Packt

Data Science

Python

Are you someone wishing to learn the skills to analyze data professionally using Power BI? Do you want to learn to create impressive reports? Then this is the right course for you! Learn the complete workflow in Power BI from A-to-Z and advanced analytics with DAX-calculated columns and measures, forecasting, and advanced analytics.

Microsoft Power BI - The Complete Masterclass [2023 EDITION]

General Admission

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			<h2>Overview
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					<p><span>This comprehensive course on </span><b><span>Data Science &amp; Machine Learning with Python</span></b> will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This </span><b>Data Science &amp; Machine Learning with Python</b> comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.<br /></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this </span><b><span>Data Science &amp; Machine Learning with Python</span></b>. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our </span><b><span>Data Science &amp; Machine Learning with Python</span></b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</p><ul><li>Open doors of opportunities</li><li>Increase your adaptability</li><li>Keep you relevant</li><li>Boost confidence</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>2 sections • 90 lectures • 10:24:00 total length</p></div><div><div><div><div><div><div><div><p>•Course Overview &amp; Table of Contents: 00:09:00</p></div><div><div><p>•Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00</p></div><div><div><p>•Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00</p></div><div><div><p>•System and Environment preparation - Part 1: 00:08:00</p></div><div><div><p>•System and Environment preparation - Part 2: 00:06:00</p></div><div><div><p>•Learn Basics of python - Assignment 1: 00:10:00</p></div><div><div><p>•Learn Basics of python - Assignment 2: 00:09:00</p></div><div><div><p>•Learn Basics of python - Functions: 00:04:00</p></div><div><div><p>•Learn Basics of python - Data Structures: 00:12:00</p></div><div><div><p>•Learn Basics of NumPy - NumPy Array: 00:06:00</p></div><div><div><p>•Learn Basics of NumPy - NumPy Data: 00:08:00</p></div><div><div><p>•Learn Basics of NumPy - NumPy Arithmetic: 00:04:00</p></div><div><div><p>•Learn Basics of Matplotlib: 00:07:00</p></div><div><div><p>•Learn Basics of Pandas - Part 1: 00:06:00</p></div><div><div><p>•Learn Basics of Pandas - Part 2: 00:07:00</p></div><div><div><p>•Understanding the CSV data file: 00:09:00</p></div><div><div><p>•Load and Read CSV data file using Python Standard Library: 00:09:00</p></div><div><div><p>•Load and Read CSV data file using NumPy: 00:04:00</p></div><div><div><p>•Load and Read CSV data file using Pandas: 00:05:00</p></div><div><div><p>•Dataset Summary - Peek, Dimensions and Data Types: 00:09:00</p></div><div><div><p>•Dataset Summary - Class Distribution and Data Summary: 00:09:00</p></div><div><div><p>•Dataset Summary - Explaining Correlation: 00:11:00</p></div><div><div><p>•Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00</p></div><div><div><p>•Dataset Visualization - Using Histograms: 00:07:00</p></div><div><div><p>•Dataset Visualization - Using Density Plots: 00:06:00</p></div><div><div><p>•Dataset Visualization - Box and Whisker Plots: 00:05:00</p></div><div><div><p>•Multivariate Dataset Visualization - Correlation Plots: 00:08:00</p></div><div><div><p>•Multivariate Dataset Visualization - Scatter Plots: 00:05:00</p></div><div><div><p>•Data Preparation (Pre-Processing) - Introduction: 00:09:00</p></div><div><div><p>•Data Preparation - Re-scaling Data - Part 1: 00:09:00</p></div><div><div><p>•Data Preparation - Re-scaling Data - Part 2: 00:09:00</p></div><div><div><p>•Data Preparation - Standardizing Data - Part 1: 00:07:00</p></div><div><div><p>•Data Preparation - Standardizing Data - Part 2: 00:04:00</p></div><div><div><p>•Data Preparation - Normalizing Data: 00:08:00</p></div><div><div><p>•Data Preparation - Binarizing Data: 