of a series of videos that focuses on the APOD Viewer application.</p></td></tr></tbody></table><br><h4>14. Create Your Executable Files Using "Auto Py To Exe"</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Installing "Auto Py To Exe" and Building Your First Executable File</p><p style='margin: 0'>This video explains how to create your executable files and how to install "Auto Py To Exe."</p></td></tr><tr><td style='padding: 15px;'><p>2. Building a Second Executable File</p><p style='margin: 0'>This video explains how to create a second executable file using "Auto Py To Exe."</p></td></tr><tr><td style='padding: 15px;'><p>3. A Small Change to Build the APOD Viewer Executable File</p><p style='margin: 0'>This video explains how to make changes to build the APOD viewer executable files.</p></td></tr><tr><td style='padding: 15px;'><p>4. Thank You!</p><p style='margin: 0'>This is the last video of the course with a short thank you note.</p></td></tr></tbody></table>

Programming

With this course, you will kick start your journey of creating awesome GUI applications in Python using the Tkinter library. During the process, you will become familiar with the GUI widgets, application layout, and executable files.

The Art of Doing: Create 10 Python GUIs with Tkinter Today!

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					<div><p>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Python 3.9 Coding has been specially designed to help learners gain a good command of Python 3.9 Coding, providing them with a solid foundation of knowledge to become a qualified professional.</p><p>Through this Python 3.9 Coding, you will gain both practical and theoretical understanding of Python 3.9 Coding that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</p><p>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</p></div>
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										<span>Access to course contents that are designed and prepared by industry professionals</span>
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										<span>Study at your convenient time and from wherever you want</span>
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										<span>You will receive a completion certificate upon completing the course.</span>
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										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
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										<span>You will learn the researched and proven approach adopted by successful people to transform their careers.</span>
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										<span>You will be able to incorporate various techniques successfully and understand your customers better.</span>
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										<span>Access to a computer or digital device with internet connectivity.</span>
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                                <h2>Course Curriculum</h2>
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					<td>Module 01: Introduction</td><td><span>🕐 00:02:00</span></td>
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Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Python 3.9 Coding

<strong>Take your Python skills to the next level. Learn how expert programmers work with code and the techniques they use.</strong>
        <p>So you have taken an initial programming course or have learned the basics on your own. However, when you look at other people's code, or listen to their discussions, you see many concepts that seem alien and that you don't understand. So, the question is: How do you go from a novice programmer to an expert? How do you become a professional?

This course answers this question. In it, we will build on top of your existing basic understanding of the Python language (and programming in general). We will cover concepts that will take you to the next level of programming expertise. These will include language constructs that are typically not covered in a beginner-level course-concepts such as generators, decorators, callbacks, higher-order functions, context managers, and more.
We will also discuss some tools that are not difficult but are essential to the life of a professional programmer. An example of this is logging for tracking down bugs, a simple technique that is used in all production-level software but is never touched upon in typical programming courses. We will discuss parallel programming, multi-threading, and synchronization issues-another important concept you must understand to code in a production environment. We discuss these through a case study to explain WHY you need them as well as HOW to use them.
Closely related to this, a highly important concept in today's programming practice is the idea of asynchronous programming, more commonly known as asyncio. Python and JavaScript, two of the most influential languages in today's development, are heavily focused on this new paradigm and we will cover this in a very intuitive, easy-to-understand manner.

All the code files are placed at https://github.com/PacktPublishing/Beyond-Basic-Programming---Intermediate-Python</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Cross-cutting tools (logging and testing)<br>&bull;&nbsp;&nbsp;Caveats in using lists <br>&bull;&nbsp;&nbsp;Intermediate Python concepts such as decorators, generators, and context managers<br>&bull;&nbsp;&nbsp;Parallel programming, multithreading, synchronization, and locks <br>&bull;&nbsp;&nbsp;Asynchronous programming (asyncio) and data plotting <br>&bull;&nbsp;&nbsp;Regular expressions (and how to avoid headaches when using them)</p>
        <h2>Audience</h2><p>Anyone who has a basic understanding of Python but wants to take their skills to the next level; beginners who want to learn the tools and techniques used by expert professional programmers; anyone who wants to learn about cross-cutting tools and skills that are useful in many programming languages and to learn the latest programming concepts, such as context managers, asynchronous programming, and regular expressions in a modular way.</p>
        <h2>Approach</h2><p>For all concepts, this course is designed into a case-based teaching and Problem-based Learning approach and explains why we need the concepts we're using and how everything fits together.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Take your Python skill to the next level * &bull;&nbsp;&nbsp;Master cross-cutting tools (logging and testing) * &bull;&nbsp;&nbsp;Use asynchronous programming (asyncio) * &bull;&nbsp;&nbsp;Plot data * &bull;&nbsp;&nbsp;Modular Regular Expressions </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/beyond-basic-programming---intermediate-python</p>
        <h2>About the Author</h2><h3>Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.

He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Cross-cutting Tools</h4><p></p><br><h4>3. Intermediate Programming Concepts</h4><p></p><br><h4>4. Time Saving Features</h4><p></p><br><h4>5. Parallel and Asynchronous Programming</h4><p></p><br><h4>6. Functional Programming</h4><p></p><br><h4>7. Applications</h4><p></p>

Take your Python skills to the next level. Learn how expert programmers work with code and the techniques they use.

Beyond Basic Programming - Intermediate Python

<p><strong>Get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now</strong></p> <p>Give a compliment to your career and take it to the next level. This <strong>Python Data Science </strong>will provide you with the essential knowledge and skills required to shine in your professional career. Whether you want to develop skills for your next job or want to elevate skills for your next promotion, this <strong>Python Data Science </strong>will help you keep ahead of the pack.</p> <p>The <strong>Python Data Science </strong>incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can reinforce your professional skills and practical knowledge, reaching out to the level of expertise required for your position. Further, this <strong>Python Data Science </strong>will add extra value to your resume to stand out to potential employers.</p> <p>Along with this course<strong>,</strong> you will get 10 other premium courses. Also, you will get an original Hardcopy and PDF certificate for the title course and a student ID card absolutely free.</p> <p><strong>This Bundle Consists of the following Premium courses: </strong></p> <ul> <li><strong> Course 1: Python Programming for Everybody<br /></strong></li><li><strong> Course 2: Higher Order Functions in Python - Level 03<br /></strong></li><li><strong> Course 3: Learn to Use Python for Spatial Analysis in ArcGIS<br /></strong></li><li><strong> Course 4: Data Structures Complete Course<br /></strong></li><li><strong> Course 5: Basic Google Data Studio<br /></strong></li><li><strong> Course 6: Data Analytics with Tableau<br /></strong></li><li><strong> Course 7: Business Intelligence and Data Mining Masterclass<br /></strong></li><li><strong> Course 8: SQL Programming Complete Bundle<br /></strong></li><li><strong> Course 9:  Decision Making and Critical Thinking<br /></strong></li><li><strong> Course 10: Time Management</strong></li></ul> <p>Enrol now,to advance your career, and use the premium study materials from Apex Learning.</p> <p><strong>Benefits you'll get from choosing Apex Learning for this Python Data Science:</strong></p> <ul> <li>Pay once and get lifetime access <strong>to 11 CPD courses </strong>including this Python Data Science Course</li><li>Free e-Learning App for engaging reading materials &amp; helpful assistance</li><li>Certificates, student ID for the course included in a one-time fee</li><li>Free up your time - don't waste time and money travelling for classes</li><li>Accessible, informative modules designed by expert instructors</li><li>Learn about at your ease - anytime, from anywhere</li><li>Study from your computer, tablet or mobile device</li><li>CPD accredited course - improve the chance of gaining professional skills</li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Python Data Science expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes.</p> <p><strong>Curriculum:</strong></p> <p><strong>Course 1: Data Science with Python<br /></strong></p> <ul> <li>Introduction</li><li>Data Cleaning Packages</li><li>Data Visualization packages</li></ul> <p><strong>Course 2: Python Programming for Everybody</strong></p> <p>Module 01<strong><br /></strong></p> <ul> <li>A Installing Python</li><li>Documentation</li><li>Command Line</li><li>Variables</li><li>Simple Syntax</li><li>Keywords</li><li>Import Module</li></ul> <p>Module 02</p> <ul> <li>Additional Topics</li><li>If Elif Else</li><li>Iterable</li><li>For</li><li>Loops</li><li>Execute</li><li>Exceptions</li></ul> <p>Module 03</p> <ul> <li>Data Types</li><li>Number Types</li><li>More Number Types</li><li>Strings</li><li>More Strings</li><li>Files</li><li>Lists</li><li>Dictionaries</li><li>Tuples</li><li>Sets</li></ul> <p>Module 04</p> <ul> <li>Comprehensions</li><li>Definitions</li><li>Functions</li><li>Default Arguments</li><li>Doc Strings</li><li>Variadic Functions</li><li>Factorial</li></ul> <p>Module 05</p> <ul> <li>Function Objects</li><li>Lambda</li><li>Generators</li><li>Closures</li><li>Classes</li><li>Object Initialization</li><li>Class Static Members</li><li>Classic Inheritance</li><li>Data Hiding</li></ul> <p><strong>How will I get my Certificate?</strong></p> <p>After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p> <ul> <li><strong>PDF Certificate: Free (Previously it was £6*11 = £66)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        110 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <p>Anyone from any background can enrol in this bundle.</p> 
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                             <p>This course has been designed to be fully compatible with tablets and smartphones.</p> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <p>Having these various certifications will increase the value in your CV and open you up to multiple job sectors.</p> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>Data Science with Python</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p> <p>Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

Python Data Science Courses

<strong>If you are someone with a background in Python programming and is interested in presenting your analysis in interactive web-based dashboards, then you are in the right place. This course primarily focuses on Dash, along with other key data science libraries, including Pandas and Plotly. Learn to use Dash and Plotly in Python which can help you to visualize your critical insights and KPIs in web apps that are easily sharable.</strong>
        <p>Data visualization is a critical part of data science, in this quick and practical course you will learn how to create a great-looking interactive dashboard with Dash and Python. With this new skill, you will be able to present and share your analysis better with your customers' executives and the rest of your team.

This course is broken down into 5 parts and it is necessary to follow the structure as each part is built on top of the other. The first part is an introduction to Plotly Dash followed by building the layout of dashboards. The third part is how to make dashboards interactive with callbacks, fourth part introduces us to customize the look of dashboards. The last and final part is a business-centric project case study on creating an interactive map dashboard.

