Name: The Complete Guide for SQL with Microsoft SQL Server
Price: 82.99 GBP
Availability: InStock

NextGen Learning

Learn Ethical Hacking From A-Z: Beginner To Expert Course

General Admission

<p><strong>Level 3 Data Entry, Data Analytics, Document Control &amp; Virtual Assistant Diploma</strong></p><p><br />Learn everything there is to discover an array of limitless opportunities in our Data Entry, Data Analytics, Document Control &amp; Virtual Assistant Diploma Course. Explore the complexities of handling, evaluating, and managing data as you set out to become a professional.</p><p><strong>4-in-1 Bundle Includes: </strong></p><ul><li><strong>Course 01: </strong>Data Entry</li><li><strong>Course 02: </strong>Data Analytics</li><li><strong>Course 03: </strong>Document Control</li><li><strong>Course 04: </strong>Virtual Assistant</li></ul><p>Become proficient in doing Data Entry in Microsoft Word and in Microsoft Excel, understand Advanced Data Entry Techniques which will help with your Data Entry Career and Portfolio.</p><p>Go through the statistics of data analytics, learning the storing and gathering of data along with the tools to do so. Learn document control strategies next to document control lifecycle and the management as well.</p><p>Gain an understanding of the role and responsibilities along with the knowledge and skills required to become a Virtual Assistant. These 4 courses are your perfect gateway to your ultimate success in the field of Data Entry Administrator, a Virtual Assistant, Document Control Analyst or a Data Analytics Manager.</p><p>So don't miss out on this opportunity to upskill your career to the next level and in doing so without making a dent in your wallet as you will save up to 90% off.</p><p><strong>Key Features</strong></p><ul><li>Instant e-certificate</li><li>Fully online, interactive course with audio voiceover</li><li>Developed by qualified professionals in the field</li><li>Self-paced learning and laptop, tablet, smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Course Curriculum</strong></p><p><strong>Efficient Data Entry</strong></p><ul><li>Module 01: Introduction to Data Entry and Essential Tools</li><li>Module 02: Data Entry in Microsoft Word</li><li>Module 03: Data Entry in Microsoft Excel</li><li>Module 04: Advanced Data Entry Techniques and Tools</li><li>Module 05: Data Entry Best Practices and Accuracy</li><li>Module 06: Building Your Data Entry Career and Portfolio</li></ul><p><strong>Data Analytics</strong></p><ul><li>Module 01: Introduction to the World of Data</li><li>Module 02: Basics of Data Analytics</li><li>Module 03: Statistics for Data Analytics</li><li>Module 04: Actions Taken in the Data Analysis Process</li><li>Module 05: Gathering the Right Information</li><li>Module 06: Storing Data</li><li>Module 07: Data Mining</li><li>Module 08: Excel for Data Analytics</li><li>Module 09: Tools for Data Analytics</li><li>Module 10: Data-Analytic Thinking</li><li>Module 11: Data Visualisation That Clearly Describes Insights</li><li>Module 12: Data Visualisation Tools</li></ul><p><strong>Document Control</strong></p><ul><li>Module 01: Introduction to Document Control</li><li>Module 02: Principles of Document Control and Elements of Document Control Environment</li><li>Module 03: Document Control Lifecycle</li><li>Module 04: Document Control Strategies and Instruments</li><li>Module 05: Document Management</li><li>Module 06: Quality Assurance and Controlling Quality of Documents</li><li>Module 07: Project Document Control</li><li>Module 08: Electronic Document Management Systems and Soft Copy Documentation</li></ul><p><strong>Virtual Assistant</strong></p><ul><li>Module 01: Introduction to Virtual Assistant</li><li>Module 02: Different Types of Virtual Assistants</li><li>Module 03: Be a Virtual Assistant</li><li>Module 04: Must-Have Skills for Virtual Assistants</li><li>Module 05: Essential Tools for a Virtual Assistant</li><li>Module 06: Finding Your Niche</li><li>Module 07: Get Hired for VA Jobs</li><li>Module 08: How to Set VA Business</li><li>Module 09: Admin Support- a Vital Part of VA</li><li>Module 10: How to Manage Clients</li><li>Module 11: Working with a Virtual Team</li><li>Module 12: Data Security Measures</li></ul><p><strong>Accreditation</strong></p><p>These courses are fully CPD QS accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99).</p><p><strong>Assessment</strong></p><p>At the end of the course, there will be an online assessment, which you will need to pass to complete the course.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone looking to improve their Data Entry, Document Control &amp; Virtual Assistant skills.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Virtual Assistant</li><li>Admin, secretarial &amp; PA</li><li>Document Control Analyst</li><li>Data Analytics Manager</li><li>Python &amp; Data Scientist</li><li>Data Analyst</li><li>Data Entry Clerk</li><li>Data Entry Operator</li><li>Data Entry Specialist</li><li>Data Entry Administrator</li><li>Data Entry Analyst</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>You will get the hard copy certificates for Free! The delivery charge of the hard copy certificate inside the UK is £3.99 each.</p></div>

                        </div>

Training Express

Level 3 Data Entry, Data Analytics, Document Control & Virtual Assistant Diploma Course