00:06:00</p></div><div><div><p>•Feature Selection - Introduction: 00:07:00</p></div><div><div><p>•Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00</p></div><div><div><p>•Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00</p></div><div><div><p>•Feature Selection - Recursive Feature Elimination: 00:11:00</p></div><div><div><p>•Feature Selection - Principal Component Analysis (PCA): 00:09:00</p></div><div><div><p>•Feature Selection - Feature Importance: 00:07:00</p></div><div><div><p>•Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00</p></div><div><div><p>•Algorithm Evaluation Techniques - Introduction: 00:07:00</p></div><div><div><p>•Algorithm Evaluation Techniques - Train and Test Set: 00:11:00</p></div><div><div><p>•Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00</p></div><div><div><p>•Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00</p></div><div><div><p>•Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Introduction: 00:09:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Log Loss: 00:03:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Classification Report: 00:04:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00</p></div><div><div><p>•Algorithm Evaluation Metrics - Mean Square Error: 00:03:00</p></div><div><div><p>•Algorithm Evaluation Metrics - R Squared: 00:04:00</p></div><div><div><p>•Classification Algorithm Spot Check - Logistic Regression: 00:12:00</p></div><div><div><p>•Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00</p></div><div><div><p>•Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00</p></div><div><div><p>•Classification Algorithm Spot Check - Naive Bayes: 00:04:00</p></div><div><div><p>•Classification Algorithm Spot Check - CART: 00:04:00</p></div><div><div><p>•Classification Algorithm Spot Check - Support Vector Machines: 00:05:00</p></div><div><div><p>•Regression Algorithm Spot Check - Linear Regression: 00:08:00</p></div><div><div><p>•Regression Algorithm Spot Check - Ridge Regression: 00:03:00</p></div><div><div><p>•Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00</p></div><div><div><p>•Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00</p></div><div><div><p>•Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00</p></div><div><div><p>•Regression Algorithm Spot Check - CART: 00:04:00</p></div><div><div><p>•Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00</p></div><div><div><p>•Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00</p></div><div><div><p>•Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00</p></div><div><div><p>•Pipelines : Data Preparation and Data Modelling: 00:11:00</p></div><div><div><p>•Pipelines : Feature Selection and Data Modelling: 00:10:00</p></div><div><div><p>•Performance Improvement: Ensembles - Voting: 00:07:00</p></div><div><div><p>•Performance Improvement: Ensembles - Bagging: 00:08:00</p></div><div><div><p>•Performance Improvement: Ensembles - Boosting: 00:05:00</p></div><div><div><p>•Performance Improvement: Parameter Tuning using Grid Search: 00:08:00</p></div><div><div><p>•Performance Improvement: Parameter Tuning using Random Search: 00:06:00</p></div><div><div><p>•Export, Save and Load Machine Learning Models : Pickle: 00:10:00</p></div><div><div><p>•Export, Save and Load Machine Learning Models : Joblib: 00:06:00</p></div><div><div><p>•Finalizing a Model - Introduction and Steps: 00:07:00</p></div><div><div><p>•Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00</p></div><div><div><p>•Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00</p></div><div><div><p>•Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00</p></div><div><div><p>•Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00</p></div><div><div><p>•Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00</p></div><div><div><p>•Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00</p></div><div><div><p>•Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resources - Data Science &amp; Machine Learning with Python: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Learning & Development

