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			<h2>Certification</h2>		</div>
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			<h2>After successfully completing the course, you will be able to get the UK and internationally accepted certificate to share your achievement with potential employers or include it in your CV. The PDF Certificate + Transcript is available at £6.99 (Special Offer - 50% OFF). In addition, you can get a hard copy of your certificate for £12 (Shipping cost inside the UK is free, and outside the UK is £9.99).</h2>		</div>
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		</section>
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					</div>
		</div>
								</div>
					</div>

Course Cloud

Python Intermediate Coding

General Admission

<p>Becoming proficient in creating dynamic and educational maps with Folium and Python is essential in today's data visualisation and analysis landscape. Our 'Introduction to Maps in Folium and Python' is a world-recognised certification programme endorsed by QLS -The Quality Licence Scheme Awarding Body. This opens the global door for skilled people like you.</p><p>The Introduction to Maps in Folium and Python course empowers students to utilise interactive mapping for various applications by providing a fundamental understanding of geospatial data representation. Geographical insights into data analyses are a valuable tool in information-driven decision-making.</p><p>The course has been carefully designed to accommodate learners at an introductory and intermediate level. After reading through the extensive introduction, participants set out to learn the basics of making their very first Folium map. To reinforce the skills they have learned, the course concludes with sections on sharing and exporting maps, as well as practical experience with project source code and an assignment.</p><p>Explore the possibilities of geospatial data representation by starting this educational journey. In the age of data-driven decision-making, enhance your professional image by using data to speak loudly in the language of maps. Accompany us on this voyage and map your Introduction to Maps in Python and Folium course to become an expert mapper in the ever-changing data science industry!</p><p><strong>Learning Outcome of the '</strong><strong>I</strong><strong>ntroduction to Maps in Folium and Python' Course</strong><strong>:</strong></p><ul><li>Get a firm foundation for using Folium and Python to create an interactive map</li><li>Utilise markers to draw attention to specific locations on maps efficiently to improve the information's presentation and aesthetic appeal</li><li>Learn how to create point and choropleth maps to represent and analyse spatial data in various ways</li><li>Discover boundary measurement methods that will give a thorough grasp of the spatial dimensions of the data</li><li>Gain expertise in layer creation and base map customisation to modify the visual context of maps to meet particular analytical requirements</li><li>Develop expertise by becoming proficient in sophisticated spatial analysis methods</li><li>Understanding the distribution of spatial data, and learning how to make heat maps</li></ul><p><strong>Why learn 'I</strong><strong>ntroduction to Maps in Folium and Python' </strong><strong>with us?</strong></p><p>As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for the '</strong><strong>I</strong><strong>ntroduction to Maps in Folium and Python' Course</strong><strong>:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Teaching Assistant Assignment Help</li><li>QLS Endorsed Teaching Assistant Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Teaching Assistant Study Materials</li><li>14 Days Refund Guarantee</li></ul><p><strong>Course Curriculum of the 'I</strong><strong>ntroduction to Maps in Folium and Python' Course:</strong></p><ul><li>Section 01: Introduction</li><li>Section 02: Creating your first folium map</li><li>Section 03: Markers</li><li>Section 04: Creating a Point Map</li><li>Section 05: Creating a Choropleth Map</li><li>Section 06: Measuring Boundaries</li><li>Section 07: Creating Layers and BaseMaps</li><li>Section 08: Creating a Heatmap</li><li>Section 09: Sharing and Exporting Our Maps</li><li>Section 10: Project Source Code</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Students must finish and turn in a thorough assignment covering all topics in our curriculum. Our expert tutor will review the assignment to ensure it meets Quality Learning Systems (QLS) standards. Once the assessment is successful and passes a quality check, students will be awarded a certificate for completing the Introduction to Maps in Folium and Python Course.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><p><strong>Endorsement:</strong></p><p>QLS approves this certified course as a well-qualified, knowledge-based training programme. A trainer will be available to address your inquiries, including guidance on your pathways to higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        120 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>Whether you're a beginner, an enthusiast, or an experienced professional, this 'Introduction to Maps in Folium and Python' course provides valuable skills for individuals keen on exploring the dynamic world of data visualisation through interactive maps. This is also suitable for the following professionals:</p><ul><li>Data Scientists and Analysts</li><li>GIS Professionals</li><li>Researchers</li><li>Developers</li><li>Students and Academics</li><li>Business Analysts</li><li>Urban Planners and Architects</li><li>Environmental Scientists</li><li>Government Professionals</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No specific requirements exist for enrolling in the Introduction to Maps in Folium and Pythoncourse. Therefore, learners do not need previous qualifications to sign up for this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Get access to the 'Introduction to Maps in Folium and Python' course and explore industry opportunities such as the following:</p><ul><li>Urban Planner. Salary Range: £25,000-£45,000 per year</li><li>Environmental Scientist. Salary Range: £25,000-£45,000 per year</li><li>Web Developer (with GIS skills). Salary Range: £25,000-£50,000 per year</li><li>Government Policy Analyst. Salary Range: £30,000-£50,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate in Maps in Folium and Python at QLS Level 3</h4>
        <p>
            Hard copy certificate -
            £75
        </p>

                        </div>

Kingston Open College

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Introduction to Maps in Folium and Python -  Level 3 (QLS Endorsed)

Packt

Node.js

Windows 10

Cloud Computing

Docker

Python

Linux

Understand how Docker works and learn its core features with this hands-on course. This is 90% practice without any useless demos!