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This class is intended for experienced developers who are responsible for managing big data transformations including:<br />Extracting, loading, transforming, cleaning, and validating data.<br />Designing pipelines and architectures for data processing.<br />Creating and maintaining machine learning and statistical models.<br />Querying datasets, visualizing query results and creating reports</p>Overview<p>Design and build data processing systems on Google Cloud Platform.<br />Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.<br />Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.<br />Derive business insights from extremely large datasets using Google BigQuery.<br />Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.<br />Enable instant insights from streaming data</p><p>Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.</p></div><div id="course-pills-2"><h4>Introduction to Data Engineering</h4><ul><li>Explore the role of a data engineer.</li><li>Analyze data engineering challenges.</li><li>Intro to BigQuery.</li><li>Data Lakes and Data Warehouses.</li><li>Demo: Federated Queries with BigQuery.</li><li>Transactional Databases vs Data Warehouses.</li><li>Website Demo: Finding PII in your dataset with DLP API.</li><li>Partner effectively with other data teams.</li><li>Manage data access and governance.</li><li>Build production-ready pipelines.</li><li>Review GCP customer case study.</li><li>Lab: Analyzing Data with BigQuery.</li></ul><h4>Building a Data Lake</h4><ul><li>Introduction to Data Lakes.</li><li>Data Storage and ETL options on GCP.</li><li>Building a Data Lake using Cloud Storage.</li><li>Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.</li><li>Securing Cloud Storage.</li><li>Storing All Sorts of Data Types.</li><li>Video Demo: Running federated queries on Parquet and ORC files in BigQuery.</li><li>Cloud SQL as a relational Data Lake.</li><li>Lab: Loading Taxi Data into Cloud SQL.</li></ul><h4>Building a Data Warehouse</h4><ul><li>The modern data warehouse.</li><li>Intro to BigQuery.</li><li>Demo: Query TB+ of data in seconds.</li><li>Getting Started.</li><li>Loading Data.</li><li>Video Demo: Querying Cloud SQL from BigQuery.</li><li>Lab: Loading Data into BigQuery.</li><li>Exploring Schemas.</li><li>Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.</li><li>Schema Design.</li><li>Nested and Repeated Fields.</li><li>Demo: Nested and repeated fields in BigQuery.</li><li>Lab: Working with JSON and Array data in BigQuery.</li><li>Optimizing with Partitioning and Clustering.</li><li>Demo: Partitioned and Clustered Tables in BigQuery.</li><li>Preview: Transforming Batch and Streaming Data.</li></ul><h4>Introduction to Building Batch Data Pipelines</h4><ul><li>EL, ELT, ETL.</li><li>Quality considerations.</li><li>How to carry out operations in BigQuery.</li><li>Demo: ELT to improve data quality in BigQuery.</li><li>Shortcomings.</li><li>ETL to solve data quality issues.</li></ul><h4>Executing Spark on Cloud Dataproc</h4><ul><li>The Hadoop ecosystem.</li><li>Running Hadoop on Cloud Dataproc.</li><li>GCS instead of HDFS.</li><li>Optimizing Dataproc.</li><li>Lab: Running Apache Spark jobs on Cloud Dataproc.</li></ul><h4>Serverless Data Processing with Cloud Dataflow</h4><ul><li>Cloud Dataflow.</li><li>Why customers value Dataflow.</li><li>Dataflow Pipelines.</li><li>Lab: A Simple Dataflow Pipeline (Python/Java).</li><li>Lab: MapReduce in Dataflow (Python/Java).</li><li>Lab: Side Inputs (Python/Java).</li><li>Dataflow Templates.</li><li>Dataflow SQL.</li></ul><h4>Manage Data Pipelines with Cloud Data Fusion and Cloud Composer</h4><ul><li>Building Batch Data Pipelines visually with Cloud Data Fusion.</li><li>Components.</li><li>UI Overview.</li><li>Building a Pipeline.</li><li>Exploring Data using Wrangler.</li><li>Lab: Building and executing a pipeline graph in Cloud Data Fusion.</li><li>Orchestrating work between GCP services with Cloud Composer.</li><li>Apache Airflow Environment.</li><li>DAGs and Operators.</li><li>Workflow Scheduling.</li><li>Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.</li><li>Monitoring and Logging.</li><li>Lab: An Introduction to Cloud Composer.</li></ul><h4>Introduction to Processing Streaming Data</h4><ul><li>Processing Streaming Data.</li></ul><h4>Serverless Messaging with Cloud Pub/Sub</h4><ul><li>Cloud Pub/Sub.</li><li>Lab: Publish Streaming Data into Pub/Sub.</li></ul><h4>Cloud Dataflow Streaming Features</h4><ul><li>Cloud Dataflow Streaming Features.</li><li>Lab: Streaming Data Pipelines.</li></ul><h4>High-Throughput BigQuery and Bigtable Streaming Features</h4><ul><li>BigQuery Streaming Features.</li><li>Lab: Streaming Analytics and Dashboards.</li><li>Cloud Bigtable.</li><li>Lab: Streaming Data Pipelines into Bigtable.</li></ul><h4>Advanced BigQuery Functionality and Performance</h4><ul><li>Analytic Window Functions.</li><li>Using With Clauses.</li><li>GIS Functions.</li><li>Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.</li><li>Performance Considerations.</li><li>Lab: Optimizing your BigQuery Queries for Performance.</li><li>Optional Lab: Creating Date-Partitioned Tables in BigQuery.</li></ul><h4>Introduction to Analytics and AI</h4><ul><li>What is AI?.</li><li>From Ad-hoc Data Analysis to Data Driven Decisions.</li><li>Options for ML models on GCP.</li></ul><h4>Prebuilt ML model APIs for Unstructured Data</h4><ul><li>Unstructured Data is Hard.</li><li>ML APIs for Enriching Data.</li><li>Lab: Using the Natural Language API to Classify Unstructured Text.</li></ul><h4>Big Data Analytics with Cloud AI Platform Notebooks</h4><ul><li>What's a Notebook.</li><li>BigQuery Magic and Ties to Pandas.</li><li>Lab: BigQuery in Jupyter Labs on AI Platform.</li></ul><h4>Production ML Pipelines with Kubeflow</h4><ul><li>Ways to do ML on GCP.</li><li>Kubeflow.</li><li>AI Hub.</li><li>Lab: Running AI models on Kubeflow.</li></ul><h4>Custom Model building with SQL in BigQuery ML</h4><ul><li>BigQuery ML for Quick Model Building.</li><li>Demo: Train a model with BigQuery ML to predict NYC taxi fares.</li><li>Supported Models.</li><li>Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.</li><li>Lab Option 2: Movie Recommendations in BigQuery ML.</li></ul><h4>Custom Model building with Cloud AutoML</h4><ul><li>Why Auto ML?</li><li>Auto ML Vision.</li><li>Auto ML NLP.</li><li>Auto ML Tables.</li></ul></div>