Computing Skills

Data Skills

Trainers Course

Data Science & Machine Learning with Python

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				<h3><span id="docs-internal-guid-8c7677fd-7fff-3dba-9e42-22d2785cca3c"><p><span>Experience the future of </span><span>geographical analysis</span><span> with our </span><span>Machine Learning for Predictive Maps in Python and Leaflet</span><span> course. Master the unique blend of programming, machine learning, and geographic information systems, all while honing your ability to predict and visualise spatial data in a powerful and effective way. This course offers you an unparalleled understanding of </span><span>modern map creation</span><span>, combined with the magic of prediction using </span><span>machine learning models</span><span>.</span></p><br /><p><span>Starting from the ground up, you'll be introduced to all the necessary setups and installations. After that, you will be diving into the depth of </span><span>Django server-side code</span><span> and front-end application code writing. The heart of the course lies in learning how to automate the machine learning pipeline, leading you to </span><span>easily create predictive models</span><span>. </span></p><br /><p><span>Improve your maps with </span><span>Leaflet programming</span><span>, making your predictions accurate and also </span><span>visually striking</span><span>. By the end of this course, you will be armed with experience furnished by our comprehensive project </span><span>source code and assignments</span><span>, empowering you to drive data-driven decisions and insightful spatial analysis. </span><span>Join us</span><span> and </span><span>map your way to success</span><span>! Sign up today.</span></p><br /><br /><p><span>Learning Outcomes:</span></p><p><span>Upon completion of the Machine Learning course, you will be able to:</span></p><br /><ul><li><p><span>Understand how to set up and install relevant software and libraries.</span></p></li><li><p><span>Master Django server-side and application front-end code writing.</span></p></li><li><p><span>Gain proficiency in the concepts and implementation of Machine Learning.</span></p></li><li><p><span>Learn to automate Machine Learning pipelines for efficient workflows.</span></p></li><li><p><span>Acquire skills in Leaflet programming for enhanced map visuals.</span></p></li><li><p><span>Handle project source code effectively for real-world projects.</span></p></li><li><p><span>Apply knowledge practically via assignments and gain experience.</span></p></li></ul><br /><br /><p><span>Who is this course for?</span></p><p><span>This Machine Learning course is ideal for:</span></p><br /><ul><li><p><span>Aspiring Data Scientists keen on harnessing geographical data.</span></p></li><li><p><span>GIS professionals aiming to integrate Machine Learning into their skill set.</span></p></li><li><p><span>Software Developers interested in creating geographically-focused applications.</span></p></li><li><p><span>Analysts keen on enhancing their data visualisation skills with mapping.</span></p></li></ul><br /><br /><p><span>Certification</span></p><p><span>After studying the course materials of the </span><span>Machine Learning for Predictive Maps in Python and Leaflet</span><span> course, there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test, you have a </span><span>range of certification options</span><span> to choose from. You can claim a CPD Accredited PDF Certificate for</span><span> £4.99</span><span>, a CPD Accredited Hardcopy Certificate at</span><span> £8.00</span><span>, or you may choose to receive a PDF Transcript for</span><span> £4.99</span><span> or a</span><span> </span><span>Hardcopy Transcript for</span><span> £9.99</span><span>. Select according to your needs, and we assure </span><span>timely delivery</span><span> of your chosen certificate.</span></p><br /><br /><p><span>Requirements</span></p><p><span>This professionally designed </span><span>Machine Learning for Predictive Maps in Python and Leaflet </span><span>course does not require you to have any prior qualifications or experience. It is open to everyone. You will be able to access the course from anywhere at any time. Just enrol and start learning!</span></p><br /><br /><p><span>Career Path:</span></p><p><span>Our Machine Learning course will help you to </span><span>pursue a range of career paths</span><span>, such as:</span></p><br /><ul><li><p><span>Junior Data Analyst: £25,000 - £35,000 annually.</span></p></li><li><p><span>Data Scientist: £40,000 - £60,000 annually.</span></p></li><li><p><span>GIS Analyst: £30,000 - £45,000 annually.</span></p></li><li><p><span>Geospatial Software Developer: £35,000 - £55,000 annually.</span></p></li><li><p><span>Machine Learning Engineer: £50,000 - £80,000 annually.</span></p></li><li><p><span>Lead Data Scientist (GIS speciality): £70,000 - £100,000+ annually.</span></p></li></ul></span></h3>					</div>
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					<td colspan="4">Section 01: Introduction</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td colspan="4">Section 02: Setup and Installations</td>
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					<td>Python Installation</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Creating a Python Virtual Environment</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Installing Visual Studio Code IDE</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td>Installing PostgreSQL Database Server Part 1</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td>Installing PostgreSQL Database Server Part 2</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td colspan="4">Section 03: Writing the Django Server-Side Code</td>
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					<td>Adding the settings.py Code</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td>Creating a Django Model</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Adding the admin.py Code</td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td colspan="4">Section 04: Writing the Application Front-end Code</td>
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					<td>Creating Template Files</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating Django Views</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating URL Patterns for the REST API</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Adding the index.html code</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Adding the layout.html code</td>
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					<td><span><i></i> 00:19:00</span></td>
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					<td>Creating our First Map</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Adding Markers</td>
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					<td><span><i></i> 00:16:00</span></td>
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					<td colspan="4">Section 05: Machine Learning</td>
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					<td>Installing Jupyter Notebook</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td>Data Pre-processing</td>
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					<td><span><i></i> 00:31:00</span></td>
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					<td>Model Selection</td>
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					<td><span><i></i> 00:20:00</span></td>
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					<td>Model Evaluation and Building a Prediction Dataset</td>
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					<td><span><i></i> 00:11:00</span></td>
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					<td colspan="4">Section 06: Automating the Machine Learning Pipeline</td>
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					<td>Creating a Django Model</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Embedding the Machine Learning Pipeline in the Application</td>
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					<td><span><i></i> 00:42:00</span></td>
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					<td>Creating a URL Endpoint for our Prediction Dataset</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td colspan="4">Section 07: Leaflet Programming</td>
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					<td>Creating Multiple Basemaps</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Creating the Marker Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating the Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td>Creating the Predicted Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td>Creating the Predicted High Risk Point Layer Group</td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td>Creating the Legend</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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					<td>Creating the Prediction Score Legend</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 08: Project Source Code</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resource</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Machine Learning for Predictive Maps in Python and Leaflet</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Machine Learning for Predictive Maps in Python and Leaflet