Throughout the course, you will be using 3 real-world datasets, which are the World Happiness dataset, FIFA Soccer dataset, and US electricity prices to help you learn Dash more.

By the end of the course, you will learn to build dashboards from scratch, by customizing their look and adding interactive features, with all free Python libraries.

All resources and code files are placed here: https://github.com/PacktPublishing/Python-Interactive-Dashboards-with-Plotly-Dash</p>
        <h2>What You Will Learn</h2><p>Learn to create a grid layout in dashboards<br>Create interactive graphs that are clickable<br>Create data tables in dashboards<br>Create a dashboard with a navigation bar and cards<br>Learn to add buttons to dashboards<br>Learn the basics of Python with this course</p>
        <h2>Audience</h2><p>This course can be taken by any Python programmer who wants to present their analyses in interactive web-based dashboards.</p>
        <h2>Approach</h2><p>This course includes instructional videos that walk you through the process step-by-step. You can learn at your own pace and download the Python scripts to use for your own projects.</p>
        <h2>Key Features</h2><p>Learn how to set up the layout of dashboards with Dash components * Make more dynamic dashboards with chained interactive features * Use Plotly to create plots including line charts, Choropleth maps</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Interactive-Dashboards-with-Plotly-Dash</p>
        <h2>About the Author</h2><h3>Just Into Data</h3><p>Just Into Data is the brainchild of Justin and Lianne. Justin is an experienced data scientist in many different fields, such as marketing, anti-money laundering, and big data technologies. He also has a bachelor's degree in computer engineering and a master's degree in statistics.

Lianne is an experienced statistician who has worked in the central bank as well as commercial banks, where she monitored major financial institutions and conducted fraud analysis. She has both a bachelor's and a master's degree in statistics.</p>
        <h2>Course Outline</h2><h4>1. Let's Get Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video introduces you to the course and the author.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Overview</p><p style='margin: 0'>This video introduces you to the course overview.</p></td></tr></tbody></table><br><h4>2. Python Crash Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting Up Python Environment</p><p style='margin: 0'>This video explains how to set up a Python environment.</p></td></tr><tr><td style='padding: 15px;'><p>2. Overview of Data Types, Numeric, Define Variables</p><p style='margin: 0'>This video explains an overview of data types, numeric, define variables.</p></td></tr><tr><td style='padding: 15px;'><p>3. Strings, Common Functions, and Methods</p><p style='margin: 0'>This video explains strings, common functions, and methods.</p></td></tr><tr><td style='padding: 15px;'><p>4. Lists, Tuples, Sets, Dictionaries, Booleans</p><p style='margin: 0'>This video explains lists, tuples, sets, dictionaries, Booleans.</p></td></tr><tr><td style='padding: 15px;'><p>5. If Statements, Loops</p><p style='margin: 0'>This video explains if statements, loops.</p></td></tr><tr><td style='padding: 15px;'><p>6. Define Functions, Use Packages</p><p style='margin: 0'>This video defines functions, use packages.</p></td></tr></tbody></table><br><h4>3. Introduction to Plotly Dash</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Dash</p><p style='margin: 0'>This video explains what Dash is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating Your First Dashboard</p><p style='margin: 0'>This video explains how to create your first dashboard.</p></td></tr></tbody></table><br><h4>4. Building the Layout of Dashboards</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. HTML Components Overview</p><p style='margin: 0'>This video explains HTML components overview.</p></td></tr><tr><td style='padding: 15px;'><p>2. Core Components Overview</p><p style='margin: 0'>This video explains the overview of core components.</p></td></tr></tbody></table><br><h4>5. Making Dashboards Interactive with Callbacks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Callbacks</p><p style='margin: 0'>This video introduces you to callbacks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building Callbacks with Single Input and Output</p><p style='margin: 0'>This video introduces you to building callbacks with single input and output.</p></td></tr><tr><td style='padding: 15px;'><p>3. Creating Interactive Plotly Figures</p><p style='margin: 0'>This video explains how to create interactive Plotly figures.</p></td></tr><tr><td style='padding: 15px;'><p>4. Building Callbacks with Multiple Inputs and Outputs</p><p style='margin: 0'>This video explains how to build callbacks with multiple inputs and outputs.</p></td></tr><tr><td style='padding: 15px;'><p>5. Chaining Callbacks' Inputs and Outputs</p><p style='margin: 0'>This video explains how to chain callbacks' inputs and outputs.</p></td></tr><tr><td style='padding: 15px;'><p>6. Controlling Callbacks with States (Button)</p><p style='margin: 0'>This video explains how to control callbacks with States (button).</p></td></tr></tbody></table><br><h4>6. Capstone Project I</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Avocado Prices Dashboard Overview</p><p style='margin: 0'>This video explains an overview of the Avocado prices dashboard.</p></td></tr></tbody></table><br><h4>7. Customizing the Look of Dashboards</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Customizing with Inline CSS</p><p style='margin: 0'>This video explains customizing with inline CSS.</p></td></tr><tr><td style='padding: 15px;'><p>2. Customizing with External CSS</p><p style='margin: 0'>This video explains customizing with external CSS.</p></td></tr><tr><td style='padding: 15px;'><p>3. Creating a Grid Layout</p><p style='margin: 0'>This video explains how to create a grid layout.</p></td></tr><tr><td style='padding: 15px;'><p>4. Building a Navigation Bar</p><p style='margin: 0'>This video explains how to build a navigation bar.</p></td></tr><tr><td style='padding: 15px;'><p>5. Building a Deck of Cards</p><p style='margin: 0'>This video explains how to build a deck of cards.</p></td></tr></tbody></table><br><h4>8. Case Study I: Creating an Interactive Map Dashboard</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting Up the Layout: RangeSlider</p><p style='margin: 0'>This video explains setting up the layout: RangeSlider.</p></td></tr><tr><td style='padding: 15px;'><p>2. Setting Up the Layout: Choropleth Map</p><p style='margin: 0'>This video explains Choropleth map.</p></td></tr><tr><td style='padding: 15px;'><p>3. Setting Up the Layout: DataTable</p><p style='margin: 0'>This video explains DataTable.</p></td></tr><tr><td style='padding: 15px;'><p>4. Adding Callbacks: Update the Map</p><p style='margin: 0'>This video explains how to update the map.</p></td></tr><tr><td style='padding: 15px;'><p>5. Adding Callbacks: Update the DataTable</p><p style='margin: 0'>This video explains how to update the DataTable.</p></td></tr></tbody></table><br><h4>9. Case Study II: Building a Live Updating Financial Dashboard</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting Up the Layout: Input and Button</p><p style='margin: 0'>This video explains setting up the layout: Input and Button.</p></td></tr><tr><td style='padding: 15px;'><p>2. Setting Up the Layout: Candlestick and DataTable</p><p style='margin: 0'>This video explains setting up the layout: Candlestick and DataTable.</p></td></tr><tr><td style='padding: 15px;'><p>3. Setting Up the Layout: Tabs</p><p style='margin: 0'>This video explains setting up the layout: Tabs.</p></td></tr><tr><td style='padding: 15px;'><p>4. Setting Up the Layout: Interval</p><p style='margin: 0'>This video explains setting up the layout: Interval.</p></td></tr><tr><td style='padding: 15px;'><p>5. Adding Callbacks: Live Update the Chart</p><p style='margin: 0'>This video explains adding callbacks: live update the chart.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adding Callbacks: Live Update the DataTable</p><p style='margin: 0'>This video explains how to update the DataTable.</p></td></tr></tbody></table><br><h4>10. Capstone Project II</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Life Expectancy Dashboard Overview</p><p style='margin: 0'>This video gives an overview of the life expectancy dashboard.</p></td></tr></tbody></table><br><h4>11. Bonus Materials</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Jupyter Dash</p><p style='margin: 0'>This video introduces you to the Jupyter dashboard.</p></td></tr></tbody></table>

Data Visualization

If you are someone with a background in Python programming and is interested in presenting your analysis in interactive web-based dashboards, then you are in the right place. This course primarily focuses on Dash, along with other key data science libraries, including Pandas and Plotly. Learn to use Dash and Plotly in Python which can help you to visualize your critical insights and KPIs in web apps that are easily sharable.

Python Interactive Dashboards with Plotly Dash

London

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"></section></div></div><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></p><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Machine Learning 2-day Course</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prerequisites:</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Basic knowledge of Python coding is a pre-requisite.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Who Should Attend?</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Practical:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project</li><li fr-original-style="" style="box-sizing: border-box;">We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.&nbsp;</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Outline:</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Supervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine</li><li fr-original-style="" style="box-sizing: border-box;">Regression Algorithms: Linear, Polynomial</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Unsupervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Clustering Algorithms: K-means clustering, Hierarchical Clustering</li><li fr-original-style="" style="box-sizing: border-box;">Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)</li><li fr-original-style="" style="box-sizing: border-box;">Association Machine Learning Algorithms: Apriori, Euclat</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Other machine learning Algorithms:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting</li><li fr-original-style="" style="box-sizing: border-box;">Reinforcement learning Algorithms: Q-Learning</li><li fr-original-style="" style="box-sizing: border-box;">Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Exploration and Preprocessing:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">The first part of a Machine Learning project understands the data and the problem at hand.</li><li fr-original-style="" style="box-sizing: border-box;">Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is included in this Python Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning Certificate on completion &nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning notes</li><li fr-original-style="" style="box-sizing: border-box;">Practical Python Machine Learning exercises and code examples</li><li fr-original-style="" style="box-sizing: border-box;">After the course, 1 free, online session for questions or revision Python Machine Learning.</li><li fr-original-style="" style="box-sizing: border-box;">Max group size on this Python Machine Learning is 4.</li></ul></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