Nexus Human

Google Data Analytics

Data Engineering

Big Data Analytics

Data Science

Google Cloud

Data Skills

Data Engineering on Google Cloud

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Experienced system administrators or network administrators, software and DevOps engineers</p>Overview<p>By the end of the course, you should be able to meet the following objectives:<br />Describe VMware NSX Advanced Load Balancer architecture<br />Describe VMware NSX Advanced Load Balancer components and main functions<br />Explain VMware NSX Advanced Load Balancer key features and benefits<br />Describe and leverage VMware NSX Advanced Load Balancer REST API<br />Describe and leverage VMware NSX Advanced Load Balancer SDKs with extended focus on Python SDK<br />Leverage REST API and SDK features and functions to provision application delivery components<br />Describe and leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations<br />Describe and leverage VMware NSX Advanced Load Balancer Github, Docker (avinetworks/avitools), Ansible Galaxy and other open source resources to accelerate the automation planning and implementation<br />Leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations to provision infrastructure components<br />Leverage VMware NSX Advanced Load Balancer Ansible and Terraform integrations to automate and streamline application delivery services provisioning</p><p>This three-day, fast-paced course provides comprehensive training on how to automate infrastructure and application components of VMware NSX Advanced Load Balancer (Avi Networks) solution. This course covers key application delivery features of NSX Advanced Load Balancer (Avi Networks) features and functionality offered in VMware NSX Advanced Load Balancer 18.2 release and focuses on how to plan and implement automation of infrastructure and application components leveraging REST API, SDK or automation solutions such as Ansible, Terraform or similar. Access to a software-defined data center environment is provided through hands-on labs to reinforce the skills and concepts presented in the course.</p></div><div id="course-pills-2"><h4>Course Introduction</h4><ul><li>Introductions and course logistics</li><li>Course objectives</li></ul><h4>Introduction to NSX Advanced Load Balancer</h4><ul><li>Introduce NSX Advanced Load Balancer</li><li>Discuss NSX Advanced Load Balancer use cases, and benefits</li><li>Explain NSX Advanced Load Balancer architecture and components</li><li>Explain the management, control, data, and consumption planes and functions</li></ul><h4>Virtual Services Configuration Concepts</h4><ul><li>Explain Virtual Service components</li><li>Explain Virtual Service types</li><li>Explain and configure basic virtual services components such as Application Profiles, Network Profiles,Pools and Health Monitors</li></ul><h4>Pools Configuration Concepts</h4><ul><li>Explain and deep dive on Pool configuration options</li><li>Describe multiple load balancing algorithms</li><li>Explain multiple Health Monitor types</li><li>Explain multiple Persistent profiles</li><li>Explain and configure Pool Groups</li></ul><h4>Leveraging NSX Advanced Load Balancer REST API</h4><ul><li>Explain NSX Advanced Load Balancer automation vision</li><li>Explain and introduce NSX Advanced Load Balancer REST API</li><li>Describe NSX Advanced Load Balancer REST API methods and capabilities</li><li>Describe NSX Advanced Load Balancer REST API session handling properties such authentication, API versioning and tenancy model</li><li>Deep dive on NSX Advanced Load Balancer REST API Object Model</li><li>Explain and investigate NSX Advanced Load Balancer REST API leveraging browser and command line utilities</li><li>Explain and interact with NSX Advanced Load Balancer REST API leveraging browser, Postman and Curl</li><li>Explain Swagger-based API Documentation</li><li>Explain and leverage NSX Advanced Load Balancer Inventory API</li><li>Explain and leverage NSX Advanced Load Balancer methods such as GET, PUT, POST and PATCH and associated queries, filters and parameters</li><li>Deep dive on NSX Advanced Load Balancer PATCH method</li><li>Explain and leverage NSX Advanced Load Balancer Analytics API</li><li>Explain and leverage NSX Advanced Load Balancer MACRO API</li></ul><h4>NSX Advanced Load Balancer Software-Defined Kits (SDKs) and ControlScripts</h4><ul><li>Introduce NSX Advanced Load Balancer SDKs</li><li>Describe, install and leverage NSX Advanced Load Balancer Python SDK</li><li>Deep dive on NSX Advanced Load Balancer Python SDK</li><li>Describe and leverage Golang SDK</li><li>Leverage NSX Advanced Load Balancer open source resources such as Github, etc to accelerate SDKs adoption</li><li>Describe NSX Advanced Load Balancer Events and Alerts framework</li><li>Introduce ControlScripts foundations</li><li>Leverage ControlScripts to automate configuration changes and alerts remediation</li></ul><h4>Automating NSX Advanced Load Balancer Application Delivery Services with Ansible and Terraform</h4><ul><li>Introduce NSX Advanced Load Balancer Configuration Orchestration and Management vision</li><li>Introduce and explain Ansible foundations</li><li>Describe Ansible and NSX Advanced Load Balancer Ansible capabilities</li><li>Deep dive and implement NSX Advanced Load Balancer Ansible Core configuration modules (avinetworks/avisdk)</li><li>Deep dive and implement Ansible NSX Advanced</li><li>Load Balancer Declarative configuration role (avinetworks/aviconfig)</li><li>Leverage Swagger NSX Advanced Load Balancer REST API models to develop and implement Ansible playbooks</li><li>Explain application delivery configuration automation</li><li>approach and models</li><li>Apply configuration automation models with Ansible</li><li>Introduce and explain Terraform foundations</li><li>Describe Terraform and NSX Advanced Load Balancer Terraform capabilities</li><li>Deep dive and implement NSX Advanced Load Balancer Terraform Provider</li><li>Leverage Swagger NSX Advanced Load Balancer REST API models to develop and implement Terraform plans</li><li>Apply configuration automation models with Terraform</li></ul><h4>Automating NSX Advanced Load Balancer Infrastructure with Ansible and Terraform</h4><ul><li>Introduce NSX Advanced Load Balancer infrastructure Automation vision</li><li>Describe infrastructure deployment approach and capabilities</li><li>Describe Ansible and NSX Advanced Load Balancer Ansible Infrastructure deployment approach and capabilities</li><li>Describe Terraform and NSX Advanced Load Balancer Terraform deployment approach and capabilities</li><li>Leverage Terraform to deploy Controllers and perform system configuration, including control plane cluster setup</li><li>Leverage Terraform to provision Cloud, Service Engine Groups and Service Engine components</li><li>Describe and leverage Ansible roles to deploy Controllers and perform initial system configuration, including control plane cluster setup</li><li>Leverage Ansible declarative and core roles to provision Cloud, Service Engine Groups and Service Engine components</li><li>Describe and implement combined Terraform + Ansible model to streamline NSX Advanced Load Balancer solution deployment</li></ul></div>

VMware Vsphere

Service Design Courses (SDC)

Microsoft Virtual Academy (MVA)

VMware NSX Advanced Load Balancer: Infrastructure and Application Automation

<p><strong> 3-</strong><strong>in-1 Exclusive Bundle </strong></p> <p>Our extensive <strong>C++</strong> course is developed thoroughly for you to indulge and learn all the topics with utmost clarity and thorough explanation.</p> <p>This course includes not only one course but <strong>THREE</strong> courses within the bundle. So you are getting two extra courses for the price of one, no extra money! Our course aims to provide detailed insight into the C++ with full support from our teachers. So if you are looking for a convenient way to boost your knowledge further of the C++ topic but are too busy to go to a class, then enrol in this course and you are sorted!</p> <p>Our in-depth course has no time limit and can be accessed from anywhere in the world.</p> <p>You'll pick up a ton of fresh information, whether you're a novice or an advanced. The mega course's sheer size and breadth speak for itself! <strong>So, do not delay any further; we are excited and looking forward to letting you in our CPD Accredited course as much as you are!</strong></p> <p><strong>The following courses will not only fast-track your career but also make it even more rewarding:</strong></p> <ul> <li><strong>Course 01: </strong>C++ Programming Certificate Course</li><li><strong>Course 02: </strong>C++ Development: The Complete Coding Guide</li><li><strong>Course 03: </strong>Computer Vision By Using C++ and OpenCV with GPU support</li></ul> <p><strong><strong>Learning Outcome</strong></strong></p> <p>From this interactive course, you will be able to:</p> <ul> <li>Deep dive into the basics of C++.</li><li>Learn how to improve your skills in general.</li><li>Increase your ability to reason and solve problems.</li><li>Utilise the C++ to boost output.</li><li>Gain confidence and clarity in your communication.</li><li>Explore different career routes in this field.</li></ul><p>This comprehensive three-in-one<strong> C++ </strong>course equips you with critical guidance, methods, and strategies for increasing both employee and professional development. The C++ course from Next Generation's easy-to-digest and deliverable modules will provide you with the most essential and useful knowledge for growing your profession, from setting personal development goals to forming a cross-functional team.</p> <p><strong>Course Curriculum: </strong></p> <ul> <li>Starter Examples</li><li>Learning C Concepts</li><li>Data Types and Inference</li><li>Constants L and R Values</li><li>Increment &amp; Decrement Operators</li><li>Conditional Ternary Operator</li><li>Range-Based For Loop</li><li>Function Arguments Passed By Value</li><li>Multidimensional Arrays</li><li>Preprocessor Conditional Directives</li><li>Optical flow with Nvidia Flownet2 (Python)</li></ul> <p>And many more.....</p> <p><strong>Show off your new skills with a certificate of completion</strong></p> <p>Once you complete the <strong>C++ </strong>course, you will be eligible to request a digital certificate for free. For Printed Transcript &amp; Hardcopy Certificate-</p> <ul> <li><strong>4.99 GBP</strong> (Inside the UK Postal Fee)</li><li><strong>8.99 GBP</strong> (International Delivery Fee)</li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                             <p>This comprehensive<strong> three-in-one </strong>bundled <strong>C++</strong> course has no restrictions for people registering for it. Anyone between the age of above sixteen can apply for the course. So whether you are an eager learner to explore the deeper unknown facts of the subject or a skilled individual in this field who wishes to enhance their career prospects further can take this course.</p> 
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                             <p>No previous knowledge is required to enrol in this training.</p> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <p>The aim of this exclusive bundle<strong> C++</strong> course is to help you toward your dream career. So, complete this course and enhance your skills to explore opportunities in relevant areas.</p> 
                        </div>

Certified Cryptocurrency Investigator Course (CCI)

Certificate in Teaching Program (CTP)

Certificate in Education & Training (CET)

Professional Skills Course (PSC)

Trainers Course

Continuous Professional Development (CPD)

Professional C++ Coder

<strong>A knowledge of Windows PowerShell is a must for IT professionals and this course is a one-stop-shop for gaining this necessary and in-demand skill.