Learn how to create different containers such as Node.js, Python, WordPress, MongoDB, MySQL, Redis, Elasticsearch, and much more.

The Complete Practical Docker Guide

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					<div><p>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 has been specially designed to help learners gain a good command of Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4, providing them with a solid foundation of knowledge to become a qualified professional.</p><p>Through this Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4, you will gain both practical and theoretical understanding of Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</p><p>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
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										<span>Familiar yourself with the recent development and updates of the relevant industry</span>
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										<span>Know how to use your theoretical knowledge to adapt in any working environment</span>
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										<span>Get help from our expert tutors anytime you need</span>
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										<span>Access to course contents that are designed and prepared by industry professionals</span>
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										<span>Study at your convenient time and from wherever you want</span>
									</li>
						</ul>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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											<span>
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										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
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										<span>You will receive a completion certificate upon completing the course.</span>
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										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
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										<span>You will learn the researched and proven approach adopted by successful people to transform their careers.</span>
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										<span>You will be able to incorporate various techniques successfully and understand your customers better.</span>
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			<h2> <b>Requirements</b></h2>		</div>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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										<span>Access to a computer or digital device with internet connectivity.</span>
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		</section><div>
                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"><strong>Section 01: Introduction</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction</td><td><span>🕐 00:16:00</span></td>
				</tr>
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					<td colspan="4"><strong>Section 02: Building a Spatial Database</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Installing PostgreSQL and PostGIS</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating the Application Database</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Section 03: Creating a Django Python Application</strong></td>
				</tr>
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					<td>▶</td>
					<td>Installing Django in a Python Virtual Environment</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Installing the ATOM IDE</td><td><span>🕐 00:09:00</span></td>
				</tr>
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					<td colspan="4"><strong>Section 04: Writing the Django Server-Side Code</strong></td>
				</tr>
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					<td>▶</td>
					<td>Creating the Django Base Project</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Adding the Database Configuration to the settings.py File</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating a Model in the models.py File</td><td><span>🕐 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Section 05: Web Scraping and ETL</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Extracting Data from the Web</td><td><span>🕐 00:24:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Cleaning and Transforming the Data Part 1</td><td><span>🕐 00:18:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Cleaning and Transforming the Data Part 2</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Loading the Data into the Model</td><td><span>🕐 00:12:00</span></td>
				</tr>
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					<td colspan="4"><strong>Section 06: Building the Django Front-End</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Adding the Web Map Tile Service Link in settings.py</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Reading from the Model and Creating a GeoJSON Dataset</td><td><span>🕐 00:12:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Adding Template Files (HTML)</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Adding a Layout and the Base Map</td><td><span>🕐 00:25:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Section 07: Data Visualization</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Plotting Circle Markers</td><td><span>🕐 00:16:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating a Sliding Sidebar</td><td><span>🕐 00:14:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating a Doughnut Chart</td><td><span>🕐 00:19:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating a Multi-Bar Bar Chart</td><td><span>🕐 00:12:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating a KPI</td><td><span>🕐 00:17:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Section 08: Project Source Code</strong></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resource - Web Scraping and Mapping Dam Levels in Python and Leaflet</td><td><span>🕐 00:00:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Assignment</strong></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Web Scraping and Mapping Dam Levels in Python and Leaflet</td><td><span>🕐 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Skill Up

Learning & Development

Online Course Marketing

Online Course Creation

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

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

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Power Automate

Programming

ChatGPT

This course will teach you how to link ChatGPT's API directly into your applications and solutions. Learn to integrate the API into Power Apps. Build any business application you want using Power Apps, which can now incorporate ChatGPT. Extend ChatGPT to any platform, including React, Webflow, Zapier, Excel, and so on.

Make Apps with ChatGPT and Generative AI

<strong>This video course gives you an insight into applied data science concepts using Python. With the help of interesting activities and hands-on coding exercises, you'll learn about data science, extended data analysis, linear and logistic regression, data visualization, k-means clustering, and decision trees.</strong>
        <p>There is an ever-increasing demand for storing different types of data in organizations. Be it a small or big organization, employers want to keep records of their employees, customers, accounts, feedback, and so on. However, maintaining such an enormous collection of data is not possible without the help of data science. This video course will help you to learn the important concepts and theories of applied data science that you need to know to store, manipulate, and visualize huge chunks of data.

The course starts with an introduction to applied data science and a tutorial on how to set up a Jupyter notebook. You'll then go on to understand linear regression using Boston data. As you advance, you'll discover data visualization techniques and explore time series and data evaluation. You'll also get to grips with extended data analysis with the help of a temperature analysis activity. Toward the end, you'll be introduced to k-means clustering and gain a solid understanding of decision trees.

By the end of this course, you'll be well-versed with applied data science concepts and be able to apply your skills in the real world.