Pcw Courses Ltd

Python Machine Learning Course, 1-Days, Online Attendance

Python Basics 1-days Online Instructor-Led

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p></p><p>Students in this course are interested in Azure development or in passing the Microsoft Azure Developer Associate certification exam.</p><p></p><p></p><p>This course teaches developers how to create end-to-end solutions in Microsoft Azure. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions.</p><p></p>Prerequisites<p>To be successful in this course, learners should have the following: Hands-on experience with Azure IaaS and PaaS solutions, and the Azure Portal. Experience writing in an Azure supported language at the intermediate level. (C#, JavaScript, Python, or Java) Ability to write code to connect and perform operations on, a SQL or NoSQL database product. (SQL Server, Oracle, MongoDB, Cassandra or similar) Experience writing code to handle authentication, authorization, and other security principles at the intermediate level. A general understanding of HTML, the HTTP protocol and REST API interfaces.</p></div><div id="course-pills-2"><h3>1 - Explore Azure App Service</h3> <ul> <li>Examine Azure App Service</li> <li>Examine Azure App Service plans</li> <li>Deploy to App Service</li> <li>Explore authentication and authorization in App Service</li> <li>Discover App Service networking features</li> </ul> <h3>2 - Configure web app settings</h3> <ul> <li>Configure application settings</li> <li>Configure general settings</li> <li>Configure path mappings</li> <li>Enable diagnostic logging</li> <li>Configure security certificates</li> </ul> <h3>3 - Scale apps in Azure App Service</h3> <ul> <li>Examine autoscale factors</li> <li>Identify autoscale factors</li> <li>Enable autoscale in App Service</li> <li>Explore autoscale best practices</li> </ul> <h3>4 - Explore Azure App Service deployment slots</h3> <ul> <li>Explore staging environments</li> <li>Examine slot swapping</li> <li>Swap deployment slots</li> <li>Route traffic in App Service</li> </ul> <h3>5 - Explore Azure Functions</h3> <ul> <li>Discover Azure Functions</li> <li>Compare Azure Functions hosting options</li> <li>Scale Azure Functions</li> </ul> <h3>6 - Develop Azure Functions</h3> <ul> <li>Explore Azure Functions development</li> <li>Create triggers and bindings</li> <li>Connect functions to Azure services</li> </ul> <h3>7 - Explore Azure Blob storage</h3> <ul> <li>Explore Azure Blob storage</li> <li>Discover Azure Blob storage resource types</li> <li>Explore Azure Storage security features</li> <li>Discover static website hosting in Azure Storage</li> </ul> <h3>8 - Manage the Azure Blob storage lifecycle</h3> <ul> <li>Explore the Azure Blob storage lifecycle</li> <li>Discover Blob storage lifecycle policies</li> <li>Implement Blob storage lifecycle policies</li> <li>Rehydrate blob data from the archive tier</li> </ul> <h3>9 - Work with Azure Blob storage</h3> <ul> <li>Explore Azure Blob storage client library</li> <li>Create a client object</li> <li>Manage container properties and metadata by using .NET</li> <li>Set and retrieve properties and metadata for blob resources by using REST</li> </ul> <h3>10 - Explore Azure Cosmos DB</h3> <ul> <li>Identify key benefits of Azure Cosmos DB</li> <li>Explore the resource hierarchy</li> <li>Explore consistency levels</li> <li>Choose the right consistency level</li> <li>Explore supported APIs</li> <li>Discover request units</li> </ul> <h3>11 - Work with Azure Cosmos DB</h3> <ul> <li>Explore Microsoft .NET SDK v3 for Azure Cosmos DB</li> <li>Create stored procedures</li> <li>Create triggers and user-defined functions</li> <li>Explore change feed in Azure Cosmos DB</li> </ul> <h3>12 - Manage container images in Azure Container Registry</h3> <ul> <li>Discover the Azure Container Registry</li> <li>Explore storage capabilities</li> <li>Build and manage containers with tasks</li> <li>Explore elements of a Dockerfile</li> </ul> <h3>13 - Run container images in Azure Container Instances</h3> <ul> <li>Explore Azure Container Instances</li> <li>Run containerized tasks with restart policies</li> <li>Set environment variables in container instances</li> <li>Mount an Azure file share in Azure Container Instances</li> </ul> <h3>14 - Implement Azure Container Apps</h3> <ul> <li>Explore Azure Container Apps</li> <li>Explore containers in Azure Container Apps</li> <li>Implement authentication and authorization in Azure Container Apps</li> <li>Manage revisions and secrets in Azure Container Apps</li> <li>Explore Dapr integration with Azure Container Apps</li> </ul> <h3>15 - Explore the Microsoft identity platform</h3> <ul> <li>Explore the Microsoft identity platform</li> <li>Explore service principals</li> <li>Discover permissions and consent</li> <li>Discover conditional access</li> </ul> <h3>16 - Implement authentication by using the Microsoft Authentication Library</h3> <ul> <li>Explore the Microsoft Authentication Library</li> <li>Initialize client applications</li> </ul> <h3>17 - Implement shared access signatures</h3> <ul> <li>Discover shared access signatures</li> <li>Choose when to use shared access signatures</li> <li>Explore stored access policies</li> </ul> <h3>18 - Explore Microsoft Graph</h3> <ul> <li>Discover Microsoft Graph</li> <li>Query Microsoft Graph by using REST</li> <li>Query Microsoft Graph by using SDKs</li> <li>Apply best practices to Microsoft Graph</li> </ul> <h3>19 - Implement Azure Key Vault</h3> <ul> <li>Explore Azure Key Vault</li> <li>Discover Azure Key Vault best practices</li> <li>Authenticate to Azure Key Vault</li> </ul> <h3>20 - Implement managed identities</h3> <ul> <li>Explore managed identities</li> <li>Discover the managed identities authentication flow</li> <li>Configure managed identities</li> <li>Acquire an access token</li> </ul> <h3>21 - Implement Azure App Configuration</h3> <ul> <li>Explore the Azure App Configuration service</li> <li>Create paired keys and values</li> <li>Manage application features</li> <li>Secure app configuration data</li> </ul> <h3>22 - Explore API Management</h3> <ul> <li>Discover the API Management service</li> <li>Explore API gateways</li> <li>Explore API Management policies</li> <li>Create advanced policies</li> <li>Secure APIs by using subscriptions</li> <li>Secure APIs by using certificates</li> </ul> <h3>23 - Explore Azure Event Grid</h3> <ul> <li>Explore Azure Event Grid</li> <li>Discover event schemas</li> <li>Explore event delivery durability</li> <li>Control access to events</li> <li>Receive events by using webhooks</li> <li>Filter events</li> </ul> <h3>24 - Explore Azure Event Hubs</h3> <ul> <li>Discover Azure Event Hubs</li> <li>Explore Event Hubs Capture</li> <li>Scale your processing application</li> <li>Control access to events</li> <li>Perform common operations with the Event Hubs client library</li> </ul> <h3>25 - Discover Azure message queues</h3> <ul> <li>Choose a message queue solution</li> <li>Explore Azure Service Bus</li> <li>Discover Service Bus queues, topics, and subscriptions</li> <li>Explore Service Bus message payloads and serialization</li> <li>Explore Azure Queue Storage</li> <li>Create and manage Azure Queue Storage and messages by using .NET</li> </ul> <h3>26 - Monitor app performance</h3> <ul> <li>Explore Application Insights</li> <li>Discover log-based metrics</li> <li>Instrument an app for monitoring</li> <li>Select an availability test</li> <li>Troubleshoot app performance by using Application Map</li> </ul> <h3>27 - Develop for Azure Cache for Redis</h3> <ul> <li>Explore Azure Cache for Redis</li> <li>Configure Azure Cache for Redis</li> <li>Interact with Azure Cache for Redis by using .NET</li> </ul> <h3>28 - Develop for storage on CDNs</h3> <ul> <li>Explore Azure Content Delivery Networks</li> <li>Control cache behavior on Azure Content Delivery Networks</li> <li>Interact with Azure Content Delivery Networks by using .NET</li> </ul></div>