This course is designed to take system administrators to an advanced level in PowerShell scripting. You will find ways to automate your daily work using the advantages of PowerShell scripting.</strong>
        <p>PowerShell is a task automation and configuration management program from Microsoft, consisting of a command-line shell and the associated scripting language. Professionals who want to start with PowerShell and have some basic idea of the command line will find it extremely easy to understand the underlying concepts of PowerShell and will be able to integrate PowerShell with non-Microsoft products as well.

Here, you will look at the PowerShell logging module, installing software with PowerShell, automation solution for daily validation reports, database interaction using PowerShell, automation for Web/App Service status, Windows Task Scheduler and scheduling PowerShell scripts to run, pulling reports from Windows event viewer using this PowerShell, looking at PowerShell advanced functions and modules, building validation, PowerShell with Windows Event Viewer, PowerShell for programming, and using PowerShell as an automation tool.

You will be working on a project where you will develop a robust automation solution for 'Application and System Validation', which generates a consolidated HTML report in the end, displaying all different test case results.

By the end of this course, you will have learned advanced-level knowledge of PowerShell scripting. You will easily automate your daily repetitive work using PowerShell scripting.

All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/PowerShell-for-Automating-Administration</p>
        <h2>What You Will Learn</h2><p>Learn automation of administration work with Windows PowerShell<br>Develop application and server validation mechanism<br>Develop file purge and archive mechanism from scratch<br>Learn an important skill for Windows Server Administration<br>Learn the concepts of Windows PowerShell scripting<br>Write your own automation scripts for complex scenarios with ease</p>
        <h2>Audience</h2><p>This course is designed for students/IT professionals who desire to automate their or their team's day-to-day manual work, system administrators, and aspirants seeking a career in IT automation. Application support analysts and cloud consultants will also gain a lot from this course.<br><br>To get the most out of this course, you should have some basic understanding of the command line (Win CMD/Unix Shell), and a PC/laptop installed with Windows OS.</p>
        <h2>Approach</h2><p>This is a comprehensive, easy-to-understand, to-the-point, and well-balanced course on automating tasks using PowerShell scripts. There will be a brief introduction to what you will be learning/implementing/executing at the start of each video to set the right expectations. You will be learning by working on practical projects and assignments.</p>
        <h2>Key Features</h2><p>Learn and implement advanced concepts such as file backup, archival, and purge solution * Comes bundled with all the resource files, PPTs, and assignment questions to leverage your learning * A well-balanced, application-based, and complete course to use PowerShell for automating tasks</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/PowerShell-for-Automating-Administration</p>
        <h2>About the Author</h2><h3>Vijay Saini</h3><p>Vijay Saini is an accomplished IT professional who brings a wealth of expertise to the table. With a diversified background across multiple domains, Vijay has proven himself a valuable asset to his current role as a leading cloud service provider. His strong focus on automation has resulted in significant cost savings for the business, making him a valuable asset to the team.

Vijay is highly knowledgeable in the areas of Microsoft Azure, cloud technologies, DBMS, and Python, and is a specialist in automation using PowerShell. With a passion for teaching, Vijay has leveraged e-learning platforms to share his expertise with others, helping them develop their own skills and knowledge.</p>
        <h2>Course Outline</h2><h4>1. PowerShell to Install Software on Multiple Remote Computers</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Software Installations with PowerShell - Part 1</p><p style='margin: 0'>This video explains PowerShell and helps you understand ways to install and download the applications/tools with the help of PowerShell.</p></td></tr><tr><td style='padding: 15px;'><p>2. Software Installations with PowerShell - Part 2</p><p style='margin: 0'>This video will help you develop a super powerful PowerShell script that takes care of end-to-end software installation for us. Here, you will successfully download and install the WinRAR and Notepad++ applications.</p></td></tr></tbody></table><br><h4>2. PowerShell Automation Concepts</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How to Hide Date Time Using PowerShell</p><p style='margin: 0'>This video will help you hide date and time in the taskbar using PowerShell in a Windows 11 machine.</p></td></tr><tr><td style='padding: 15px;'><p>2. Learn PowerShell Logging Module</p><p style='margin: 0'>This video explains automating systems and the application validation process. Here, you will be using the PowerShell Logging module, which helps automate complex operations that run error-free.</p></td></tr></tbody></table><br><h4>3. CSV File Handling with PowerShell</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. CSV File Handling with PowerShell</p><p style='margin: 0'>This video will help you with the handling or actually doing some sort of operations on the CSV file using PowerShell. Here, you will use the live structured data in a CSV file and perform various operations on it.</p></td></tr></tbody></table><br><h4>4. Administering Planned Service Restarts</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Manage Windows Services Using PowerShell - 1</p><p style='margin: 0'>This is the first of the two-part video where you will learn the commandlet Get-Service that helps fetch the services.</p></td></tr><tr><td style='padding: 15px;'><p>2. Manage Windows Services Using PowerShell - 2</p><p style='margin: 0'>This is the second of the two-part video where you will learn the other commandlets such as Set-Service, Restart-Service, Start-Service, and Stop-Service.</p></td></tr><tr><td style='padding: 15px;'><p>3. Automation of Planned Service Restarts - Version 1</p><p style='margin: 0'>This is the first of the two-part video where you will work on a PowerShell script for a centralized solution to deal with restarting fixed services on multiple servers.</p></td></tr><tr><td style='padding: 15px;'><p>4. Automation of Planned Service Restarts - Version 2</p><p style='margin: 0'>This is the second of the two-part video where you will work on a PowerShell script for a centralized solution to deal with restarting different services on different servers.</p></td></tr></tbody></table><br><h4>5. Project Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project that you will be working on in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Project Directory and File Structure</p><p style='margin: 0'>This video demonstrates the project directory and file structure that will help you give an upper hand in customizing the automation part as per your requirement.</p></td></tr><tr><td style='padding: 15px;'><p>3. XML Configuration File</p><p style='margin: 0'>This video explains the XML configuration file that is used to configure the parameters and initial settings for our computer programs.</p></td></tr></tbody></table><br><h4>6. Lab Setup</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Lab Setup Guide</p><p style='margin: 0'>This video is a guide to the lab setup where you will understand the architecture of the simple application.</p></td></tr><tr><td style='padding: 15px;'><p>2. Lab Setup: Deploying the Resources in Cloud</p><p style='margin: 0'>This is a lab setup video where you will be deploying the resources in Cloud.</p></td></tr><tr><td style='padding: 15px;'><p>3. Lab Setup: Deploying the Validation Scripts</p><p style='margin: 0'>This is a lab setup video where you will be deploying the Validation Scripts.</p></td></tr><tr><td style='padding: 15px;'><p>4. Lab Setup: Script Execution</p><p style='margin: 0'>This is a lab setup video where you will be looking at the execution of the script.</p></td></tr></tbody></table><br><h4>7. Server Validation Script</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview of Server Validation Script</p><p style='margin: 0'>This video provides an Overview of Server Validation Script.</p></td></tr><tr><td style='padding: 15px;'><p>2. Validation Functions Overview</p><p style='margin: 0'>This video provides an overview of the validation functions.</p></td></tr><tr><td style='padding: 15px;'><p>3. Validation Functions Discussion - Part 1</p><p style='margin: 0'>This is the first of the two-part video that focuses on the validation functions. Here, you will be studying these functions: Validate-SysInfo, Validate-URL, Validate-TopMemoryConsumingProcesses, Validate-DisktHealth, and Validate-SQLConnection.</p></td></tr><tr><td style='padding: 15px;'><p>4. Validation Functions Discussion - Part 2</p><p style='margin: 0'>This is the second of the two-part video that focuses on the validation functions. Here, you will be studying these functions: Validate-WebService, Validate-AppService, Validate-IIS, Validate-SQLDatabase, and Validate-AppVersion.</p></td></tr></tbody></table><br><h4>8. Report Consolidation and Code Enhancement</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Report Consolidation - Template Overview</p><p style='margin: 0'>This video explains the report consolidation script. Here, you will be looking at an overview of the template.</p></td></tr><tr><td style='padding: 15px;'><p>2. Report Consolidation - Script Overview</p><p style='margin: 0'>This video explains the report consolidation script. Here, you will be looking at implementing the script using PowerShell.</p></td></tr><tr><td style='padding: 15px;'><p>3. Code Enhancements - Logging</p><p style='margin: 0'>This video demonstrates code enhancements. Here, you will be using the logging module using PowerShell.</p></td></tr></tbody></table><br><h4>9. Productionization of the Application Validation Tool</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Code Changes to be able to execute Scripts Centrally</p><p style='margin: 0'>In this video, you will see what changes we can make in our application and system validation scripts to be able to put the scripts on a remote server; and we never have to login into the remote server at all, be it for executing the script or looking at the result.</p></td></tr><tr><td style='padding: 15px;'><p>2. Preparing Pull_Repos_and_Execute Script</p><p style='margin: 0'>This video demonstrates remote script execution architecture. If you don't want to copy the scripts to the server each time and want to fully automate it using PowerShell, then this video will be of great help.</p></td></tr><tr><td style='padding: 15px;'><p>3. Remote Execution of the Script from Terminal Server</p><p style='margin: 0'>In this video, you will be executing the script remotely from the terminal server.</p></td></tr></tbody></table>