All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Projects-in-Data-Science</p>
        <h2>What You Will Learn</h2><p>Gain a solid understanding of applied data science<br>Get to grips with extended data analysis using an example data set<br>Find out how to perform linear and logistic regression<br>Become familiar with scattered and advanced scattered plot<br>Develop skills to plot time-series data<br>Explore k-means clustering and define centroids for clustering</p>
        <h2>Audience</h2><p>If you are a data science enthusiast who wants to develop your skills to effectively maintain your organization's data, then this video course is for you.</p>
        <h2>Approach</h2><p>Using detailed explanations, coding exercises, and activities, this video course helps you to get up and running with applied data science concepts.</p>
        <h2>Key Features</h2><p>Understand applied data science concepts with the help of engaging activities * Get to grips with data visualization, plotting, and clustering techniques * Discover how to make the best use of decision trees</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Projects-in-Data-Science</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</h4><p></p><br><h4>2. Extended Data Analysis</h4><p></p><br><h4>3. Regression</h4><p></p><br><h4>4. Data Visualization</h4><p></p><br><h4>5. Activity</h4><p></p><br><h4>6. Time Series</h4><p></p><br><h4>7. K-Means Clustering</h4><p></p><br><h4>8. Decision Tree</h4><p></p>

Data Science

Data Visualization

Data Analysis

This video course gives you an insight into applied data science concepts using Python. With the help of interesting activities and hands-on coding exercises, you'll learn about data science, extended data analysis, linear and logistic regression, data visualization, k-means clustering, and decision trees.

Projects in Data Science

<div>
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		</div>
				</div>
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				<div>
			<h4>Overview</h4>		</div>
				</div>
				</div>
		<div>
				<div>
				<div>
							<p>Are you ready to enhance your career and job prospects in the exciting field of Machine Learning? Our 'Machine Learning with Python Course' is your gateway to mastering this transformative technology. Dive into the world of Machine Learning and gain valuable insights into its foundational concepts through a structured curriculum. From understanding the basics of Machine Learning to harnessing the power of Numpy and Matplotlib libraries, this course equips you with the knowledge you need to excel in this dynamic field. Explore the intricacies of Polynomial Regression and unlock the potential of data-driven decision-making. With Machine Learning at the forefront, elevate your career and stay ahead in the competitive job market.</p><h5><strong>Learning Outcomes:</strong></h5><p>By the end of this course, you will:</p><ul><li>Gain a deep understanding of Machine Learning principles and techniques.</li><li>Master the Numpy library for efficient data manipulation and analysis.</li><li>Create insightful data visualizations using Matplotlib.</li><li>Develop expertise in Polynomial Regression for predictive modeling.</li><li>Apply Machine Learning concepts to real-world problems.</li><li>Be prepared to pursue advanced Machine Learning specializations.</li></ul>						</div>
				</div>
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				</div>
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				<div>
			<h4>Description</h4>		</div>
				</div>
				</div>
		<div>
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				<div>
							<p>Our 'Machine Learning with Python Course' offers a comprehensive journey into the realm of Machine Learning. With a focus on theoretical foundations, this course provides you with a strong knowledge base to tackle complex problems in data analysis and prediction. You'll delve into essential topics such as Numpy and Matplotlib, gaining the skills necessary for data manipulation and visualization. The course culminates in an exploration of Polynomial Regression, a vital tool in predictive modeling. Whether you're a beginner or looking to reinforce your understanding of Machine Learning, this course is designed to empower you with the knowledge you need.</p>						</div>
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			<h4>Why Choose Us?</h4>		</div>
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										<span>This course is accredited by the CPD Quality Standards.</span>
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										<span>Lifetime access to the whole collection of the learning materials.</span>
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										<span>Online test with immediate results.</span>
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										<span>Enroling in the course has no additional cost.</span>
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										<span>You can study and complete the course at your own pace.</span>
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										<span>Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.</span>
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			<h4>Certificate of Achievement</h4>		</div>
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							<p>Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.</p>						</div>
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			<h4>Who Is This Course For?</h4>		</div>
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										<span>Aspiring data scientists and analysts.</span>
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										<span>Professionals seeking to transition into Machine Learning roles.</span>
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										<span>Students and enthusiasts passionate about data-driven solutions.</span>
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										<span>Anyone interested in understanding the foundations of Machine Learning.</span>
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			<h4>Requirements</h4>		</div>
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							<p><span>The Machine Learning with Python Course course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Machine Learning with Python Course, without any fuss.</span></p>						</div>
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			<h4>Career Path</h4>		</div>
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										<span>Machine Learning Engineer</span>
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										<span>Data Scientist</span>
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										<span>AI Researcher</span>
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										<span>Data Analyst</span>
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										<span>Business Intelligence Analyst</span>
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										<span>Software Developer</span>
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										<span>Statistician</span>
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			<h4>Order Your Certificate</h4>		</div>
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							<div><div><div><div id="1693981461.590199"><div><div><div><div><div><div><div><div><div><div><div>To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>						</div>
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				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table>				<tbody><tr>
					<td colspan="4">Section 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Course</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>What is Machine Learning</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Life Cycle</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
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								<tr>
					<td colspan="4">Section 02: Numpy Library</td>
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					<td><i></i></td>
					<td>Introduction to Numpy Library</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Arrays from Scratch</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Arrays from Scratch Continued</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Array Indexing and Slicing</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>Numpy Array Functions and Shape Modification</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>Mathematical Operations on Numpy Arrays</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td><i></i></td>
					<td>Introduction to Pandas Library</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>Working with Pandas DataFrames</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td><i></i></td>
					<td>Slicing and Indexing with Pandas</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td>Create DataFrame and Explore Dataset</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
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					<td><i></i></td>
					<td>Data Analysis with Pandas DataFrame</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td><i></i></td>
					<td>Other Useful Methods in Pandas Library</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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								<tr>
					<td colspan="4">Section 03: Matplotlib</td>
				</tr>
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					<td><i></i></td>
					<td>Introduction to Matplotlib</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>Customizing Line Plots</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Create Plot Using DataFrame</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Standard Scaler to Scale the Data</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Encoding Categorical Data</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sklearn Pipeline and Column Transformer</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluation Metrics in Sklearn</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Linear Regression</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Evaluation of Linear Regression Model</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: Polynomial Regression</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Polynomial Regression</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Polynomial Regression Continued</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sklearn Pipeline Polynomial Regression</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Decision Tree Classifier</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Decision Tree Evaluation</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Random Forest</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Support Vector Machines</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>K-means Clustering</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>KMeans Clustering - Hands On</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td><i></i></td>
					<td>Data Loading and Analysis</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>Dimensionality Reduction with PCA</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>Hyper Parameter Tuning</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Summary</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Order Your Certificate</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order Your Certificate</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