Nexus Human

Microsoft AZ-900

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

AWS Certified Solutions Architect

AZ-204T00 Developing Solutions for Microsoft Azure

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.</p><p></p><p></p><p>AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language.</p><p></p>Prerequisites<p>Before attending this course, students must have:  </p><li>Knowledge of Microsoft Azure and ability to navigate the Azure portal  </li><li>Knowledge of either C# or Python  </li><li>Familiarity with JSON and REST programming semantics  <br />  Recommended course prerequisites  </li><li>AI-900T00: Microsoft Azure AI Fundamentals course<p></p></li></div><div id="course-pills-2"><h3>1 - Prepare to develop AI solutions on Azure</h3> <ul> <li>Define artificial intelligence</li> <li>Understand AI-related terms</li> <li>Understand considerations for AI Engineers</li> <li>Understand considerations for responsible AI</li> <li>Understand capabilities of Azure Machine Learning</li> <li>Understand capabilities of Azure AI Services</li> <li>Understand capabilities of the Azure Bot Service</li> <li>Understand capabilities of Azure Cognitive Search</li> </ul> <h3>2 - Create and consume Azure AI services</h3> <ul> <li>Provision an Azure AI services resource</li> <li>Identify endpoints and keys</li> <li>Use a REST API</li> <li>Use an SDK</li> </ul> <h3>3 - Secure Azure AI services</h3> <ul> <li>Consider authentication</li> <li>Implement network security</li> </ul> <h3>4 - Monitor Azure AI services</h3> <ul> <li>Monitor cost</li> <li>Create alerts</li> <li>View metrics</li> <li>Manage diagnostic logging</li> </ul> <h3>5 - Deploy Azure AI services in containers</h3> <ul> <li>Understand containers</li> <li>Use Azure AI services containers</li> </ul> <h3>6 - Analyze images</h3> <ul> <li>Provision an Azure AI Vision resource</li> <li>Analyze an image</li> <li>Generate a smart-cropped thumbnail</li> </ul> <h3>7 - Classify images</h3> <ul> <li>Provision Azure resources for Azure AI Custom Vision</li> <li>Understand image classification</li> <li>Train an image classifier</li> </ul> <h3>8 - Detect, analyze, and recognize faces</h3> <ul> <li>Identify options for face detection analysis and identification</li> <li>Understand considerations for face analysis</li> <li>Detect faces with the Azure AI Vision service</li> <li>Understand capabilities of the face service</li> <li>Compare and match detected faces</li> <li>Implement facial recognition</li> </ul> <h3>9 - Read Text in images and documents with the Azure AI Vision Service</h3> <ul> <li>Explore Azure AI Vision options for reading text</li> <li>Use the Read API</li> </ul> <h3>10 - Analyze video</h3> <ul> <li>Understand Azure Video Indexer capabilities</li> <li>Extract custom insights</li> <li>Use Video Analyzer widgets and APIs</li> </ul> <h3>11 - Analyze text with Azure AI Language</h3> <ul> <li>Provision an Azure AI Language resource</li> <li>Detect language</li> <li>Extract key phrases</li> <li>Analyze sentiment</li> <li>Extract entities</li> <li>Extract linked entities</li> </ul> <h3>12 - Build a question answering solution</h3> <ul> <li>Understand question answering</li> <li>Compare question answering to Azure AI Language understanding</li> <li>Create a knowledge base</li> <li>Implement multi-turn conversation</li> <li>Test and publish a knowledge base</li> <li>Use a knowledge base</li> <li>Improve question answering performance</li> </ul> <h3>13 - Build a conversational language understanding model</h3> <ul> <li>Understand prebuilt capabilities of the Azure AI Language service</li> <li>Understand resources for building a conversational language understanding model</li> <li>Define intents, utterances, and entities</li> <li>Use patterns to differentiate similar utterances</li> <li>Use pre-built entity components</li> <li>Train, test, publish, and review a conversational language understanding model</li> </ul> <h3>14 - Create a custom text classification solution</h3> <ul> <li>Understand types of classification projects</li> <li>Understand how to build text classification projects</li> </ul> <h3>15 - Create a custom named entity extraction solution</h3> <ul> <li>Understand custom named entity recognition</li> <li>Label your data</li> <li>Train and evaluate your model</li> </ul> <h3>16 - Translate text with Azure AI Translator service</h3> <ul> <li>Provision an Azure AI Translator resource</li> <li>Specify translation options</li> <li>Define custom translations</li> </ul> <h3>17 - Create speech-enabled apps with Azure AI services</h3> <ul> <li>Provision an Azure resource for speech</li> <li>Use the Azure AI Speech to Text API</li> <li>Use the text to speech API</li> <li>Configure audio format and voices</li> <li>Use Speech Synthesis Markup Language</li> </ul> <h3>18 - Translate speech with the Azure AI Speech service</h3> <ul> <li>Provision an Azure resource for speech translation</li> <li>Translate speech to text</li> <li>Synthesize translations</li> </ul> <h3>19 - Create an Azure AI Search solution</h3> <ul> <li>Manage capacity</li> <li>Understand search components</li> <li>Understand the indexing process</li> <li>Search an index</li> <li>Apply filtering and sorting</li> <li>Enhance the index</li> </ul> <h3>20 - Create a custom skill for Azure AI Search</h3> <ul> <li>Create a custom skill</li> <li>Add a custom skill to a skillset</li> </ul> <h3>21 - Create a knowledge store with Azure AI Search</h3> <ul> <li>Define projections</li> <li>Define a knowledge store</li> </ul> <h3>22 - Plan an Azure AI Document Intelligence solution</h3> <ul> <li>Understand AI Document Intelligence</li> <li>Plan Azure AI Document Intelligence resources</li> <li>Choose a model type</li> </ul> <h3>23 - Use prebuilt Azure AI Document Intelligence models</h3> <ul> <li>Understand prebuilt models</li> <li>Use the General Document, Read, and Layout models</li> <li>Use financial, ID, and tax models</li> </ul> <h3>24 - Extract data from forms with Azure Document Intelligence</h3> <ul> <li>What is Azure Document Intelligence?</li> <li>Get started with Azure Document Intelligence</li> <li>Train custom models</li> <li>Use Azure Document Intelligence models</li> <li>Use the Azure Document Intelligence Studio</li> </ul> <h3>25 - Get started with Azure OpenAI Service</h3> <ul> <li>Access Azure OpenAI Service</li> <li>Use Azure OpenAI Studio</li> <li>Explore types of generative AI models</li> <li>Deploy generative AI models</li> <li>Use prompts to get completions from models</li> <li>Test models in Azure OpenAI Studio's playgrounds</li> </ul> <h3>26 - Build natural language solutions with Azure OpenAI Service</h3> <ul> <li>Integrate Azure OpenAI into your app</li> <li>Use Azure OpenAI REST API</li> <li>Use Azure OpenAI SDK</li> </ul> <h3>27 - Apply prompt engineering with Azure OpenAI Service</h3> <ul> <li>Understand prompt engineering</li> <li>Write more effective prompts</li> <li>Provide context to improve accuracy</li> </ul> <h3>28 - Generate code with Azure OpenAI Service</h3> <ul> <li>Construct code from natural language</li> <li>Complete code and assist the development process</li> <li>Fix bugs and improve your code</li> </ul> <h3>29 - Generate images with Azure OpenAI Service</h3> <ul> <li>What is DALL-E?</li> <li>Explore DALL-E in Azure OpenAI Studio</li> <li>Use the Azure OpenAI REST API to consume DALL-E models</li> </ul> <h3>30 - Use your own data with Azure OpenAI Service</h3> <ul> <li>Understand how to use your own data</li> <li>Add your own data source</li> <li>Chat with your model using your own data</li> </ul> <h3>31 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div>

Azure Machine Learning

Microsoft AI-900

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

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 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
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 Requirements 
No experience required. Just enrol & start learning python.
 Career path 
Upon completion of our Program, the potential career paths include:
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 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Academy for Health and Fitness

Programming Fundamentals

Career Development

IT Certifications

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<strong>Learn Python Programming the Easy Way, Complete with Examples, Quizzes, Exercises and more. Learn Python 2 and Python 3.</strong>
        <p>This course will teach you Python in a practical manner, complete with examples, quizzes, exercises, and more. You will learn when to use Python 2 and Python 3; both versions are covered in the course and you will learn to program in both. You'll learn how to take tedious and repetitious tasks and turn them into programs that will save you time and simplify your life on Linux, UNIX, or MAC systems. You will learn how to prepare your computer for programming in Python; how to work with various data types including strings, lists, tuples, dictionaries, Booleans; how to perform mathematical operations using Python, and more.</p>
        <h2>What You Will Learn</h2><p>When to use Python 2 and when to use Python 3.<br>How to install Python on Windows, Mac, and Linux.<br>How to prepare your computer for programming in Python.<br>The various ways to run a Python program on Windows, Mac, and Linux.<br>Suggested text editors and Integrated Development Environments to use when coding in Python.<br>How to work with various data types including strings, lists, tuples, dictionaries, Booleans, and more.<br>What variables are and when to use them.<br>How to perform mathematical operations using Python.<br>How to capture input from a user.<br>Ways to control the flow of your programs.<br>The importance of white space in Python.<br>How to organize your Python programs: learn what goes where.<br>What modules are, when you should use them, and how to create your own.<br>How to define and use functions.<br>Important built-in Python functions that you ll use often.<br>How to read from and write to files.<br>The difference between binary and text files.<br>Various ways of getting help, including finding Python documentation.<br></p>
        <h2>Audience</h2><p>This course targets anyone interested in Python programming, Python scripting, or computer programming in general; those who want to become a highly paid Python developer; and those who want to open up doors in their IT career by learning one of the world's most popular and in-demand programming languages.</p>
        <h2>Approach</h2><p>A complete course packed with step-by-step instructions, practice exercises with solutions, and quizzes. This course is clearly divided into small parts to help you understand each part individually and learn at your own pace. Once you've completed this course you'll know how to write programs that will run on the Linux, Mac, and Windows operating systems. You can even take what you've learned and apply it to web applications. </p>
        <h2>Key Features</h2><p>How to install Python on Windows, Mac, and Linux. * A practical tutorial designed for Python beginners. * </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-for-beginners-learn-python-programming-python-3-</p>
        <h2>About the Author</h2><h3>Jason Cannon</h3><p>Jason Cannon is a professional system administrator, consultant, and author. He started his career as a Unix and Linux System engineer in 1999. Since that time, he has utilized his Linux skills at companies such as Xerox, UPS, Hewlett-Packard, and Amazon.com. Additionally, he has acted as a technical consultant and independent contractor for small businesses and Fortune 500 companies.