Packt

Shell Scripting

A knowledge of Windows PowerShell is a must for IT professionals and this course is a one-stop-shop for gaining this necessary and in-demand skill.

This course is designed to take system administrators to an advanced level in PowerShell scripting. You will find ways to automate your daily work using the advantages of PowerShell scripting.

PowerShell for Automating Administration

<strong>Master HTML 5 and the Materialize CSS Framework by Building 5 Real-World Responsive Material Design-Based Themes</strong>
        <p>The author has created a completely custom HTML learning environment for the first few sections of this course. Each lecture has an html page associated with it and has a start state. As we move along through the lectures, you code along with him learning all that Materialize offers including CSS components, utilities, JS widgets and more. 5 Real Projects/Themes. We will create the following projects from scratch..Travelville -  A travel agency website/theme  (Carousel slider, autocomplete, scrolling navigation)Quazzu - Cloud hosting theme (SideNav, cards, CSS overlays)Madmin - Interactive admin theme (Working todo list, charts, preloader, counter script)BizLand - Business consultation theme with a full screen landing page (Scrollfire, Google maps integration)Galappear - Graphic design portfolio (Uses Sass and scrollfire for scroll animations)</p>
        <h2>What You Will Learn</h2><p>- Learn to correctly structure HTML 5 documents with semantic tags and attributes. <br>- Learn and create amazing high-quality Materialize themes and UIs from scratch. <br>- Learn the Materialize utilities, classes, components and JS widgets using a custom sandbox environment. <br>- Learn some custom JavaScript/JQuery to make your themes more interactive.</p>
        <h2>Audience</h2><p>Anyone that wants to learn and master Materialize CSS and build real world themes</p>
        <h2>Approach</h2><p>This course is for anyone that knows basic HTML and a little CSS that wants to take their frontend website and UI creation to the next level using the Materialize CSS framework which is based off of Google's Material Design scheme.</p>
        <h2>Key Features</h2><p>- You will learn: - CSS and JavaScript components, Animations/transitions.  * - Build 5 beautiful and completely responsive, real-world themes from absolute scratch.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/materialize-css-from-scratch-with-5-projects</p>
        <h2>About the Author</h2><h3>Brad Traversy</h3><p>Brad Traversy has been programming for around 12 years and teaching for almost 5 years. He is the owner of Traversy Media, which is a successful web development YouTube channel and specializes in everything from HTML5 to front-end frameworks such as Angular as well as server-side technologies such as Node.js, PHP, and Python. Brad has mastered explaining overly complex topics in a simple manner that is understandable. Invest in your knowledge by watching Brad's courses.</p>
        <h2>Course Outline</h2><h4>1. Intro & Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome To the Course</p><p style='margin: 0'>Intro &amp; Getting Started: Welcome To the Course</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Materialize & Why Use It?</p><p style='margin: 0'>Intro &amp; Getting Started: What Is Materialize &amp; Why Use It?</p></td></tr><tr><td style='padding: 15px;'><p>3. Environment Setup</p><p style='margin: 0'>Intro &amp; Getting Started: Environment Setup</p></td></tr><tr><td style='padding: 15px;'><p>4. Materialize Sandbox Setup</p><p style='margin: 0'>Intro &amp; Getting Started: Materialize Sandbox Setup</p></td></tr></tbody></table><br><h4>2. CSS Components - Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Typography & Alignment</p><p style='margin: 0'>CSS Components - Part 1: Typography &amp; Alignment</p></td></tr><tr><td style='padding: 15px;'><p>2. Text & Background Colors</p><p style='margin: 0'>CSS Components - Part 1: Text &amp; Background Colors</p></td></tr><tr><td style='padding: 15px;'><p>3. Buttons & Icons</p><p style='margin: 0'>CSS Components - Part 1: Buttons &amp; Icons</p></td></tr><tr><td style='padding: 15px;'><p>4. Floating & Fixed Action Buttons</p><p style='margin: 0'>CSS Components - Part 1: Floating &amp; Fixed Action Buttons</p></td></tr><tr><td style='padding: 15px;'><p>5. Navbar</p><p style='margin: 0'>CSS Components - Part 1: Navbar</p></td></tr><tr><td style='padding: 15px;'><p>6. Collections & Badges</p><p style='margin: 0'>CSS Components - Part 1: Collections &amp; Badges</p></td></tr><tr><td style='padding: 15px;'><p>7. The Grid System</p><p style='margin: 0'>CSS Components - Part 1: The Grid System</p></td></tr></tbody></table><br><h4>3. CSS Components - Part 2</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Cards</p><p style='margin: 0'>CSS Components - Part 2: Cards</p></td></tr><tr><td style='padding: 15px;'><p>2. Breadcrumb & Footer</p><p style='margin: 0'>CSS Components - Part 2: Breadcrumb &amp; Footer</p></td></tr><tr><td style='padding: 15px;'><p>3. Basic Form & Input Controls</p><p style='margin: 0'>CSS Components - Part 2: Basic Form &amp; Input Controls</p></td></tr><tr><td style='padding: 15px;'><p>4. Fancy Form & Input Controls</p><p style='margin: 0'>CSS Components - Part 2: Fancy Form &amp; Input Controls</p></td></tr><tr><td style='padding: 15px;'><p>5. Tables & Media</p><p style='margin: 0'>CSS Components - Part 2: Tables &amp; Media</p></td></tr><tr><td style='padding: 15px;'><p>6. Chips & Pagination</p><p style='margin: 0'>CSS Components - Part 2: Chips &amp; Pagination</p></td></tr><tr><td style='padding: 15px;'><p>7. Preloaders</p><p style='margin: 0'>CSS Components - Part 2: Preloaders</p></td></tr></tbody></table><br><h4>4. Interactive Components - Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Carousel</p><p style='margin: 0'>Interactive Components - Part 1: Carousel</p></td></tr><tr><td style='padding: 15px;'><p>2. Collapsible</p><p style='margin: 0'>Interactive Components - Part 1: Collapsible</p></td></tr><tr><td style='padding: 15px;'><p>3. Feature Discovery</p><p style='margin: 0'>Interactive Components - Part 1: Feature Discovery</p></td></tr><tr><td style='padding: 