NextGen Learning

Machine Learning with Python Course

<strong>This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.</strong>
        <p>Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.

The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for products recommendation, prepare data, and model development using a two-tower approach.

You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system.

Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems.

All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning</p>
        <h2>What You Will Learn</h2><p>Learn about deep learning and recommender systems<br>Explore the mechanisms of deep learning-based approaches<br>Learn to implement a two-tower model for recommenders<br>Implement TensorFlow to develop a recommender system<br>Learn basic neural network models for recommendations<br>Explore neural collaborative filtering and variational autoencoders</p>
        <h2>Audience</h2><p>This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. Individuals passionate about recommender systems with the help of TensorFlow Recommenders will benefit from this course. Deep learning practitioners, research scholars, and data scientists will also benefit from the course. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.</p>
        <h2>Approach</h2><p>The course is well-structured and explanatory. Every module has engaging content covering necessary theoretical concepts with practical explanations. The lectures are divided into many videos and comprehensively detailed code notebooks, providing a unique hands-on experience using a real-time project. This course is easily understandable, expressive, and self-explanatory, with live coding.</p>
        <h2>Key Features</h2><p>Understand, implement, and evaluate deep learning models for building real-world recommendation systems * Validate, test, and make predictions using recommender systems with the help of TensorFlow Recommenders * Explore the benefits and challenges of deep learning in recommender systems</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning</p>
        