Redhill

<h3><b>Network automation training course description</b></h3> 
				<p> This course concentrates on the technical side of tools
and languages for network DevOps rather than the soft
skills. These tools include Python, Ansible, Git and
NAPALM
By the end of the course delegates should be able to
recognise the tools that they can use to automate their
networks and be able to use the knowledge gained to
feel confident approaching network automation.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
          <li>Describe network DevOps.</li>
					<li>Choose network automation tools.</li>
                    <li>Explain the role of various network automation
technologies including:<br />

                    Python<br />
                   Ansible<br />
                    Git<br />
											NAPALM</li>
                    </ul>
       
        <h5>
        <b>Network automation training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>Those wishing to learn about the tools of network
automation.</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>Introduction to data communications.</p>
              
							<li><b>Duration</b></li>
							
              <p>1 day
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>Network automation training course contents</b></h3>
      <ul>
				<li>What is DevOps and network automation</li><p>
Programming and automating networks, networks
and clouds, AWS, OpenStack, SDN, DevOps for
network operations. Unit testing. Hype vs reality.
Benefits and features.
				</p><li>Network monitoring and troubleshooting</li><p>
Traditional methods, SNMP. Netflow and xflow.
Traditional automation. Streaming telemetry.
Event driven automation. gRPC, Protocol buffers.
</p><li>Configuration management</li><p>
Catch 22 and initial configuration. ZTP, POAP.
Traditional automation. TFTP. Ansible vs the rest
(chef, salt, puppet). Jinja2 and templating. How
ansible works.
</p><li>Network programmability</li><p>
Programming languages. Linux, shell scripting.
Python vs the rest. Off box vs on box automation.
</p><li>Python network libraries</li><p>
Sockets pysnmp, ncclient, paramiko, netmiko,
pyez, NAPALM.
</p><li>APIs</li><p>
Proprietary APIs, CLI, NETCONF, RETCONF.
YANG, XML, YAML, JSON.
</p><li>Other tools</li><p>
Git, GitHub, Jenkins, JIRA and others.

</p>
      </ul>

Systems & Network Training

Network & Security

Network Engineer

Online Course Creation

Professional Skills Course (PSC)

ICDL (International Certification of Digital Literacy)

Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4

<strong>This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.</strong>
        <p>From self-driving cars to artificial intelligence (AI) bots, machine learning (ML) is slowly spreading its reach and making our devices smarter. If you have ever wanted to play a role in the future of technology development, then here is your chance to get started with ML. This course breaks the complex topics of ML into simple concepts that are easier to understand.
The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model.
By the end of this course, you will have gained the skills to create real-world ML solutions.
All the recourses for this course are avialable at https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional</p>
        <h2>What You Will Learn</h2><p>Detect credit card fraud by using probability densities<br>Become familiar with the natural language processing methodology<br>Use the Canadian Institute for Advanced Research-10 (CIFAR-10) object recognition dataset to implement a deep neural network<br>Improve image quality using Super-Resolution Convolutional Neural Network (SRCNN)<br>Solve a text classification task using multiple classification algorithms<br>Use K-means clustering in an unsupervised algorithm</p>
        <h2>Audience</h2><p>If you want to understand machine learning (ML) algorithms and concepts to build effective ML solutions for the modern world, this course is for you. Basic Python skills and a good understanding of mathematics are needed to get started with this course.</p>
        <h2>Approach</h2><p>With the help of engaging project building activities, examples, and quizzes, this course helps you to master the concepts of machine learning and gives you the confidence to build real-world solutions using supervised and unsupervised algorithms, neural networks, and a lot more.</p>
        <h2>Key Features</h2><p>Grasp the core concepts of machine learning (ML) * Find out how to use neural networks in ML projects * Learn how to build real-world projects using supervised and unsupervised learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional</p>
        <h2>About the Author</h2><h3>Eduonix Learning Solutions</h3><p>Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Machine Learning (ML)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML)?</p><p style='margin: 0'>This video explains the concept of ML.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types and Applications of Machine Learning (ML)</p><p style='margin: 0'>This video explains the various types and applications of ML.</p></td></tr><tr><td style='padding: 15px;'><p>4. Artificial Intelligence (AI) versus Machine Learning (ML)</p><p style='margin: 0'>This video illustrates the difference between AI and ML.</p></td></tr><tr><td style='padding: 15px;'><p>5. Essential Mathematics for Machine Learning (ML) and Artificial Intelligence (AI)</p><p style='margin: 0'>This video highlights some mathematical concepts that are needed to understand ML and AI.</p></td></tr></tbody></table><br><h4>2. Supervised Learning - Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Supervised Learning</p><p style='margin: 0'>This video provides an introduction to supervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Methods for Classification</p><p style='margin: 0'>This video explains the liner methods for classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear Methods for Regression</p><p style='margin: 0'>This video explains linear methods for regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Support Vector Machines (SVM)</p><p style='margin: 0'>This video explains the concept of SVM.</p></td></tr><tr><td style='padding: 15px;'><p>5. Basic Expansions</p><p style='margin: 0'>This video explains some basic expansions.</p></td></tr><tr><td style='padding: 15px;'><p>6. Model Selection Procedures</p><p style='margin: 0'>This video focuses on model selection procedures.</p></td></tr><tr><td style='padding: 15px;'><p>7. Bonus! Supervised Learning Project in Python - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that presents a supervised learning project in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Bonus! Supervised Learning Project in Python - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that presents a supervised learning project in Python.</p></td></tr></tbody></table><br><h4>3. Unsupervised Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Unsupervised Learning</p><p style='margin: 0'>This video provides an introduction to unsupervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Association Rules</p><p style='margin: 0'>This video focuses on the association rule learning method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Cluster Analysis</p><p style='margin: 0'>This video explains the concept of cluster analysis.</p></td></tr><tr><td style='padding: 15px;'><p>4. Reinforcement Learning</p><p style='margin: 0'>This video explains the concept of reinforcement learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Bonus! K-Means Clustering Project</p><p style='margin: 0'>This video presents a project on K-means clustering.</p></td></tr></tbody></table><br><h4>4. Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Neural Networks</p><p style='margin: 0'>This video provides an introduction to neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Perceptron</p><p style='margin: 0'>This video explains the concept of perceptron in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Backpropagation Algorithm</p><p style='margin: 0'>This video explains the backpropagation algorithm in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Training Procedures</p><p style='margin: 0'>This video focuses on training procedures in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Convolutional Neural Network (CNN)</p><p style='margin: 0'>This video explains the concept of CNN.