15px;'><p>4. Dialogs (Toasts & Tooltips)</p><p style='margin: 0'>Interactive Components - Part 1: Dialogs (Toasts &amp; Tooltips)</p></td></tr><tr><td style='padding: 15px;'><p>5. Material Box & Slider</p><p style='margin: 0'>Interactive Components - Part 1: Material Box &amp; Slider</p></td></tr><tr><td style='padding: 15px;'><p>6. Modals</p><p style='margin: 0'>Interactive Components - Part 1: Modals</p></td></tr></tbody></table><br><h4>5. Interactive Components - Part 2</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Parallax</p><p style='margin: 0'>Interactive Components - Part 2: Parallax</p></td></tr><tr><td style='padding: 15px;'><p>2. Side Navigation</p><p style='margin: 0'>Interactive Components - Part 2: Side Navigation</p></td></tr><tr><td style='padding: 15px;'><p>3. PushPin</p><p style='margin: 0'>Interactive Components - Part 2: PushPin</p></td></tr><tr><td style='padding: 15px;'><p>4. ScrollSpy Nav Scrolling</p><p style='margin: 0'>Interactive Components - Part 2: ScrollSpy Nav Scrolling</p></td></tr><tr><td style='padding: 15px;'><p>5. Tabs</p><p style='margin: 0'>Interactive Components - Part 2: Tabs</p></td></tr></tbody></table><br><h4>6. Project 1 - Travelville</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Intro</p><p style='margin: 0'>Project 1 - Travelville: Project Intro</p></td></tr><tr><td style='padding: 15px;'><p>2. Navbar & Sidenav</p><p style='margin: 0'>Project 1 - Travelville: Navbar &amp; Sidenav</p></td></tr><tr><td style='padding: 15px;'><p>3. Image Slider</p><p style='margin: 0'>Project 1 - Travelville: Image Slider</p></td></tr><tr><td style='padding: 15px;'><p>4. Search & Icon Boxes</p><p style='margin: 0'>Project 1 - Travelville: Search &amp; Icon Boxes</p></td></tr><tr><td style='padding: 15px;'><p>5. Popular Places & Gallery</p><p style='margin: 0'>Project 1 - Travelville: Popular Places &amp; Gallery</p></td></tr><tr><td style='padding: 15px;'><p>6. Contact Section & ScrollSpy</p><p style='margin: 0'>Project 1 - Travelville: Contact Section &amp; ScrollSpy</p></td></tr></tbody></table><br><h4>7. Project 2 - Quazzu</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Intro</p><p style='margin: 0'>Project 2 - Quazzu: Project Intro</p></td></tr><tr><td style='padding: 15px;'><p>2. Navbar & Showcase</p><p style='margin: 0'>Project 2 - Quazzu: Navbar &amp; Showcase</p></td></tr><tr><td style='padding: 15px;'><p>3. Boxes, About & Overlay</p><p style='margin: 0'>Project 2 - Quazzu: Boxes, About &amp; Overlay</p></td></tr><tr><td style='padding: 15px;'><p>4. Podcast Section & Testimonials Carousel</p><p style='margin: 0'>Project 2 - Quazzu: Podcast Section &amp; Testimonials Carousel</p></td></tr><tr><td style='padding: 15px;'><p>5. Footer & Login Modal</p><p style='margin: 0'>Project 2 - Quazzu: Footer &amp; Login Modal</p></td></tr><tr><td style='padding: 15px;'><p>6. Solutions Page</p><p style='margin: 0'>Project 2 - Quazzu: Solutions Page</p></td></tr><tr><td style='padding: 15px;'><p>7. Signup Page</p><p style='margin: 0'>Project 2 - Quazzu: Signup Page</p></td></tr></tbody></table><br><h4>8. Project 3 - Madmin</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Intro</p><p style='margin: 0'>Project 3 - Madmin: Project Intro</p></td></tr><tr><td style='padding: 15px;'><p>2. Navbar & Sidenav</p><p style='margin: 0'>Project 3 - Madmin: Navbar &amp; Sidenav</p></td></tr><tr><td style='padding: 15px;'><p>3. Stat Counters & Preloader</p><p style='margin: 0'>Project 3 - Madmin: Stat Counters &amp; Preloader</p></td></tr><tr><td style='padding: 15px;'><p>4. Display Chart & Recent Comments</p><p style='margin: 0'>Project 3 - Madmin: Display Chart &amp; Recent Comments</p></td></tr><tr><td style='padding: 15px;'><p>5. Recent Posts & Todo List</p><p style='margin: 0'>Project 3 - Madmin: Recent Posts &amp; Todo List</p></td></tr><tr><td style='padding: 15px;'><p>6. Floating Button & Modals</p><p style='margin: 0'>Project 3 - Madmin: Floating Button &amp; Modals</p></td></tr><tr><td style='padding: 15px;'><p>7. Posts & Categories Pages</p><p style='margin: 0'>Project 3 - Madmin: Posts &amp; Categories Pages</p></td></tr><tr><td style='padding: 15px;'><p>8. Comments & Users Pages</p><p style='margin: 0'>Project 3 - Madmin: Comments &amp; Users Pages</p></td></tr><tr><td style='padding: 15px;'><p>9. Login & Details Pages</p><p style='margin: 0'>Project 3 - Madmin: Login &amp; Details Pages</p></td></tr></tbody></table><br><h4>9. Project 4 - BizLand</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Intro</p><p style='margin: 0'>Project 4 - BizLand: Project Intro</p></td></tr><tr><td style='padding: 15px;'><p>2. Full Screen Header</p><p style='margin: 0'>Project 4 - BizLand: Full Screen Header</p></td></tr><tr><td style='padding: 15px;'><p>3. ScrollFire & Boxes</p><p style='margin: 0'>Project 4 - BizLand: ScrollFire &amp; Boxes</p></td></tr><tr><td style='padding: 15px;'><p>4. About, Testimonials & ScrollSpy</p><p style='margin: 0'>Project 4 - BizLand: About, Testimonials &amp; ScrollSpy</p></td></tr><tr><td style='padding: 15px;'><p>5. Contact & Google Map</p><p style='margin: 0'>Project 4 - BizLand: Contact &amp; Google Map</p></td></tr></tbody></table><br><h4>10. Project 5 - Galappear (With Sass)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Intro</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Project Intro</p></td></tr><tr><td style='padding: 15px;'><p>2. Koala Setup & Sass</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Koala Setup &amp; Sass</p></td></tr><tr><td style='padding: 15px;'><p>3. Header & Navbar</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Header &amp; Navbar</p></td></tr><tr><td style='padding: 15px;'><p>4. Changing & Using Variables</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Changing &amp; Using Variables</p></td></tr><tr><td style='padding: 15px;'><p>5. Gallery Layout</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Gallery Layout</p></td></tr><tr><td style='padding: 15px;'><p>6. Modals & ScrollFire</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Modals &amp; ScrollFire</p></td></tr><tr><td style='padding: 15px;'><p>7. Inner Pages</p><p style='margin: 0'>Project 5 - Galappear (With Sass): Inner Pages</p></td></tr></tbody></table>