        <h2>Course Outline</h2><h4>1. Introduction</h4><p>This section focuses on introducing the author and presenting a generalized overview of the course content.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. About the Instructor</p><p style='margin: 0'>In this video, we will get a brief outline of the author, AI Sciences, and how they deliver their courses.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Outline</p><p style='margin: 0'>This brief introductory video will cover the course contents and the concepts you will learn in this course.</p></td></tr></tbody></table><br><h4>2. Deep Learning Foundation for Recommender Systems</h4><p>This section focuses on deep learning methodologies used in recommender systems. Here, we will cover inferences, embeddings, and neutral collaborative filtering aside from VAE and deep learning models.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>In this video, we will look at a general introduction to deep learning and what is covered in this section under deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Overview</p><p style='margin: 0'>This video briefly overviews the contents and topics covered in this module, including deep learning in recommender systems, the pros and cons of deep learning, recommender interference, and more.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deep Learning in Recommender Systems</p><p style='margin: 0'>In this video, we will explore deep learning concepts in recommender systems, migrating from machine learning to deep learning, and capturing non-linear to non-trivial relationships.</p></td></tr><tr><td style='padding: 15px;'><p>4. Inference after Training</p><p style='margin: 0'>In this lecture, you will learn how to deploy the model as a service to infer the likelihood of new interactions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Inference Mechanism</p><p style='margin: 0'>In this lesson, we will understand the generic recommendation systems interference mechanism.</p></td></tr><tr><td style='padding: 15px;'><p>6. Embeddings and User Context</p><p style='margin: 0'>This video focuses on collaborative filtering with the help of deep learning and neural collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>7. Neural Collaborative Filtering</p><p style='margin: 0'>In this video, we will understand neural collaborative filtering and how to incorporate it into our recommender system.</p></td></tr><tr><td style='padding: 15px;'><p>8. VAE Collaborative Filtering</p><p style='margin: 0'>This lesson explores the variational autoencoder for collaborative filtering, using representations in hidden layers.</p></td></tr><tr><td style='padding: 15px;'><p>9. Strengths and Weaknesses of DL Models</p><p style='margin: 0'>In this lesson, you will learn about the strengths and weaknesses of deep learning recommender system models.</p></td></tr><tr><td style='padding: 15px;'><p>10. Deep Learning Quiz</p><p style='margin: 0'>This quiz is based on the deep learning concepts learned so far in this module.</p></td></tr><tr><td style='padding: 15px;'><p>11. Deep Learning Quiz Solution</p><p style='margin: 0'>This is the solution to the quiz based on the deep learning concepts learned so far in this module.</p></td></tr></tbody></table><br><h4>3. Project Amazon Product Recommendation System</h4><p>This module focuses on TensorFlow recommenders, creating a two-tower model, data visualization with WordCloud, creating tensors with DataFrame, query towers, random train test split, and computer loss.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Overview</p><p style='margin: 0'>This is a brief introductory overview of what is being covered in this section, including creating projects, TensorFlow recommenders, libraries, WordCloud, and DataFrame.</p></td></tr><tr><td style='padding: 15px;'><p>2. TensorFlow Recommenders</p><p style='margin: 0'>In this video, you will learn about TensorFlow recommenders used to develop recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. Two-Tower Model</p><p style='margin: 0'>In this lesson, we will discuss the two-tower model, which uses user embedding and item embedding in the recommender system.</p></td></tr><tr><td style='padding: 15px;'><p>4. Project Overview</p><p style='margin: 0'>This video outlines the project and will demonstrate how to install the packages required for the project, data preparation, using TensorFlow, evaluation, and recommendation.</p></td></tr><tr><td style='padding: 15px;'><p>5. Download Libraries</p><p style='margin: 0'>In this video, you will learn to implement the Amazon recommender system using the TensorFlow recommender based on specific user behavior. You will learn to download and import particular libraries for the project.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Visualization with WordCloud</p><p style='margin: 0'>In this video, you will learn how to load a dataset for the project being developed, using WordCloud, importing WordCloud, STOPWORDS, and ImageColorGenerator.</p></td></tr><tr><td style='padding: 15px;'><p>7. Make Tensors from DataFrame</p><p style='margin: 0'>In this video, we will continue to check our dataset using a single username. We will use a part of the DataFrame to do so.</p></td></tr><tr><td style='padding: 15px;'><p>8. Rating Our Data</p><p style='margin: 0'>In this video, you will learn about the next part of the project, which is rating our data.</p></td></tr><tr><td style='padding: 15px;'><p>9. Random Train-Test Split</p><p style='margin: 0'>In this lesson, you will learn to perform the test train split, which will do the training split and then create a prediction; we will use a random dataset (80-20 ratio).</p></td></tr><tr><td style='padding: 15px;'><p>10. Making the Model and Query Tower</p><p style='margin: 0'>In this lecture, you will learn to develop our model and create a query tower to perform retrieval tasks.</p></td></tr><tr><td style='padding: 15px;'><p>11. Candidate Tower and Retrieval System</p><p style='margin: 0'>In this video, you will learn how to create a candidate tower and develop a retrieval system.</p></td></tr><tr><td style='padding: 15px;'><p>12. Compute Loss</p><p style='margin: 0'>Here, we will look at the next step of training our model: the compute loss function.</p></td></tr><tr><td style='padding: 15px;'><p>13. Train and Validation</p><p style='margin: 0'>After entirely developing our model, you will learn to fit and evaluate the model for functionality.</p></td></tr><tr><td style='padding: 15px;'><p>14. Accuracy Versus Recommendations</p><p style='margin: 0'>In this video, we will perform data visualization with the project we created and check the model's accuracy.</p></td></tr><tr><td style='padding: 15px;'><p>15. Making Recommendations</p><p style='margin: 0'>In this lesson, you will learn to create a recommendation and use a brute-force algorithm to generate the recommendation.</p></td></tr></tbody></table>

TensorFlow

This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.