</p></td></tr></tbody></table><br><h4>5. Real-world Machine Learning (ML)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Real-world Machine Learning (ML)</p><p style='margin: 0'>This video provides an introduction to real-world ML.</p></td></tr><tr><td style='padding: 15px;'><p>2. Choosing an Algorithm</p><p style='margin: 0'>This video explains how to choose an algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>3. Design and Analysis of Machine Learning (ML) Experiments</p><p style='margin: 0'>This video focuses on the design and analysis of ML experiments.</p></td></tr><tr><td style='padding: 15px;'><p>4. Common Software for Machine Learning (ML)</p><p style='margin: 0'>This video highlights some common software needed for ML.</p></td></tr></tbody></table><br><h4>6. Final Project</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting up OpenAI Gym</p><p style='margin: 0'>This video explains how to set up OpenAI gym.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Training the Network - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that demonstrates how to build and train the network.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building and Training the Network - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that demonstrates how to build and train the network.</p></td></tr></tbody></table><br><h4>7. Project 1 - Board Game Review Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Dataset - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that explains how to build the dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building the Dataset - Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains how to build the dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>4. Training Models</p><p style='margin: 0'>This video demonstrates how to train models.</p></td></tr></tbody></table><br><h4>8. Project 2 - Credit Card Fraud Detection t</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Credit Card Fraud Detection - Dataset</p><p style='margin: 0'>This video explains how to build a dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>3. Credit Card Fraud Detection - Algorithms</p><p style='margin: 0'>This video demonstrates how to work on algorithms for the project.</p></td></tr></tbody></table><br><h4>9. Project 3 - Getting Started with Natural Language Processing (NLP) in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provided an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tokenizing, Stopwords, and Stemming</p><p style='margin: 0'>This video explains the concepts of tokenizing, stopwords, and stemming.</p></td></tr><tr><td style='padding: 15px;'><p>3. Tagging, Chunking, and Named Entity Recognition</p><p style='margin: 0'>This video explains the concepts of tagging, chunking, and named entity recognition.</p></td></tr><tr><td style='padding: 15px;'><p>4. Text Classification</p><p style='margin: 0'>This video explains the process of text classification.</p></td></tr></tbody></table><br><h4>10. Project 4 - Obtaining Near State-of-the-art Performance on Object Recognition Tasks Using Deep Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading and Preprocessing the Canadian Institute For Advanced Research - 10 (CIFAR-10) Dataset</p><p style='margin: 0'>This video demonstrates how to load and preprocess the CIFAR-10 dataset.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building and Deploying the All-Convolutional Neural Network (CNN) Network - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that demonstrates how to build and deploy the All-CNN network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Building and Deploying the All- Convolutional Neural Network (CNN) Network - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that demonstrates how to build and deploy the All-CNN network.</p></td></tr></tbody></table><br><h4>11. Project 5 - Image Super-resolution with the Super-Resolution Convolution Neural Network (SRCNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Quality Metrics and Preprocessing Images</p><p style='margin: 0'>This video focuses on quality metrics and preprocessing images.</p></td></tr><tr><td style='padding: 15px;'><p>3. Image Super-resolution Using Deep Learning</p><p style='margin: 0'>This video explains how to perform image super-resolution using deep learning.</p></td></tr></tbody></table><br><h4>12. Project 6 - Natural Language Processing (NLP): Text Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Feature Engineering</p><p style='margin: 0'>This video explains the concept of feature engineering in Natural Language Processing.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deploying Scikit-learn (Sklearn) Classifiers</p><p style='margin: 0'>This video demonstrates how to deploy sklearn classifiers.</p></td></tr></tbody></table><br><h4>13. Project 7 - K-means Clustering for Image Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Preprocessing Images for Clustering</p><p style='margin: 0'>This video demonstrates how to preprocess images for clustering.</p></td></tr><tr><td style='padding: 15px;'><p>3. Evaluation and Visualization</p><p style='margin: 0'>This video explains the concept of evaluation and visualization.</p></td></tr></tbody></table><br><h4>14. Project 8 - Data Compression and Visualization Using Principal Component Analysis (PCA)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Elbow Method</p><p style='margin: 0'>This video focuses on the elbow method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA) Compression and Visualization</p><p style='margin: 0'>This video explains how to perform compression and visualization using PCA.</p></td></tr></tbody></table>