JavaScript

Web Development

HTML

Web Design

jQuery

Master HTML 5 and the Materialize CSS Framework by Building 5 Real-World Responsive Material Design-Based Themes

Materialize CSS From Scratch With 5 Projects

<strong>The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R</strong>
        <p>This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy books on R-based data science. In this age of big data, companies across the Globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and take your career to the next level.

Over the course of research, the author realized that almost all the R data science courses and books out there do take account of the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of machine learning: clustering and classification. Unlike other R instructors, the author digs deep into R's machine learning features and give you a one-of-a-kind grounding in data science! You will go all the way from carrying out data reading & cleaning to machine learning, to finally implementing powerful machine learning algorithms and evaluating their performance via R.
The following topics will be covered: - 
&bull; A full introduction to the R Framework for data science 
&bull; Data structures and reading in R, including CSV, Excel, and HTML data 
&bull; How to pre-process and clean data by removing NAs/No data, visualization 
&bull; Machine learning, supervised learning, and unsupervised learning in R 
&bull; Model building and selection and much more!

The course will help you implement methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you'll easily use data science packages such as Caret to work with real data in R. You'll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning. 

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Clustering-and-Classification-with-Machine-Learning-in-R</p>
        <h2>What You Will Learn</h2><p>Read-in data into the R environment from different sources<br>Carry out basic data pre-processing and wrangling in R Studio<br>Implement unsupervised/clustering techniques such as K-means clustering<br>Implement dimensional reduction techniques (PCA) and feature selection<br>Implement supervised learning techniques/classification such as Random Forests<br>Evaluate model performance and learn the best practices for evaluating machine learning model accuracy</p>
        <h2>Audience</h2><p>This course is for students interested in getting started with data science applications in the R Studio environment. Students wishing to learn how to implement unsupervised learning on real data. Anyone with prior exposure to R who wants to get started with practical data science.</p>
        <h2>Approach</h2><p>Every video is packed with hands-on instructions and clear explanations. Real data has been used to demonstrate how to implement these techniques in real life, on your data.</p>
        <h2>Key Features</h2><p>Provides in-depth training in everything you need to know to get started with practical R data science * Jargon-free and suitable for people who have a non-mathematical background * In-depth coverage of the latest unsupervised and supervised techniques </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in R</p><p style='margin: 0'>Introduction to the Course: Welcome to Clustering & Classification with Machine Learning in R</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing R and R Studio</p><p style='margin: 0'>Introduction to the Course: Installing R and R Studio</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Read in CSV & Excel Data</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in CSV & Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Unzipped Folder</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Unzipped Folder</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Googlesheets</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Googlesheets</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in Data from Online HTML Tables-Part 1</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Online HTML Tables-Part 2</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>7. Read Data from a Database</p><p style='margin: 0'>Read in Data from Different Sources in R: Read Data from a Database</p></td></tr></tbody></table><br><h4>3. Data Pre-processing and Visualization</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Pre-processing and Visualization: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. More Data Cleaning</p><p style='margin: 0'>Data Pre-processing and Visualization: More Data Cleaning</p></td></tr><tr><td style='padding: 15px;'><p>3. Introduction to dplyr for Data Summarizing-Part 1</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to dplyr for Data Summarizing-Part 2</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Exploratory Data Analysis (EDA): Basic Visualizations with R</p><p style='margin: 0'>Data Pre-processing and Visualization: Exploratory Data Analysis (EDA): Basic Visualizations with R</p></td></tr><tr><td style='padding: 15px;'><p>6. More Exploratory Data Analysis with xda</p><p style='margin: 0'>Data Pre-processing and Visualization: More Exploratory Data Analysis with xda</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Exploration & Visualization With dplyr & ggplot2</p><p style='margin: 0'>Data Pre-processing and Visualization: Data Exploration & Visualization With dplyr & ggplot2</p></td></tr><tr><td style='padding: 15px;'><p>8. Associations Between Quantitative Variables- Theory</p><p style='margin: 0'>Data Pre-processing and Visualization: Associations Between Quantitative Variables- Theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Testing for Correlation</p><p style='margin: 0'>Data Pre-processing and Visualization: Testing for Correlation</p></td></tr><tr><td style='padding: 15px;'><p>10. Evaluate the Relation Between Nominal Variables</p><p style='margin: 0'>Data Pre-processing and Visualization: Evaluate the Relation Between Nominal Variables</p></td></tr><tr><td style='padding: 15px;'><p>11. Cramer's V for Examining the Strength of Association Between Nominal Variable</p><p style='margin: 0'>Data Pre-processing and Visualization: Cramer's V for Examining the Strength of Association Between Nominal Variable</p></td></tr></tbody></table><br><h4>4. Machine Learning for Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How is Machine Learning Different from Statistical Data Analysis?</p><p style='margin: 0'>Machine Learning for Data Science: How is Machine Learning Different from Statistical Data Analysis?</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML) About? Some Theoretical Pointers</p><p style='margin: 0'>Machine Learning for Data Science: What is Machine Learning (ML) About? Some Theoretical Pointers</p></td></tr></tbody></table><br><h4>5. Unsupervised Learning in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>2. Other Ways of Selecting Cluster Numbers</p><p style='margin: 0'>Unsupervised Learning in R: Other Ways of Selecting Cluster Numbers</p></td></tr><tr><td style='padding: 15px;'><p>3. Fuzzy K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: Fuzzy K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>4. Weighted k-means</p><p style='margin: 0'>Unsupervised Learning in R: Weighted k-means</p></td></tr><tr><td style='padding: 15px;'><p>5. Partitioning Around Meloids (PAM)</p><p style='margin: 0'>Unsupervised Learning in R: Partitioning Around Meloids (PAM)</p></td></tr><tr><td style='padding: 15px;'><p>6. Hierarchical Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: Hierarchical Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>7. Expectation-Maximization (EM) in R</p><p style='margin: 0'>Unsupervised Learning in R: Expectation-Maximization (EM) in R</p></td></tr><tr><td style='padding: 15px;'><p>8. DBSCAN Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: DBSCAN Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>9. Cluster a Mixed Dataset</p><p style='margin: 0'>Unsupervised Learning in R: Cluster a Mixed Dataset</p></td></tr><tr><td style='padding: 15px;'><p>10. Should We Even Do Clustering?</p><p style='margin: 0'>Unsupervised Learning in R: Should We Even Do Clustering?</p></td></tr><tr><td style='padding: 15px;'><p>11. Assess Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in R: Assess Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>12. Which Clustering Algorithm to Choose?</p><p style='margin: 0'>Unsupervised Learning in R: Which Clustering Algorithm to Choose?