Jason has professional experience with CentOS, RedHat Enterprise Linux, SUSE Linux Enterprise Server, and Ubuntu. He has used several Linux distributions on personal projects including Debian, Slackware, CrunchBang, and others. In addition to Linux, Jason has experience supporting proprietary Unix operating systems including AIX, HP-UX, and Solaris. He enjoys teaching others how to use and exploit the power of the Linux operating system.</p>
        <h2>Course Outline</h2><h4>1. Course Overview and Downloads</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Overview</p><p style='margin: 0'>Course Overview and Downloads: Course Overview</p></td></tr></tbody></table><br><h4>2. Python Setup</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Python 2 vs Python 3</p><p style='margin: 0'>Python Setup: Python 2 vs Python 3</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing Python on Windows</p><p style='margin: 0'>Python Setup: Installing Python on Windows</p></td></tr><tr><td style='padding: 15px;'><p>3. Installing Python on Mac</p><p style='margin: 0'>Python Setup: Installing Python on Mac</p></td></tr><tr><td style='padding: 15px;'><p>4. Installing Python on Linux</p><p style='margin: 0'>Python Setup: Installing Python on Linux</p></td></tr><tr><td style='padding: 15px;'><p>5. Writing Python Programs</p><p style='margin: 0'>Python Setup: Writing Python Programs</p></td></tr><tr><td style='padding: 15px;'><p>6. Running Python Programs</p><p style='margin: 0'>Python Setup: Running Python Programs</p></td></tr></tbody></table><br><h4>3. Strings and Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Strings and Variables: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Variables and Strings</p><p style='margin: 0'>Strings and Variables: Variables and Strings</p></td></tr><tr><td style='padding: 15px;'><p>3. Functions and Printing</p><p style='margin: 0'>Strings and Variables: Functions and Printing</p></td></tr><tr><td style='padding: 15px;'><p>4. String Methods</p><p style='margin: 0'>Strings and Variables: String Methods</p></td></tr><tr><td style='padding: 15px;'><p>5. String Concatenation</p><p style='margin: 0'>Strings and Variables: String Concatenation</p></td></tr><tr><td style='padding: 15px;'><p>6. Formatting Strings</p><p style='margin: 0'>Strings and Variables: Formatting Strings</p></td></tr><tr><td style='padding: 15px;'><p>7. Section Summary</p><p style='margin: 0'>Strings and Variables: Section Summary</p></td></tr><tr><td style='padding: 15px;'><p>8. Solutions to the Practice Exercises - #1 - Video</p><p style='margin: 0'>Strings and Variables: Solutions to the Practice Exercises - #1 - Video</p></td></tr><tr><td style='padding: 15px;'><p>9. Solutions to the Practice Exercises - #2 - Video</p><p style='margin: 0'>Strings and Variables: Solutions to the Practice Exercises - #2 - Video</p></td></tr><tr><td style='padding: 15px;'><p>10. Solutions to the Practice Exercises - #3 - Video</p><p style='margin: 0'>Strings and Variables: Solutions to the Practice Exercises - #3 - Video</p></td></tr></tbody></table><br><h4>4. Numbers and Math</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Numbers and Math: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Numbers, Numeric Operations, and Numeric Functions</p><p style='margin: 0'>Numbers and Math: Numbers, Numeric Operations, and Numeric Functions</p></td></tr><tr><td style='padding: 15px;'><p>3. Comments</p><p style='margin: 0'>Numbers and Math: Comments</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Numbers and Math: Section Summary</p></td></tr></tbody></table><br><h4>5. Booleans and Conditionals</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Booleans and Conditionals: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Booleans</p><p style='margin: 0'>Booleans and Conditionals: Booleans</p></td></tr><tr><td style='padding: 15px;'><p>3. Conditionals</p><p style='margin: 0'>Booleans and Conditionals: Conditionals</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Booleans and Conditionals: Section Summary</p></td></tr></tbody></table><br><h4>6. Functions</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Functions: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Functions, Part I</p><p style='margin: 0'>Functions: Functions, Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Functions, Part II</p><p style='margin: 0'>Functions: Functions, Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Functions: Section Summary</p></td></tr></tbody></table><br><h4>7. Lists</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Lists: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Lists</p><p style='margin: 0'>Lists: Lists</p></td></tr><tr><td style='padding: 15px;'><p>3. Slices</p><p style='margin: 0'>Lists: Slices</p></td></tr><tr><td style='padding: 15px;'><p>4. Exception Handling</p><p style='margin: 0'>Lists: Exception Handling</p></td></tr><tr><td style='padding: 15px;'><p>5. Loops</p><p style='margin: 0'>Lists: Loops</p></td></tr><tr><td style='padding: 15px;'><p>6. Sorting and Ranges</p><p style='margin: 0'>Lists: Sorting and Ranges</p></td></tr><tr><td style='padding: 15px;'><p>7. Section Summary</p><p style='margin: 0'>Lists: Section Summary</p></td></tr></tbody></table><br><h4>8. Dictionaries</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Dictionaries: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Dictionaries - Part I</p><p style='margin: 0'>Dictionaries: Dictionaries - Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Dictionaries - Part II</p><p style='margin: 0'>Dictionaries: Dictionaries - Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Dictionaries: Section Summary</p></td></tr></tbody></table><br><h4>9. Tuples</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Tuples: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Tuples</p><p style='margin: 0'>Tuples: Tuples</p></td></tr><tr><td style='padding: 15px;'><p>3. Section Summary</p><p style='margin: 0'>Tuples: Section Summary</p></td></tr></tbody></table><br><h4>10. Files</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Files: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Files, Part I</p><p style='margin: 0'>Files: Files, Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Files, Part II</p><p style='margin: 0'>Files: Files, Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Files: Section Summary</p></td></tr></tbody></table><br><h4>11. Modules</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Modules: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Modules, Part I</p><p style='margin: 0'>Modules: Modules, Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Modules, Part II</p><p style='margin: 0'>Modules: Modules, Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Overview</p><p style='margin: 0'>Modules: Section Overview</p></td></tr></tbody></table>

Learn Python Programming the Easy Way, Complete with Examples, Quizzes, Exercises and more. Learn Python 2 and Python 3.

Python for Beginners: Learn Python Programming (Python 3)

<p><strong>Start your career as a Data Analyst and earn upto £35,000/ Month </strong></p><p>Have you ever imagined commanding a six-figure salary with the power of Python? Welcome to the "Data Analyst with Python - Job Ready Program with Career Support", where the world of big data is at your fingertips, and the only limit is your own curiosity. Explore the revolutionary journey that is geared towards turning raw data into insightful decisions. Our comprehensive program paves the way to becoming a sought-after Data Analyst, unlocking the door to a thriving career field as Python continues to spearhead the data revolution.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Data Analysts with Python expertise, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Data Analyst with Python - Job Ready Program today and start your journey to becoming a successful Data Analyst!</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Analyst with Python Job Ready Program:</p><ul><li><strong>Personalised Guidance:</strong> Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some experience, we will guide you towards honing your skills and developing a strong foundation in Data Analysis with Python.<br /><br /></li><li><strong>One-On-One Consultation Sessions with Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Analyst field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking IT Helpdesk Support. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere. <br /><br /></li><li><strong>Interview Preparation:</strong> We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.<br /><br /></li><li><strong>Money-Back Guarantee:</strong> Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee.<br /><br /></li><li><strong>Continuous Career Support: </strong>Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance. .</li></ul><p>If you have any questions, you can <strong>contact us</strong>. We will be happy to provide you with all the information you need.</p><p><strong>Here are the courses we will provide once you enrol in the program:</strong></p><ul><li>Course 01: Business and Data Analytics for Beginners</li><li>Course 02: Quick Data Science Approach from Scratch</li><li>Course 03: Learn MySQL from Scratch for Data Science and Analytics</li><li>Course 04: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 06: R Programming for Data Science</li><li>Course 07: Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 08: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>Course 09: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li>Course 10: Python from Scratch</li><li>Course 11: Level 2 Python Course</li><li>Course 12: Machine Learning for Predictive Maps in Python and Leaflet</li><li>Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li>Course 14: Python for Spatial Analysis in ArcGIS</li><li>Course 15: Ultimate Python Training for Beginners</li><li>Course 16: Python Level 1 Crash Course</li><li>Course 17: Python Programming from Scratch with My SQL Database</li></ul><p>These courses will help you to develop your knowledge and skills to become a <strong>successful Data Analyst</strong>.</p><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The Data Analyst with Python Program is completed in 9 easy steps:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Data Analyst with Python program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to 17 premium courses related to Data Analysis with Python. These courses will teach you the knowledge and skills required to become a successful Data Analyst with Python Programmer. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.</p><p><strong>Step 3: </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Finance industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.</p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.</p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.</p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Data Analysis with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Analysis with Python begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This course is tailored to suit a broad spectrum of learners, including:</p><ul><li>Aspiring Data Analysts who want to launch a new career in data analytics.</li><li>Current analysts or professionals from similar fields looking to hone their Python skills and modernise their toolset.</li><li>Business and IT students or recent graduates desiring a competitive edge in the job market.</li><li>Career shifters interested in a more dynamic and high-paying field.</li><li>Any individual who loves working with numbers, solving problems, and has a curiosity for uncovering insights in data.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li><strong>Data Analyst</strong>: £27,000 - £60,000</li><li><strong>Senior Data Analyst</strong>: £40,000 - £75,000</li><li><strong>Data Scientist</strong>: £50,000 - £100,000</li><li><strong>Data Engineer</strong>: £45,000 - £90,000</li><li><strong>Analytics Manager</strong>: £60,000 - £120,000</li><li><strong>Director of Data Science</strong>: £90,000 - £150,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate </h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Start your career as a Data Analyst and earn upto £35,000/ Month 
Have you ever imagined commanding a six-figure salary with the power of Python? Welcome to the "Data Analyst with Python - Job Ready Program with Career Support", where the world of big data is at your fingertips, and the only limit is your own curiosity. Explore the revolutionary journey that is geared towards turning raw data into insightful decisions. Our comprehensive program paves the way to becoming a sought-after Data Analyst, unlocking the door to a thriving career field as Python continues to spearhead the data revolution.
Our mission is simple - to be your trusted partner every step of the way, from training to employment.
In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Data Analysts with Python expertise, so we can help you get your foot in the door.
If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our Data Analyst with Python - Job Ready Program today and start your journey to becoming a successful Data Analyst!
Why Choose Us?
So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Analyst with Python Job Ready Program:
Personalised Guidance: Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some experience, we will guide you towards honing your skills and developing a strong foundation in Data Analysis with Python.
One-On-One Consultation Sessions with Experts: Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Analyst field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.
Extensive Job Opportunities: We have established partnerships with numerous companies actively seeking IT Helpdesk Support. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.
Interview Preparation: We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.
Money-Back Guarantee: Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee.
Continuous Career Support: Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance. .
If you have any questions, you can contact us. We will be happy to provide you with all the information you need.
Here are the courses we will provide once you enrol in the program:
Course 01: Business and Data Analytics for Beginners
Course 02: Quick Data Science Approach from Scratch
Course 03: Learn MySQL from Scratch for Data Science and Analytics
Course 04: SQL for Data Science, Data Analytics and Data Visualization
Course 05: Statistics & Probability for Data Science & Machine Learning
Course 06: R Programming for Data Science
Course 07: Python Data Science with Numpy, Pandas and Matplotlib
Course 08: Complete Python Machine Learning & Data Science Fundamentals
Course 09: 2021 Data Science & Machine Learning with R from A-Z
Course 10: Python from Scratch
Course 11: Level 2 Python Course
Course 12: Machine Learning for Predictive Maps in Python and Leaflet
Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI
Course 14: Python for Spatial Analysis in ArcGIS
Course 15: Ultimate Python Training for Beginners
Course 16: Python Level 1 Crash Course
Course 17: Python Programming from Scratch with My SQL Database
These courses will help you to develop your knowledge and skills to become a successful Data Analyst.
How Can We Offer Job Guarantees?
HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.
The Data Analyst with Python Program is completed in 9 easy steps:
Step 1: Enrol in the Programme
Begin your exciting journey with us by enrolling in the Data Analyst with Python program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.
After you enrol in the Program, you will get lifetime access to 17 premium courses related to Data Analysis with Python. These courses will teach you the knowledge and skills required to become a successful Data Analyst with Python Programmer. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!
Step 2: Initial One-On-One Counselling Session
Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.
Step 3: Certification upon Course Completion
After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.
Step 4: CV Revamping
Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Finance industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.
Step 5: Building Network and Submitting CV
We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.
Step 6: Interview Preparation
With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.
Step 7: Securing Job Interviews
Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.
Step 8: Post-Interview Support
Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.
Step 9: Celebrate Your New Job!
Once you've secured your dream job in Data Analysis with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.
We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Analysis with Python begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This course is tailored to suit a broad spectrum of learners, including:
Aspiring Data Analysts who want to launch a new career in data analytics.
Current analysts or professionals from similar fields looking to hone their Python skills and modernise their toolset.
Business and IT students or recent graduates desiring a competitive edge in the job market.
Career shifters interested in a more dynamic and high-paying field.
Any individual who loves working with numbers, solving problems, and has a curiosity for uncovering insights in data.
 Requirements 
No experience required. Just enrol & start learning.
 Career path 
Data Analyst: £27,000 - £60,000
Senior Data Analyst: £40,000 - £75,000
Data Scientist: £50,000 - £100,000
Data Engineer: £45,000 - £90,000
Analytics Manager: £60,000 - £120,000
Director of Data Science: £90,000 - £150,000
 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Business Analytics & Intelligence