Recommender Systems: An Applied Approach using Deep Learning

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming<br />Also familiar with Python</p>Overview<p>? NumPy, pandas, Matplotlib,  scikit-learn<br />? Python REPLs<br />? Jupyter Notebooks<br />? Data analytics life-cycle phases<br />? Data repairing and normalizing<br />? Data aggregation and grouping<br />? Data visualization<br />? Data science algorithms for supervised and unsupervised machine learning</p><p>Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.</p></div><div id="course-pills-2"><h4>Python for Data Science</h4><ul><li>? Using Modules</li><li>? Listing Methods in a Module</li><li>? Creating Your Own Modules</li><li>? List Comprehension</li><li>? Dictionary Comprehension</li><li>? String Comprehension</li><li>? Python 2 vs Python 3</li><li>? Sets (Python 3+)</li><li>? Python Idioms</li><li>? Python Data Science ?Ecosystem?</li><li>? NumPy</li><li>? NumPy Arrays</li><li>? NumPy Idioms</li><li>? pandas</li><li>? Data Wrangling with pandas' DataFrame</li><li>? SciPy</li><li>? Scikit-learn</li><li>? SciPy or scikit-learn?</li><li>? Matplotlib</li><li>? Python vs R</li><li>? Python on Apache Spark</li><li>? Python Dev Tools and REPLs</li><li>? Anaconda</li><li>? IPython</li><li>? Visual Studio Code</li><li>? Jupyter</li><li>? Jupyter Basic Commands</li><li>? Summary</li></ul><h4>Applied Data Science</h4><ul><li>? What is Data Science?</li><li>? Data Science Ecosystem</li><li>? Data Mining vs. Data Science</li><li>? Business Analytics vs. Data Science</li><li>? Data Science, Machine Learning, AI?</li><li>? Who is a Data Scientist?</li><li>? Data Science Skill Sets Venn Diagram</li><li>? Data Scientists at Work</li><li>? Examples of Data Science Projects</li><li>? An Example of a Data Product</li><li>? Applied Data Science at Google</li><li>? Data Science Gotchas</li><li>? Summary</li></ul><h4>Data Analytics Life-cycle Phases</h4><ul><li>? Big Data Analytics Pipeline</li><li>? Data Discovery Phase</li><li>? Data Harvesting Phase</li><li>? Data Priming Phase</li><li>? Data Logistics and Data Governance</li><li>? Exploratory Data Analysis</li><li>? Model Planning Phase</li><li>? Model Building Phase</li><li>? Communicating the Results</li><li>? Production Roll-out</li><li>? Summary</li></ul><h4>Repairing and Normalizing Data</h4><ul><li>? Repairing and Normalizing Data</li><li>? Dealing with the Missing Data</li><li>? Sample Data Set</li><li>? Getting Info on Null Data</li><li>? Dropping a Column</li><li>? Interpolating Missing Data in pandas</li><li>? Replacing the Missing Values with the Mean Value</li><li>? Scaling (Normalizing) the Data</li><li>? Data Preprocessing with scikit-learn</li><li>? Scaling with the scale() Function</li><li>? The MinMaxScaler Object</li><li>? Summary</li></ul><h4>Descriptive Statistics Computing Features in Python</h4><ul><li>? Descriptive Statistics</li><li>? Non-uniformity of a Probability Distribution</li><li>? Using NumPy for Calculating Descriptive Statistics Measures</li><li>? Finding Min and Max in NumPy</li><li>? Using pandas for Calculating Descriptive Statistics Measures</li><li>? Correlation</li><li>? Regression and Correlation</li><li>? Covariance</li><li>? Getting Pairwise Correlation and Covariance Measures</li><li>? Finding Min and Max in pandas DataFrame</li><li>? Summary</li></ul><h4>Data Aggregation and Grouping</h4><ul><li>? Data Aggregation and Grouping</li><li>? Sample Data Set</li><li>? The pandas.core.groupby.SeriesGroupBy Object</li><li>? Grouping by Two or More Columns</li><li>? Emulating the SQL's WHERE Clause</li><li>? The Pivot Tables</li><li>? Cross-Tabulation</li><li>? Summary</li></ul><h4>Data Visualization with matplotlib</h4><ul><li>? Data Visualization<br />? What is matplotlib?<br />? Getting Started with matplotlib<br />? The Plotting Window<br />? The Figure Options<br />? The matplotlib.pyplot.plot() Function<br />? The matplotlib.pyplot.bar() Function<br />? The matplotlib.pyplot.pie () Function<br />? Subplots<br />? Using the matplotlib.gridspec.GridSpec Object<br />? The matplotlib.pyplot.subplot() Function<br />? Hands-on Exercise<br />? Figures<br />? Saving Figures to File<br />? Visualization with pandas<br />? Working with matplotlib in Jupyter Notebooks<br />? Summary</li></ul><h4>Data Science and ML Algorithms in scikit-learn</h4><ul><li>? Data Science, Machine Learning, AI?</li><li>? Types of Machine Learning</li><li>? Terminology: Features and Observations</li><li>? Continuous and Categorical Features (Variables)</li><li>? Terminology: Axis</li><li>? The scikit-learn Package</li><li>? scikit-learn Estimators</li><li>? Models, Estimators, and Predictors</li><li>? Common Distance Metrics</li><li>? The Euclidean Metric</li><li>? The LIBSVM format</li><li>?  Scaling of the Features</li><li>? The Curse of Dimensionality</li><li>? Supervised vs Unsupervised Machine Learning</li><li>? Supervised Machine Learning Algorithms</li><li>? Unsupervised Machine Learning Algorithms</li><li>? Choose the Right Algorithm</li><li>? Life-cycles of Machine Learning Development</li><li>? Data Split for Training and Test Data Sets</li><li>? Data Splitting in scikit-learn</li><li>? Hands-on Exercise</li><li>? Classification Examples</li><li>? Classifying with k-Nearest Neighbors (SL)</li><li>? k-Nearest Neighbors Algorithm</li><li>? k-Nearest Neighbors Algorithm</li><li>? The Error Rate</li><li>? Hands-on Exercise</li><li>? Dimensionality Reduction</li><li>? The Advantages of Dimensionality Reduction</li><li>? Principal component analysis (PCA)</li><li>? Hands-on Exercise</li><li>? Data Blending</li><li>? Decision Trees (SL)</li><li>? Decision Tree Terminology</li><li>? Decision Tree Classification in Context of Information Theory</li><li>? Information Entropy Defined</li><li>? The Shannon Entropy Formula</li><li>? The Simplified Decision Tree Algorithm</li><li>? Using Decision Trees</li><li>? Random Forests</li><li>? SVM</li><li>? Naive Bayes Classifier (SL)</li><li>? Naive Bayesian Probabilistic Model in a Nutshell</li><li>? Bayes Formula</li><li>? Classification of Documents with Naive Bayes</li><li>? Unsupervised Learning Type: Clustering</li><li>? Clustering Examples</li><li>? k-Means Clustering (UL)</li><li>? k-Means Clustering in a Nutshell</li><li>? k-Means Characteristics</li><li>? Regression Analysis</li><li>? Simple Linear Regression Model</li><li>? Linear vs Non-Linear Regression</li><li>? Linear Regression Illustration</li><li>? Major Underlying Assumptions for Regression Analysis</li><li>? Least-Squares Method (LSM)</li><li>? Locally Weighted Linear Regression</li><li>? Regression Models in Excel</li><li>? Multiple Regression Analysis</li><li>? Logistic Regression</li><li>? Regression vs Classification</li><li>? Time-Series Analysis</li><li>? Decomposing Time-Series</li><li>? Summary</li></ul><h4>Lab Exercises</h4><ul><li>Lab 1 - Learning the Lab Environment     </li><li>Lab 2 -  Using Jupyter Notebook </li><li>Lab 3 -  Repairing and Normalizing Data  </li><li>Lab 4 -  Computing Descriptive Statistics </li><li>Lab 5 - Data Grouping and Aggregation   </li><li>Lab 6 - Data Visualization with matplotlib              </li><li>Lab 7 - Data Splitting       </li><li>Lab 8 -  k-Nearest Neighbors Algorithm  </li><li>Lab 9 -  The k-means Algorithm  </li><li>Lab 10 -  The Random Forest Algorithm </li></ul></div>