TensorFlow

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Projects in Machine Learning: From Beginner to Professional

A course by  Sekhar Metla  IT Industry Expert RequirementsNo programming experience needed. You will learn everything you need to knowNo software is required in advance of the course (all software used in the course is free)No pre-knowledge is required - you will learn from basic Audience  Beginner JavaScript, Python and MSSQL developers curious about data science development  Anyone who wants to generate new income streams  Anyone who wants to build websites  Anyone who wants to become financially independent  Anyone who wants to start their own business or become freelance  Anyone who wants to become a Full stack web developer<h3> Audience</h3><ul><li> <span></span> <span>Beginner JavaScript, Python and MSSQL developers curious about data science development</span></li><li> <span></span> <span>Anyone who wants to generate new income streams</span></li><li> <span></span> <span>Anyone who wants to build websites</span></li><li> <span></span> <span>Anyone who wants to become financially independent</span></li><li> <span></span> <span>Anyone who wants to start their own business or become freelance</span></li><li> <span></span> <span>Anyone who wants to become a Full stack web developer</span></li></ul>

Xpert Learning

Data Engineering

Salesforce Certified JavaScript Developer

Big Data Analytics

Microsoft AZ-303

Learn Data Science with Python, JavaScript, and Microsoft SQL

<h4>Overview</h4>
						<hr />
						<div></div>
							<p>
							</p><p>Data and visual analytics are emerging fields concerned with analysing, modelling, and visualizing complex high-dimensional data. It can be analysed and visualised with many languages like Python, R Programming and more. This course will help to attain the skills and give in-depth knowledge to the participant's enhanced way of modelling, analysing and visualizing techniques. </p>

<p>The course will highlight practical challenges including composite real-world data and will also comprise several practical studies</p>
<p>
							
							</p><div>
						
					</div>

GBA Corporate

Data Analysis and Visualisation

Online

On Site

Online Course Marketing

Algorithmic Trading

Investing & Trading

Making Automated Trading Bot Using Python

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.<br />Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below<br />Getting Started &amp; Optional Python Quick Refresher<br />Statistics and Probability Refresher and Python Practice<br />Probability Density Function; Probability Mass Function; Naive Bayes<br />Predictive Models<br />Machine Learning with Python<br />Recommender Systems<br />KNN and PCA<br />Reinforcement Learning<br />Dealing with Real-World Data<br />Experimental Design / ML in the Real World<br />Time Permitting: Deep Learning and Neural Networks</p><p>Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices.  This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning.  Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then  explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms</p></div><div id="course-pills-2"><h4>Getting Started</h4><ul><li>Installation: Getting Started and Overview</li><li>LINUX jump start: Installing and Using Anaconda &amp; Course Materials (or reference the default container)</li><li>Python Refresher</li><li>Introducing the Pandas, NumPy and Scikit-Learn Library</li></ul><h4>Statistics and Probability Refresher and Python Practice</h4><ul><li>Types of Data</li><li>Mean, Median, Mode</li><li>Using mean, median, and mode in Python</li><li>Variation and Standard Deviation</li></ul><h4>Probability Density Function; Probability Mass Function; Naive Bayes</h4><ul><li>Common Data Distributions</li><li>Percentiles and Moments</li><li>A Crash Course in matplotlib</li><li>Advanced Visualization with Seaborn</li><li>Covariance and Correlation</li><li>Conditional Probability</li><li>Naive Bayes: Concepts</li><li>Bayes? Theorem</li><li>Naive Bayes</li><li>Spam Classifier with Naive Bayes</li></ul><h4>Predictive Models</h4><ul><li>Linear Regression </li><li>Polynomial Regression</li><li>Multiple Regression, and Predicting Car Prices </li><li>Logistic Regression</li><li>Logistic Regression</li></ul><h4>Machine Learning with Python</h4><ul><li>Supervised vs. Unsupervised Learning, and Train/Test</li><li>Using Train/Test to Prevent Overfitting</li><li>Understanding a Confusion Matrix</li><li>Measuring Classifiers (Precision, Recall, F1, AUC, ROC)</li><li>K-Means Clustering</li><li>K-Means: Clustering People Based on Age and Income</li><li>Measuring Entropy</li><li>LINUX: Installing GraphViz</li><li>Decision Trees: Concepts</li><li>Decision Trees: Predicting Hiring Decisions</li><li>Ensemble Learning</li><li>Support Vector Machines (SVM) Overview</li><li>Using SVM to Cluster People using scikit-learn</li></ul><h4>Recommender Systems</h4><ul><li>User-Based Collaborative Filtering</li><li>Item-Based Collaborative Filtering</li><li>Finding Similar Movie</li><li>Better Accuracy for Similar Movies</li><li>Recommending movies to People</li><li>Improving your recommendations</li></ul><h4>KNN and PCA</h4><ul><li>K-Nearest-Neighbors: Concepts</li><li>Using KNN to Predict a Rating for a Movie</li><li>Dimensionality Reduction; Principal Component Analysis (PCA)</li><li>PCA with the Iris Data Set</li></ul><h4>Reinforcement Learning</h4><ul><li>Reinforcement Learning with Q-Learning and Gym</li></ul><h4>Dealing with Real-World Data</h4><ul><li>Bias / Variance Tradeoff</li><li>K-Fold Cross-Validation</li><li>Data Cleaning and Normalization</li><li>Cleaning Web Log Data</li><li>Normalizing Numerical Data</li><li>Detecting Outliers</li><li>Feature Engineering and the Curse of Dimensionality</li><li>Imputation Techniques for Missing Data</li><li>Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE</li><li>Binning, Transforming, Encoding, Scaling, and Shuffling</li></ul><h4>Experimental Design / ML in the Real World</h4><ul><li>Deploying Models to Real-Time Systems</li><li>A/B Testing Concepts</li><li>T-Tests and P-Values</li><li>Hands-on With T-Tests</li><li>Determining How Long to Run an Experiment</li><li>A/B Test Gotchas</li></ul><h4>Capstone Project</h4><ul><li>Group Project &amp; Presentation or Review</li></ul><h4>Deep Learning and Neural Networks</h4><ul><li>Deep Learning Prerequisites</li><li>The History of Artificial Neural Networks</li><li>Deep Learning in the TensorFlow Playground</li><li>Deep Learning Details</li><li>Introducing TensorFlow</li><li>Using TensorFlow</li><li>Introducing Keras</li><li>Using Keras to Predict Political Affiliations</li><li>Convolutional Neural Networks (CNN?s)</li><li>Using CNN?s for Handwriting Recognition</li><li>Recurrent Neural Networks (RNN?s)</li><li>Using an RNN for Sentiment Analysis</li><li>Transfer Learning</li><li>Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters</li><li>Deep Learning Regularization with Dropout and Early Stopping</li><li>The Ethics of Deep Learning</li><li>Learning More about Deep Learning</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Managed Learning Programme (MLP)