Data Engineering

Big Data Analytics

Data Science

Data Skills

Data Analyst

Data Analyst with Python Job Ready Program with Career Support & Money Back Guarantee

<p><strong>Start your career in Data Science and earn up to £90,000 per month.</strong></p><p>Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Data <strong>Scientists</strong>, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!</p><p>If you have any questions, you can <strong>contact us</strong>. We will be happy to provide you with all the information you need.</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.<br /><br /></li><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.<br /><br /></li><li><strong>Money-Back Guarantee:</strong> Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a <strong>100% money-back guarantee. </strong>If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.<br /><br /></li><li><strong>Continuous Career Support: </strong>Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance.</li></ul><p><strong>Here are the courses we will provide once you enrol in the program:</strong></p><ul><li>Course 01: Business and Data Analytics for Beginners</li><li>Course 02: Quick Data Science Approach from Scratch</li><li>Course 03: Learn MySQL from Scratch for Data Science and Analytics</li><li>Course 04: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 06: R Programming for Data Science</li><li>Course 07: Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 08: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>Course 09: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li>Course 10: Python Programming from Scratch with My SQL Database</li><li>Course 11: Level 2 Python Course</li><li>Course 12: Machine Learning for Predictive Maps in Python and Leaflet</li><li>Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li>Course 14: Python for Spatial Analysis in ArcGIS</li><li>Course 15: Ultimate Python Training for Beginners</li><li>Course 16: PyScript Fundamentals Training</li></ul><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The </strong><strong>Data Scientist with Python </strong><strong>Program is completed in 9 easy steps:<br /><br /></strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.<br /><br /></p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.<br /><br /></p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. <br /><br /></p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.<br /><br /></p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.<br /><br /></p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.<br /><br /></p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.<br /><br /></p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Beginners with an interest in Data Science and Python programming.</li><li>Programmers and Software Engineers looking to transition into Data Science.</li><li>Business Analysts and Project Managers aiming to leverage Data Science in their domains.</li><li>Researchers and Academics who wish to utilise Python for complex data analysis.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £60,000 - £90,000</li><li>Machine Learning Engineer: £65,000 - £100,000</li><li>Data Analyst: £40,000 - £60,000</li><li>Business Intelligence Analyst: £45,000 - £70,000</li><li>Data Engineer: £50,000 - £80,000</li><li>Big Data Architect: £70,000 - £120,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Start your career in Data Science and earn up to £90,000 per month.
Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.
Our mission is simple - to be your trusted partner every step of the way, from training to employment.
In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Data Scientists, so we can help you get your foot in the door.
If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!
If you have any questions, you can contact us. We will be happy to provide you with all the information you need.
Why Choose Us?
So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:
Personalised Guidance: We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.
One-On-One Consultation Sessions with Industry Experts: Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.
Extensive Job Opportunities: We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.
Interview Preparation: No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.
Money-Back Guarantee: Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee. If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.
Continuous Career Support: Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance.
Here are the courses we will provide once you enrol in the program:
Course 01: Business and Data Analytics for Beginners
Course 02: Quick Data Science Approach from Scratch
Course 03: Learn MySQL from Scratch for Data Science and Analytics
Course 04: SQL for Data Science, Data Analytics and Data Visualization
Course 05: Statistics & Probability for Data Science & Machine Learning
Course 06: R Programming for Data Science
Course 07: Python Data Science with Numpy, Pandas and Matplotlib
Course 08: Complete Python Machine Learning & Data Science Fundamentals
Course 09: 2021 Data Science & Machine Learning with R from A-Z
Course 10: Python Programming from Scratch with My SQL Database
Course 11: Level 2 Python Course
Course 12: Machine Learning for Predictive Maps in Python and Leaflet
Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI
Course 14: Python for Spatial Analysis in ArcGIS
Course 15: Ultimate Python Training for Beginners
Course 16: PyScript Fundamentals Training
How Can We Offer Job Guarantees?
HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.
The Data Scientist with Python Program is completed in 9 easy steps:


Step 1: Enrol in the Programme
Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.
After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!
Step 2: Initial One-On-One Counselling Session
Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.


Step 3 - Certification upon Course Completion
After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.


Step 4: CV Revamping
Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. 


Step 5: Building Network and Submitting CV
We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.


Step 6: Interview Preparation
With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.


Step 7: Securing Job Interviews
Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.


Step 8: Post-Interview Support
Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.


Step 9: Celebrate Your New Job!
Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.
We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Beginners with an interest in Data Science and Python programming.
Programmers and Software Engineers looking to transition into Data Science.
Business Analysts and Project Managers aiming to leverage Data Science in their domains.
Researchers and Academics who wish to utilise Python for complex data analysis.
 Requirements 
No experience required. Just enrol & start learning.
 Career path 
Data Scientist: £60,000 - £90,000
Machine Learning Engineer: £65,000 - £100,000
Data Analyst: £40,000 - £60,000
Business Intelligence Analyst: £45,000 - £70,000
Data Engineer: £50,000 - £80,000
Big Data Architect: £70,000 - £120,000
 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Data Science with Python - IT Job Ready Program with Career Support & Money Back Guarantee

<strong>You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.</strong>
        <p>'Python is the future of artificial intelligence and machine learning.'-Adrian Rosebrock, author.

Python is the programming language most loved by developers, data scientists, software engineers, and even hackers.

This Python basic to advanced course covers the fundamental concepts of Python programming and ways to apply this to real-world applications. The course modules, lesson-end quizzes, and lesson-end projects collectively cover the training course topics such as data operations, strings, file I/O, exception handling, error handling, conditional statements, CGI programming, shell scripting, web scraping, and Django.

It also covers Logging Framework, Unittest Framework, Socket Programming, Tornado Framework, and Networking. You will also build a Python chat server.

By the end of this course, you will have the skills to create and send robust Python applications using off-the-rack libraries confidently.

The code bundle for this course is available at: https://github.com/PacktPublishing/Python-Programming</p>
        <h2>What You Will Learn</h2><p>Learn the basics of Python<br>Learn networking and database access using Python and CGI programming<br>Understand the Python debugger<br>Understand web scraping using Beautiful Soup<br>Learn about Python Unittest<br>Build a Python chat server</p>
        <h2>Audience</h2><p>This course is for new Python users looking to make the best of this powerful programming language-college freshers, software developers, programming enthusiasts, and technical leads. Python is widely used in artificial intelligence, so if you are planning to start your career in AI/machine learning then you need to have an understanding of Python as a prerequisite.</p>
        <h2>Approach</h2><p>This course comprises self-paced videos, demos, lesson-end quizzes, and a competitive course-end project. This will help you increase your in-depth understanding of Python's design, memory models, object-arranged highlights, and information, and be industry-prepared.</p>
        <h2>Key Features</h2><p>Go from a beginner level to a confident level of Python programming * Increase your in-depth understanding of Python's design, memory models, and more * Work on a competitive project at the end of the course</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-programming</p>
        <h2>About the Author</h2><h3>Learnkart Technology Private Limited</h3><p>Learnkart understands the importance of upskilling and its impact on you. They pride themselves in creating specialized e-learning courses that will not only prepare you for certification exams but also help you gain hands-on knowledge for real-world applications. Thousands of students have developed their skills with Learnkart through a variety of courses, such as PgMP, Risk Management Professional (RMP), speech recognition, Python, Amazon Web Services (AWS), and more. Learnkart regularly adds courses to its portfolio to lend more value to your learning experience.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction</h4><p></p><br><h4>2. Python Basics</h4><p></p><br><h4>3. Data Operations</h4><p></p><br><h4>4. Conditional Statements and Functions</h4><p></p><br><h4>5. Error Handling and File Operations</h4><p></p><br><h4>6. Shell Scripting and Django</h4><p></p><br><h4>7. Course End Project</h4><p></p>

You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.