Nexus Human

Data Engineering

Machine Learning (ML)

Big Data Analytics

Data Mining

Data Skills

Python With Data Science

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.</p>Overview<p>By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.</p><p>In this course you will start with the absolute basics of Python, focusing mainly on data structures.  Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries.  You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables.  To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets.</p></div><div id="course-pills-2"><h4>Introduction to Data Structure using Python</h4><ul><li>Python for Data Wrangling</li><li>Lists, Sets, Strings, Tuples, and Dictionaries</li></ul><h4>Advanced Operations on Built-In Data Structure</h4><ul><li>Advanced Data Structures</li><li>Basic File Operations in Python</li></ul><h4>Introduction to NumPy, Pandas, and Matplotlib</h4><ul><li>NumPy Arrays</li><li>Pandas DataFrames</li><li>Statistics and Visualization with NumPy and Pandas</li><li>Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame</li></ul><h4>Deep Dive into Data Wrangling with Python</h4><ul><li>Subsetting, Filtering, and Grouping</li><li>Detecting Outliers and Handling Missing Values</li><li>Concatenating, Merging, and Joining</li><li>Useful Methods of Pandas</li></ul><h4>Get Comfortable with a Different Kind of Data Sources</h4><ul><li>Reading Data from Different Text-Based (and Non-Text-Based) Sources</li><li>Introduction to BeautifulSoup4 and Web Page Parsing</li></ul><h4>Learning the Hidden Secrets of Data Wrangling</h4><ul><li>Advanced List Comprehension and the zip Function</li><li>Data Formatting</li></ul><h4>Advanced Web Scraping and Data Gathering</h4><ul><li>Basics of Web Scraping and BeautifulSoup libraries</li><li>Reading Data from XML</li></ul><h4>RDBMS and SQL</h4><ul><li>Refresher of RDBMS and SQL</li><li>Using an RDBMS (MySQL/PostgreSQL/SQLite)</li></ul><h4>Application in real life and Conclusion of course</h4><ul><li>Applying Your Knowledge to a Real-life Data Wrangling Task</li><li>An Extension to Data Wrangling</li></ul></div>

Data Analyst

Data Processing

Data Wrangling with Python

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

"Start your career as a Certified Python Programmer and earn up to £70,000 per Year."
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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

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

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

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

Python for Beginners: Learn Python Programming (Python 3)

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Are you looking to secure a career in Python programming or software engineering?</span></p><p><span>The</span><b> Python Programming</b><span> course will take you from beginner to expert in this programming language in no time, with step-by-step instructor-led guidance. Through audiovisual training, you will develop your data analysis, programming and coding skills, learning how to perform a wide range of tasks. It will teach you how to get set up on Mac OS X, Linux, and Ubuntu, best practices for string formatting, handling exceptions, and much more.</span></p><p><span>This course is open to anyone who wants to learn Python programming from the basics to an advanced level. Enrol as a part-time or full-time student today and gain the skills to become a Python programming pro.</span></p></div>
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					<div><p>This best selling <strong>Python Programming </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Python Programming </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Python Programming </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Python Programming </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Python Programming</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Python Programming </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Python Programming </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Python Programming

<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.