Machine Learning (ML)

NLP Practitioner

Learning To Learn

Azure Machine Learning

Machine Learning Essentials with Python (TTML5506-P)

<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>

Microsoft AZ-900

Microsoft AZ-104

Microsoft Azure

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>

Microsoft AI-900

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

"Start your career as a Certified Python Programmer and earn up to £70,000 per Year."
Are you looking to make your mark in the ever-evolving world of Data Analyst? The demand for Python programmers has never been higher. Just in the UK, job postings for Analysts / Python programmers surged by an astounding 70% in 2022. This exponential growth trend signifies the soaring need for skilled Python programmers. Amid this vibrant job market, our Python Programmer online Training is your golden ticket to a prosperous career in programming or Data Analysis. Regardless of your level - beginner or expert - we ensure a comprehensive learning experience that will enhance your current skills and prepare you for the dynamic tech industry.
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Course 06: Introduction to Data Analysis
Course 07: Business and Data Analytics for Beginners
Course 08: Data Analytics with Tableau
Course 09: Quick Data Science Approach from Scratch
Course 10: Learn MySQL from Scratch for Data Science and Analytics
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Course 12: Statistics & Probability for Data Science & Machine Learning
Course 13: Basic Google Data Studio
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Course 15: Python Programming Bible | Networking, GUI, Email, XML, CGI
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 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This Python Certification Course is suitable for:
Beginners with no prior programming experience who are looking to enter the world of coding.
Intermediate programmers who wish to expand their skills and explore Python's extensive usage.
IT professionals seeking to diversify their skill sets and stay relevant in today's tech-driven market.
Data analysts and scientists who want to leverage Python's powerful libraries for their data processing needs.
Students and recent graduates looking to enhance their employability in the thriving tech industry.
Entrepreneurs who wish to understand and utilise Python to automate tasks and analyse data for their businesses.
Anyone with a keen interest in Python programming and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.
 Requirements 
No experience required. Just enrol & start learning python.
 Career path 
Upon completion of our Program, the potential career paths include:
Python Developer: £30,000 - £90,000 annually
Data Scientist: £40,000 - £100,000 annually
Machine Learning Engineer: £50,000 - £110,000 annually
Data Analyst: £35,000 - £85,000 annually
Software Engineer: £35,000 - £95,000 annually
Back-end Developer: £40,000 - £95,000 annually
Web Developer: £30,000 -£85,000 annually
 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

Become an IT professional with our Python Certification - IT Job Ready Program! Gain comprehensive skills in Python programming, data analysis, and automation. We offer a money-back guarantee if you don't secure a job within six months of completion. Enroll now and jumpstart your tech career!


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Python Certification - IT Job Ready Program with Money Back Guarantee

<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>

Programming

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 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

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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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