Python Programming

Demand for data analysts, data scientists and data engineers tripled over the past five years, rising 231%. That's much faster than job postings overall in the UK, which grew 36%. Hence, the demand for skilled data analysts has skyrocketed in the UK and worldwide! On average, salaries for these data analysts' roles are £64,376 and keep rising.
With a broader overview of the data analyst's responsibility, data chart, and presentation tools, the bundle describes how to filter, improve and handle data to present various information. You'll learn process management, how to analyse data and how to make vital business decisions. The data analyst bundle will buckle you up to perform simple to complex business analysis tasks effectively and efficiently.
Furthermore, the data analyst course accentuates developing your programming skills for a data analyst, such as Python, SQL, and R. What's more? Besides strengthening Microsoft Excel skills for a data analyst, you can improve your expertise in Tableau, Google Data Studio and business analysis ability from this Data Analyst (Data Analytics) Bundle course. Hence, you can be an efficient data analyst and a competent candidate for a challenging position.
Learning Outcomes of Data Analyst (Data Analytics)
Upon completion of the Data Analyst (Data Analytics) program, you will have acquired:
Acquire a big-picture understanding of the data analyst role
Fundamentals of data analysis (data analysis), including process management, tools for data analysis, various charts
Ability to conduct thorough research and turn raw data into presentable information
Perform many data operations in Python's famous "pandas" library, including grouping, pivoting, joining and more!
Learn about NLP, Machine Learning (ML), data visualisation using Matplotlib, Seaborn and much more
Knowledge of all the essential SQL commands and become proficient in SQL tools like GROUP BY, JOINS, and Subqueries
Become competent in using sorting and filtering commands in SQL
Learn general cloud computing concepts and gain a solid understanding of the fundamental systems on which the cloud is based
Learn how to use the R programming language for data science, machine learning, and data visualisation
Learn data analytics with Tableau, business intelligence and data mining masterclass, basic Google Data studio
This Data Analyst (Data Analytics) Bundle Consists of the following Premium courses:
Course 01: Introduction to Data Analysis
Course 02: Python for Data Analysis
Course 03: SQL Programming Masterclass
Course 04: Data Science & Machine Learning with R
Course 05: Data Analytics with Tableau
Course 06: Basic Google Data Studio
Course 07: Business Intelligence and Data Mining Masterclass
Course 08: Business Analysis Level 3
Course 09: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query
Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002)
Course 11: GDPR Data Protection Level 5
Course 12: ChatGPT Crash Course
Upon finishing this 12-course Data Analyst (Data Analytics) bundle, you will acquire the necessary skills for a thriving career in this rapidly expanding industry. Waste no more time - enrol today and commence your path towards a promising future!
The Curriculum of the Data Analyst Bundle:
Course 1: Introduction to Data Analysis
Introduction
Agenda and Principles of Process Management
The Voice of the Process
Working as One Team for Improvement
Exercise: The Voice of the Customer
Tools for Data Analysis
The Pareto Chart
The Histogram
The Run Chart
Exercise: Presenting Performance Data
Understanding Variation
The Control Chart
Control Chart Example
Control Chart Special Cases
Interpreting the Control Chart
Control Chart Exercise
Strategies to Deal with Variation
Using Data to Drive Improvement
A Structure for Performance Measurement
Exercise
Course Project
Test your Understanding
Course 2: Python for Data Analysis
Welcome, Course Introduction & overview, and
Environment set-up
Python Essentials
Python for Data Analysis using NumPy
Python for Data Analysis using Pandas
Python for Data Visualization using mat plotlib
Python for Data Visualization using Seaborn
Python for Data Visualization using pandas
Python for interactive & geographical plotting using Plotly
and Cufflinks
Capstone Project - Python for Data Analysis &
Visualisation
Python for Machine Learning (ML) - sci-kit-learn - Linear
Regression Model
Python for Machine Learning - scikit-learn - Logistic
Regression Model
Python for Machine Learning - scikit-learn - K Nearest
Neighbours
Python for Machine Learning - scikit-learn - Decision Tree
and Random Forests
Python for Machine Learning - scikit-learn -Support Vector
Machines (SVMs)
Python for Machine Learning - scikit-learn - K Means
Clustering
Python for Machine Learning - scikit-learn - Principal
Component Analysis (PCA)
Recommender Systems with Python - (Additional Topic)
Python for Natural Language Processing (NLP) - NLTK -
(Additional Topic)
Course 3: SQL Programming Masterclass
Introduction
Setting up Course Tools
Browsing & Filtering Data
Filter Data with AND, OR, NOT Operators
Math Operations
String Concatenation
Tidy Up Your Results: Sorting, Alias Names, Limit
Records
NULL, DISTINCT & CASE Statements
Functions in SQL: Multiple-Row Functions
Functions in SQL: Single-Row Functions
Nested Queries
Casting & Data Conversion
Combination Relationships
Joins in SQL
Data Manipulation (DML)
SQL Data Definition Language (DDL)
Course 4: Data Science & Machine Learning with R
Data Science and Machine Learning Course Intro
Data Types and Structures in R
Data Types and Structures in R
Intermediate R
Data Manipulation in R
Data Visualisation in R
Creating Reports with R Markdown
Building WebApps with R Shiny
Introduction to Machine Learning
Starting A Career in Data Science
Course 5: Data Analytics with Tableau
Introduction to the Course
Project 1: Discount Mart (Sales and Profit Analytics)
Project 2: Green Destinations (HR Analytics)
Project 3: Superstore (Sales Agent Tracker)
Northwind Trade (Shipping Analytics)
Project 5: Tesla (Stock Price Analytics)
Bonus: Introduction to Database Concepts
Tableau Stories
Course 6: Basic Google Data Studio
Introduction to GDS
Data Visualisation
Geo-visualisation
A Socio-Economic Case Study
Course 7: Business Intelligence and Data Mining Masterclass
What is Business Intelligence?
Starting Case in understanding BI needs in diff phases
of business
Decision-Making Process and Need for IT Systems
Problem Structure and Decision Support System
Introduction to BI Applications
Dashboard presentation systems
Different Types of Charts used in 131 Dashboards
Good Dashboard and BSC
Examples of Bad Dashboards
Process of KPI Development
Overview of Data Warehouse
More explanation of the data warehouse and steps in
processing
Data Warehouse Architecture -Tiers
Data Warehouse - components
Approach to developing data structure and Models of
database
Basics of Data Mining - Meaning
Basics of Data Mining - Stages of DM
Data Mining Technique - Classification
Data mining - Clustering
Data Mining - Association Rules
Data Mining Applications - Banking
Data Mining Applications - CRM
Data Mining Applications - other industries
Course 8: Business Analysis Level 3
Introduction to Business Analysis
Business Processes
Business Analysis Planning and Monitoring
Strategic Analysis and Product Scope
Solution Evaluation
Investigation Techniques
Ratio Analysis
Stakeholder Analysis & Management
Process Improvement with Gap Analysis
Documenting & Managing Requirements
Career Prospect as a Business Analyst in the UK
Course 9: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query
Introduction
Prepaid Expenses Models: Resources Download
Accounting for Prepaid Expenses
Excel Formulas Detailed: Introduction to three Excel
Models
Formula-Based Prepaid Expenses Model
Calculate Prepaid Expenses Amortisation from Exact
Start Date
Prepaid Expenses Summary with Power Query & Pivot
Table
Advanced VBA Prepaid Expenses Amortisation Model
BONUS: Dynamic Dashboard for Divisional Profit &
Loss statements: Easy Way
Power Query & Pivot Tables based Dashboard without any Formulas, Fully Dynamic
Course 10: Cloud Computing CompTIA Cloud+ (CV0-002)
What You Need to Know
Introducing the Cloud
System Requirements for Cloud Deployments
Cloud Storage
Cloud Compute
Cloud Networking
Cloud Security
Migrating to the Cloud
Maintaining Cloud Solutions
Troubleshooting Cloud Solutions
Course 11: GDPR Data Protection
GDPR Basics
GDPR Explained
Lawful Basis for Preparation
Rights and Breaches
Course 12: ChatGPT Crash Course
Sign up for an OpenAI Account
What can ChatGPT do for you?
ChatGPT for Business
ChatGPT for Teaching
ChatGPT for Research
Limitations of ChatGPT
Additional Videos
 CPD 
130 CPD hours/points  Accredited by CPD Quality Standards
 Who is this course for? 
Anyone from any background can enrol in this Data Analyst (Data Analytics) bundle. Persons with similar professions can also refresh or strengthen their skills by enrolling in this Data Analyst (Data Analytics) bundle course. Students can take this course to gather professional knowledge besides their studies or for the future. 
Aspiring data analysts
Business analyst professionals who want to improve their skills
Anyone who desires to develop their data analysis skills
 
 Career path 
This Data Analyst (Data Analytics) Bundle is ideal for those who want to take their development to the next level and fast-track their career in any of the following roles:
Data Analyst: £30,000 - £50,000
Senior Data Analyst: £50,000 - £70,000
Data Scientist: £70,000 - £100,000
Business Intelligence Manager: £60,000 - £80,000
Machine Learning Engineer: £60,000 - £90,000
 Certificates 
Certificate of Completion
Digital certificate - Included
PDF certificate - Free (Previously, it was £6*12 = £72)
Certificate of Completion
Hard Copy certificate - Included
Hard Copy Certificate - Free (For The Title Course: Previously, it was £10)
Other Hard Copy certificates are available for £10 each
Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Google Data Studio

GDPR

Data Mining

Tableau

Business Intelligence (BI)

ChatGPT

12 in 1 Career Guided Programme | Python, Data Science, ML, Tableau, SQL Programming, Data Mining, Business Analysis | 130 CPD Points | Tutor Support | Lifetime Access

Data Analyst (Data Analytics) Diploma - CPD Certified

Master Python 3.10 from the experts with this course and demonstrate programming wizardry in Python. Go from a geek to a wizard in no time with this well-structured video course and write Python code like a pro. Learn about the often-forgotten basics and special features of programming and Python in general.

The Ultimate Guide to Python Programming with Python 3.10

<strong>This course offers you the exact skills and principles you need to deliver a project using Python along with the tools you can use to create solid Python code. Master the language constructs, tools, and terminology with this carefully designed course for beginners.</strong>
        <p>Python is one of the most popular computer programming languages and this hands-on course for beginners will make it easy for someone who wants to get started with learning Python.

This course takes you through the introduction to the course and the learning objectives. You will learn to get started with Replit and write your first Python program. You will learn to store, process, and compare information in Python as well as learn to tidy messy data. You will learn about variables, strings, dictionaries, indexing, and slicing in Python. Further, you will understand loops in Python along with break, continue, and else functions.

Moving ahead, you will understand error handling in Python, get to know the try-and-catch block, and learn to add resources to Replit. You will also understand function parameters and explore the difference between local and global variables, and positional and keyword parameters. You will learn to work with JSON and clean the data along with various numeric functions such as min, max, and sum.

By the end of the course, you will gain a solid foundation in Python programming and be able to write high-quality code in Python.

All resources and code files are placed here: https://github.com/PacktPublishing/Python-Bootcamp-in-a-day---Python-Programming-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Master Python programming fundamentals<br>Learn how to install Python to get up and running in no time<br>Learn how to process data to create Olympics Medals Tables<br>Solve multiple specific problems for a customer in the real world<br>Learn all about objects and variables in Python<br>Understand functions and function parameters in Python</p>
        <h2>Audience</h2><p>Whether you are a software engineer, software developer, computer programmer, web developer, transitioning into a new role, or simply someone who wants to understand what makes Python tick, this is the place to start. If you are preparing for a Python certification or job role, this course is for you.</p>
        <h2>Approach</h2><p>This course is video based course with no supporting document necessary. It takes you through the fundamentals of Python and teaches you how to use it with a simple tool so you can master the fundamentals in a day.</p>
        <h2>Key Features</h2><p>Be fluent in writing Python code and working with all the fundamentals of the language such as a list, string, function, dictionary, array, set, and much more * Learn to use Python's data analysis capabilities to analyze data from a real-world data source * A complete overview of the basics with exercises to test your knowledge of storing, processing, and storing information</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Bootcamp-in-a-day---Python-Programming-for-Beginners</p>
        <h2>About the Author</h2><h3>Paul Ashun</h3><p>Paul Ashun is the CEO, MD, and chief consultant at Pashun Consulting Ltd., specializing in Scrum coaching and leadership within major global organizations. They are the authors of over 10 books on Scrum such as The Power of Scrum in the Real World, Confessions of a Scrum Master as well as the upcoming Agile User Storybook.