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					<div><p>Basic Python 3.9  has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the course today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Basic Python 3.9  has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth course is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Basic Python 3.9  is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. This course is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Basic Python 3.9 , you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Basic Python 3.9  will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Basic Python 3.9  to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.</p></div>
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Basic Python 3.9

<p><strong>Start your career as a Data Scientist and earn upto £90,000/ Year</strong></p><p>Are you ready to unlock the power of Python and dive into the exciting world of data science? Look no further because we have the ultimate solution for you: Introducing our Data Science with Python Course with Career Support and Job Ready Program!</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 <strong>Data 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 <strong>Data Scientist</strong> experience, we will guide you towards honing your skills and developing a strong foundation in <strong>Data Science</strong>.<br /><br /></li><li><strong>Tailored CV Building:</strong> Crafting an impressive CV is crucial in today's competitive job market. Our team of experts will work closely with you to create a professional CV that showcases your skills and highlights your potential as a Data Scientist. We'll ensure that your CV stands out among the rest, increasing your chances of landing that dream job.<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 <strong>Data Science</strong> 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. <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. Whether you need advice on career advancement, assistance with new projects, or simply a friendly ear to share your achievements, we'll be your trusted partner for long-term success.</li></ul><p><strong>Here are the courses we will provide once you enrol in the program:</strong></p><ul><li>Course 01: Ultimate Python Training for Beginners</li><li>Course 02: Python Intermediate Training</li><li>Course 03: Python Advanced Training</li><li>Course 04: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 05: Basic Data Analysis</li><li>Course 06: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li>Course 07: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li>Course 08: Customer Analytics Training</li><li>Course 09: Google Analytics for Everyone</li><li>Course 10: Google Data Studio: Data Analytics</li><li>Course 11: Big Data Analytics with PySpark Tableau Desktop and MongoDB</li><li>Course 12: Big Data Analytics with PySpark Power BI and MongoDB</li><li>Course 13: Excel Data Analysis</li><li>Course 14: Introduction to Data Analytics with Tableau</li><li>Course 15: Machine Learning with Python Course</li><li>Course 16: Python Programming Bible | Networking, GUI, Email, XML, CGI</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:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the <strong>Data Scientist </strong>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 <strong>Data Scientist</strong>. 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.</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 <strong>Data Scientist</strong> 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 Science, 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 <strong>Data Science</strong> 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>Aspiring data scientists looking to enter the exciting field of data science.</li><li>Professionals from other disciplines who want to transition into the data science domain.</li><li>Software engineers or programmers interested in expanding their skill set to include data science.</li><li>Analysts or researchers who want to enhance their data analysis and modeling capabilities.</li><li>Students or graduates seeking to kick-start their careers in data science with a strong foundation.</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 Analyst: £25,000 - £55,000 per year</li><li>Data Scientist: £40,000 - £85,000 per year</li><li>Machine Learning Engineer: £50,000 - £100,000 per year</li><li>Data Engineer: £45,000 - £80,000 per year</li><li>Business Intelligence Analyst: £35,000 - £65,000 per year</li><li>Data Science Manager: £70,000 - £120,000 per year.</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 Scientist and earn upto £90,000/ Year
Are you ready to unlock the power of Python and dive into the exciting world of data science? Look no further because we have the ultimate solution for you: Introducing our Data Science with Python Course with Career Support and Job Ready Program!
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 Data Scientist experience, we will guide you towards honing your skills and developing a strong foundation in Data Science.
Tailored CV Building: Crafting an impressive CV is crucial in today's competitive job market. Our team of experts will work closely with you to create a professional CV that showcases your skills and highlights your potential as a Data Scientist. We'll ensure that your CV stands out among the rest, increasing your chances of landing that dream job.
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.
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. Whether you need advice on career advancement, assistance with new projects, or simply a friendly ear to share your achievements, we'll be your trusted partner for long-term success.
Here are the courses we will provide once you enrol in the program:
Course 01: Ultimate Python Training for Beginners
Course 02: Python Intermediate Training
Course 03: Python Advanced Training
Course 04: SQL for Data Science, Data Analytics and Data Visualization
Course 05: Basic Data Analysis
Course 06: Develop Big Data Pipelines with R & Sparklyr & Tableau
Course 07: Develop Big Data Pipelines with R, Sparklyr & Power BI
Course 08: Customer Analytics Training
Course 09: Google Analytics for Everyone
Course 10: Google Data Studio: Data Analytics
Course 11: Big Data Analytics with PySpark Tableau Desktop and MongoDB
Course 12: Big Data Analytics with PySpark Power BI and MongoDB
Course 13: Excel Data Analysis
Course 14: Introduction to Data Analytics with Tableau
Course 15: Machine Learning with Python Course
Course 16: Python Programming Bible | Networking, GUI, Email, XML, CGI
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 Scientist 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 Scientist. 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 Scientist 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, 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 begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Aspiring data scientists looking to enter the exciting field of data science.
Professionals from other disciplines who want to transition into the data science domain.
Software engineers or programmers interested in expanding their skill set to include data science.
Analysts or researchers who want to enhance their data analysis and modeling capabilities.
Students or graduates seeking to kick-start their careers in data science with a strong foundation.
 Requirements 
No experience required. Just enrol & start learning.
 Career path 
Data Analyst: £25,000 - £55,000 per year
Data Scientist: £40,000 - £85,000 per year
Machine Learning Engineer: £50,000 - £100,000 per year
Data Engineer: £45,000 - £80,000 per year
Business Intelligence Analyst: £35,000 - £65,000 per year
Data Science Manager: £70,000 - £120,000 per year.
 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 Scientist with Python - IT Job Ready Program + Career Support & Money Back Guarantee

<p><strong>Introducing Coding using Python</strong></p>
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