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Web Development
Instructor
About This Course
This comprehensive course will equip you with the Python programming skills and data science tools needed to analyze complex datasets and extract meaningful insights. You'll learn how to manipulate data, create visualizations, and build machine learning models using Python's powerful libraries.
Starting with Python fundamentals, you'll progress to data manipulation with pandas, numerical computing with NumPy, data visualization with Matplotlib and Seaborn, and machine learning with scikit-learn. You'll work with real-world datasets to solve practical problems and develop a portfolio of data science projects.
By the end of the course, you'll have the skills to perform exploratory data analysis, create compelling visualizations, and implement machine learning algorithms. Whether you're looking to start a career in data science or enhance your current role with data analysis skills, this course provides the foundation you need to succeed.
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Science & Tech
Curriculum Overview
This course includes 10 modules, 87 lessons, and 46:29 hours of materials.
What the Web Is and How It Works
Reference Reading Materials
Frontend vs Backend (Conceptual)
Client–Server Model
Web Browsers and Rendering
Installing Developer Tools
Introduction to VS Code
Browser Developer Tools
Folder Structure for Web Projects
Understanding HTML Syntax
HTML Document Structure
Common HTML Elements
Headings, Paragraphs, Lists
Links and Images
Tables and Forms
Semantic HTML
Accessibility Basics
HTML Best Practices
Introduction to CSS
CSS Syntax and Selectors
Colors, Fonts & Text Styling
Box Model
Display Properties
Flexbox Layout
CSS Grid Layout
Responsive Design Concepts
Media Queries
UI Consistency & Design Thinking
What JavaScript Is
Variables & Data Types
Operators & Expressions
Conditional Statements
Loops
Functions
Events & Event Handling
DOM Manipulation
Form Validation
Debugging JavaScript
Why Version Control Matters
Installing Git
Git Basics
Branches & Merging (Intro)
Introduction to GitHub
Creating a Repository
Pushing Code to GitHub
Collaboration Basics
Commit Best Practices
What Prompt Engineering Is
How AI Interprets Prompts
Writing Clear Prompts
Prompt Patterns for Coding
Debugging with AI
Using AI for Documentation
Avoiding Over-Reliance on AI
Human-in-the-Loop Development
Understanding Regenerative AI
AI as a Learning Companion
AI for Accessibility
AI for Inclusive Design
AI for Sustainable Solutions
Human Creativity vs AI
AI for Community Impact
Ethics in Technology
Data Privacy & Consent
Bias in AI Systems
Academic Integrity
Responsible AI in Coding
Transparency & Attribution
Social Impact of Technology
Local & Global Ethics
Project Planning
Wireframing
Project Structure
HTML Development
CSS Styling
JavaScript Interactivity
Git Project Tracking
Testing & Debugging
Deployment Preparation
What Deployment Means
Hosting Static Websites
GitHub Pages / Netlify
Publishing a Live Website
Learning Reflection
Portfolio Building
Career Pathways
AI Skills for the Future
Course Specifications
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