</p></td></tr></tbody></table><br><h4>6. Feature/Dimension Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dimension Reduction-theory</p><p style='margin: 0'>Feature/Dimension Reduction: Dimension Reduction-theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)</p><p style='margin: 0'>Feature/Dimension Reduction: Principal Component Analysis (PCA)</p></td></tr><tr><td style='padding: 15px;'><p>3. More on PCA</p><p style='margin: 0'>Feature/Dimension Reduction: More on PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Multidimensional Scaling</p><p style='margin: 0'>Feature/Dimension Reduction: Multidimensional Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. Singular Value Decomposition (SVD)</p><p style='margin: 0'>Feature/Dimension Reduction: Singular Value Decomposition (SVD)</p></td></tr></tbody></table><br><h4>7. Feature Selection to Select the Most Relevant Predictors</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Removing Highly Correlated Predictor Variables</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Removing Highly Correlated Predictor Variables</p></td></tr><tr><td style='padding: 15px;'><p>2. Variable Selection Using LASSO Regression</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection Using LASSO Regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Variable Selection with FSelector</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection with FSelector</p></td></tr><tr><td style='padding: 15px;'><p>4. Boruta Analysis for Feature Selection</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Boruta Analysis for Feature Selection</p></td></tr></tbody></table><br><h4>8. Supervised Learning Theory</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Some Basic Supervised Learning Concepts</p><p style='margin: 0'>Supervised Learning Theory: Some Basic Supervised Learning Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. Pre-processing for Supervised Learning</p><p style='margin: 0'>Supervised Learning Theory: Pre-processing for Supervised Learning</p></td></tr></tbody></table><br><h4>9. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are GLMs?</p><p style='margin: 0'>Supervised Learning: Classification: What are GLMs?</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression Models as Binary Classifiers</p><p style='margin: 0'>Supervised Learning: Classification: Logistic Regression Models as Binary Classifiers</p></td></tr><tr><td style='padding: 15px;'><p>3. Binary Classifier with PCA</p><p style='margin: 0'>Supervised Learning: Classification: Binary Classifier with PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Some Pointers on Evaluating Accuracy</p><p style='margin: 0'>Supervised Learning: Classification: Some Pointers on Evaluating Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>5. Obtain Binary Classification Accuracy Metrics</p><p style='margin: 0'>Supervised Learning: Classification: Obtain Binary Classification Accuracy Metrics</p></td></tr><tr><td style='padding: 15px;'><p>6. More on Binary Accuracy Measures</p><p style='margin: 0'>Supervised Learning: Classification: More on Binary Accuracy Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. Our Multi-class Classification Problem</p><p style='margin: 0'>Supervised Learning: Classification: Our Multi-class Classification Problem</p></td></tr><tr><td style='padding: 15px;'><p>9. Classification Trees</p><p style='margin: 0'>Supervised Learning: Classification: Classification Trees</p></td></tr><tr><td style='padding: 15px;'><p>10. More on Classification Tree Visualization</p><p style='margin: 0'>Supervised Learning: Classification: More on Classification Tree Visualization</p></td></tr><tr><td style='padding: 15px;'><p>11. Classification with Party Package</p><p style='margin: 0'>Supervised Learning: Classification: Classification with Party Package</p></td></tr><tr><td style='padding: 15px;'><p>12. Decision Trees</p><p style='margin: 0'>Supervised Learning: Classification: Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>13. Random Forest (RF) Classification</p><p style='margin: 0'>Supervised Learning: Classification: Random Forest (RF) Classification</p></td></tr><tr><td style='padding: 15px;'><p>14. Examine Individual Variable Importance for Random Forests</p><p style='margin: 0'>Supervised Learning: Classification: Examine Individual Variable Importance for Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>15. GBM Classification</p><p style='margin: 0'>Supervised Learning: Classification: GBM Classification</p></td></tr><tr><td style='padding: 15px;'><p>16. Support Vector Machines (SVM) for Classification</p><p style='margin: 0'>Supervised Learning: Classification: Support Vector Machines (SVM) for Classification</p></td></tr><tr><td style='padding: 15px;'><p>17. More SVM for Classification</p><p style='margin: 0'>Supervised Learning: Classification: More SVM for Classification</p></td></tr><tr><td style='padding: 15px;'><p>18. Variable Importance in SVM Modelling with rminer</p><p style='margin: 0'>Supervised Learning: Classification: Variable Importance in SVM Modelling with rminer</p></td></tr></tbody></table><br><h4>10. Additional Lectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Fuzzy C-Means Clustering</p><p style='margin: 0'>Additional Lectures: Fuzzy C-Means Clustering</p></td></tr></tbody></table>

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

Clustering and Classification with Machine Learning in R

Automation Testing

Node.js

Cypress

Python

Programming

This course is perfect for quality assurance professionals who want to step into automation testing with Cypress. You will learn Cypress from scratch and become a specialist in building a solid Cypress automation framework to test any real-world web application.

Cypress - Modern Automation Testing from Scratch + Framework

Database Programming

SQL Server

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The Complete Guide for SQL with Microsoft SQL Server

<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

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CPD Accredited e-Certificate
 Digital certificate - Included 
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 Digital certificate - Included 
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Academy for Health and Fitness

Programming Fundamentals

Career Development

IT Certifications

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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)

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

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

Python Programming

<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 Analyst

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

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

The Ultimate Guide to Python Programming with Python 3.10

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

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

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

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

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

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

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

Python Bootcamp in a Day - Python Programming for Beginners.

Network

Networking

Cisco

This course is an excellent resource to learn network programming using Python. With the help of practical examples, you will learn how to automate networks with Telnet, Secure Shell (SSH), Paramiko, Netmiko, and Network Automation and Programmability Abstraction Layer with Multivendor support (NAPALM).

Python Network Programming for Network Engineers (Python 3)

<p><strong>Introducing Coding using Python</strong></p>
<p>This course introduces your child to a very advanced, yet easy to learn programming languages &ndash; Python. It&rsquo;s safe to say that many apps we use today have been built using Python &ndash; Instagram Web, Netflix, YouTube, to name a few! This course teaches the fundamentals of coding, and creates a solid foundation to solve more complex real-life problems.</p>

Riva Learning

Programming Languages

Computing

Choose this course if your child is new to Python or has done a few hours of Python before. Or, they have a good understanding of block-based platforms like Scratch, and would like to start exploring text-based programming languages.

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