He started as a software developer and over ten years later, he became an Agile portfolio manager. He is a certified Scrum Master and a PMO manager. He has led projects for the BBC, General Electric, Oracle, BSkyB, HiT Entertainment, and Razorfish. He has been coaching product owners and business analysts in international blue-chip companies, dating back to 1999, in Agile and Scrum practices such as writing user stories.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video is an introduction the course and what you can expect from the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Learning Objectives - Introduction</p><p style='margin: 0'>This video explains the learning objectives of the course.</p></td></tr><tr><td style='padding: 15px;'><p>3. What Is Python?</p><p style='margin: 0'>This video explains what Python is and the need for Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. The Example</p><p style='margin: 0'>This video explains an example of a software engineer working in a media company to support the development team.</p></td></tr></tbody></table><br><h4>2. Getting Set Up</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Getting Set Up</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. How to Get Started with Replit</p><p style='margin: 0'>This video explains how to get started with Replit.</p></td></tr><tr><td style='padding: 15px;'><p>3. Writing Your First Python Program-Hello World</p><p style='margin: 0'>This video explains writing your first Python program-Hello World.</p></td></tr></tbody></table><br><h4>3. Storing Information</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Storing Information</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Objects and Variables</p><p style='margin: 0'>This video explains objects and variables in Python using an example of Olympic results.</p></td></tr><tr><td style='padding: 15px;'><p>3. Variable Names</p><p style='margin: 0'>This video explains variable names in Python and the rules to use them.</p></td></tr><tr><td style='padding: 15px;'><p>4. Numbers</p><p style='margin: 0'>This video explains the different types of numbers in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. Strings</p><p style='margin: 0'>This video explains strings in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. Showing Output When Running Python</p><p style='margin: 0'>This video explains showing output when running Python.</p></td></tr></tbody></table><br><h4>4. Processing Information</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Processing Information</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Processing Information</p><p style='margin: 0'>This video explains processing information in Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Indexing</p><p style='margin: 0'>This video explains indexing in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Slices</p><p style='margin: 0'>This video explains slices in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. String Functions</p><p style='margin: 0'>This video explains string functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. The 'input' Function</p><p style='margin: 0'>This video explains the input function in Python.</p></td></tr><tr><td style='padding: 15px;'><p>7. More String Operations</p><p style='margin: 0'>This video explains more string operations in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Converting Between Different Types</p><p style='margin: 0'>This video explains converting between different types.</p></td></tr></tbody></table><br><h4>5. Comparing Information</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Comparing Information</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Comparing Information</p><p style='margin: 0'>This video explains comparing information in Python.</p></td></tr></tbody></table><br><h4>6. Tidying Messy Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Tidying Messy Data</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidying Messy Data</p><p style='margin: 0'>This video explains how to tidy messy data.</p></td></tr></tbody></table><br><h4>7. Storing Multiple Things</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Storing Multiple Things</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Storing Multiple Things</p><p style='margin: 0'>This video explains how to store multiple things.</p></td></tr><tr><td style='padding: 15px;'><p>3. Introducing Python Lists</p><p style='margin: 0'>This video explains lists in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Adding Items to a List</p><p style='margin: 0'>This video explains how to add items to a list.</p></td></tr><tr><td style='padding: 15px;'><p>5. Indexing and Slicing Lists</p><p style='margin: 0'>This video explains indexing and slicing lists.</p></td></tr><tr><td style='padding: 15px;'><p>6. Deleting Items in a List</p><p style='margin: 0'>This video explains how to delete items in a list.</p></td></tr><tr><td style='padding: 15px;'><p>7. Tuples</p><p style='margin: 0'>This video explains tuples in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Long Lines in Python</p><p style='margin: 0'>This video explains long lines in Python.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dictionaries</p><p style='margin: 0'>This video explains dictionaries in Python.</p></td></tr><tr><td style='padding: 15px;'><p>10. List Functions - More Ways to Work with Lists</p><p style='margin: 0'>This video explains list functions.</p></td></tr><tr><td style='padding: 15px;'><p>11. Sets</p><p style='margin: 0'>This video explains sets in Python.</p></td></tr></tbody></table><br><h4>8. Processing Collections</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Processing Collections</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple 'for' Loop</p><p style='margin: 0'>This video explains a simple for loop.</p></td></tr><tr><td style='padding: 15px;'><p>3. More Complex 'for' loops - 'break', 'continue', and 'else'</p><p style='margin: 0'>This video explains more complex 'for' loops.</p></td></tr><tr><td style='padding: 15px;'><p>4. The 'range' Function - How to Create a List of Numbers</p><p style='margin: 0'>This video explains how to create a list of numbers.</p></td></tr><tr><td style='padding: 15px;'><p>5. The 'while' Loop - Do Something for a While</p><p style='margin: 0'>This video explains the while loop.</p></td></tr></tbody></table><br><h4>9. Robust Programs - Handling Errors</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Robust Programs - Handling Errors</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Robust Programs - Handling Errors</p><p style='margin: 0'>This video explains error handling in Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Look Before You Leap</p><p style='margin: 0'>This video explains one way of making the code more robust, which is called 'look before you leap'.</p></td></tr><tr><td style='padding: 15px;'><p>4. The Try and Except Block</p><p style='margin: 0'>This video explains the try and except block in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. The Catch-All Block</p><p style='margin: 0'>This video explains the catch-all block.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adding Resources to Replit</p><p style='margin: 0'>This video explains how to add resources to Replit.</p></td></tr><tr><td style='padding: 15px;'><p>7. The Finally Block</p><p style='margin: 0'>This video explains the finally block.</p></td></tr></tbody></table><br><h4>10. Saving the Results - Working with Files</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Saving the Results - Working with Files</p><p style='margin: 0'>This video explains the learning objectives of the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Adding Bad Data</p><p style='margin: 0'>This video explains adding bad data.</p></td></tr><tr><td style='padding: 15px;'><p>3. Saving the Results - Working with Files</p><p style='margin: 0'>This video explains saving the results and working with files.</p></td></tr><tr><td style='padding: 15px;'><p>4. Binary File Modes</p><p style='margin: 0'>This video explains binary file modes.</p></td></tr><tr><td style='padding: 15px;'><p>5. Text File Modes</p><p style='margin: 0'>This video explains text file modes.</p></td></tr><tr><td style='padding: 15px;'><p>6. The With Statement</p><p style='margin: 0'>This video explains the with statement.</p></td></tr></tbody></table><br><h4>11. Breaking Up Larger Programs, Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Breaking Up Larger Programs, Part 1</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. About Replit.config</p><p style='margin: 0'>This video explains replit.config.</p></td></tr><tr><td style='padding: 15px;'><p>3. Functions</p><p style='margin: 0'>This video explains functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Function Parameters</p><p style='margin: 0'>This video explains function parameters.</p></td></tr><tr><td style='padding: 15px;'><p>5. Local Versus Global Variables</p><p style='margin: 0'>This video explains local versus global variables.</p></td></tr><tr><td style='padding: 15px;'><p>6. Positional and Keyword Parameters</p><p style='margin: 0'>This video explains positional and keyword parameters.</p></td></tr><tr><td style='padding: 15px;'><p>7. Return Statement</p><p style='margin: 0'>This video explains the return statement.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working With JSON</p><p style='margin: 0'>This video explains working with JSON.</p></td></tr><tr><td style='padding: 15px;'><p>9. Code Comments and Clear Code</p><p style='margin: 0'>This video explains code comments in Python.</p></td></tr></tbody></table><br><h4>12. Summarising and Presenting the Results</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Summarising and Presenting the Results</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Summarising and Presenting the Results Part 1</p><p style='margin: 0'>This video explains how to summarise the results using statistics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Cleaning the Data</p><p style='margin: 0'>This video explains cleaning the data in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Summarising and Presenting the Results Part 2</p><p style='margin: 0'>This video explains the code in which we will get a sum of the scores of a country.</p></td></tr><tr><td style='padding: 15px;'><p>5. More Numeric Functions - Min, Max, Sum</p><p style='margin: 0'>This video explains min, max, and sum functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. Formatted Strings, f-strings</p><p style='margin: 0'>This video explains formatted strings and f-strings in Python.</p></td></tr></tbody></table><br><h4>13. Breaking Up Larger Programs, Part 2</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Breaking Up Larger Programs, Part 2</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Splitting Code into Modules</p><p style='margin: 0'>This video explains splitting code into modules.</p></td></tr><tr><td style='padding: 15px;'><p>3. Namespaces</p><p style='margin: 0'>This video explains namespaces in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Our Modules</p><p style='margin: 0'>This video explains how to use modules in Python.</p></td></tr></tbody></table><br><h4>14. Getting Set Up on a PC or MAC</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Getting Set Up on a PC or MAC</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Set Up</p><p style='margin: 0'>This video explains how to set up Python on our systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. Starting Python on Windows 10</p><p style='margin: 0'>This video explains how to start Python on Windows 10.</p></td></tr><tr><td style='padding: 15px;'><p>4. Starting a Terminal or Command Prompt on Windows</p><p style='margin: 0'>This video explains how to start a terminal or command prompt on Windows.</p></td></tr><tr><td style='padding: 15px;'><p>5. Installing Python on Windows</p><p style='margin: 0'>This video explains the installation of Python on Windows.</p></td></tr><tr><td style='padding: 15px;'><p>6. Installing Python on MacOS</p><p style='margin: 0'>This video explains the installation on Python on MacOS.</p></td></tr><tr><td style='padding: 15px;'><p>7. Quick Python Test - Running Python</p><p style='margin: 0'>This video explains how to run a Python script.</p></td></tr><tr><td style='padding: 15px;'><p>8. Python Scripts and IDLE</p><p style='margin: 0'>This video explains Python scripts and IDE.</p></td></tr><tr><td style='padding: 15px;'><p>9. Code Editors and IDEs</p><p style='margin: 0'>This video explains code editors and IDEs.</p></td></tr><tr><td style='padding: 15px;'><p>10. Running Python Scripts</p><p style='margin: 0'>This video explains how to run Python scripts.</p></td></tr></tbody></table>

This course offers you the exact skills and principles you need to deliver a project using Python along with the tools you can use to create solid Python code. Master the language constructs, tools, and terminology with this carefully designed course for beginners.

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