Python for JavaScript Developers: AI, Data Analysis, and Automation Mastery
Bridge your JavaScript expertise to Python, mastering its ecosystem for powerful AI applications, insightful data analysis, and efficient automation workflows.
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Python Kickstart: From JS Syntax to Pythonic Fundamentals
Unit 1: Variables and Basic Types
Unit 2: String Manipulation and Immutability
Unit 3: Control Flow: Conditionals
Unit 4: Control Flow: Loops
Unit 5: Truthiness and Falsiness
Functions, Scope, and Modules: Building Reusable Python Code
Unit 1: Python Functions: The Building Blocks
Unit 2: Scope and Anonymous Functions
Unit 3: Modules and Packages: Code Organization
Python Data Structures: Lists, Tuples, Sets, and Dictionaries
Unit 1: Python Lists: Your Flexible Collections
Unit 2: Tuples: Immutable Sequences
Unit 3: Sets: Unique Collections
Unit 4: Dictionaries: Key-Value Powerhouses
Unit 5: Choosing the Right Structure
Error Handling and Debugging: Robust Python Applications
Unit 1: Understanding Python Errors
Unit 2: Graceful Error Handling with `try`/`except`
Unit 3: Advanced Exception Management
Unit 4: Debugging Python Code
Unit 5: Defensive Programming
Environment Management: `venv` and `pip` for Project Isolation
Unit 1: Why Python Environments?
Unit 2: Mastering `venv` for Isolation
Unit 3: Package Management with `pip`
Unit 4: Managing Project Dependencies
Unit 5: Troubleshooting & Advanced Tips
File System Interaction with `pathlib`: Navigating and Managing Files
Unit 1: Pathlib Basics: Your File System Navigator
Unit 2: File & Directory Operations: Create, Delete, Move
Unit 3: Reading & Writing Files: Text Content
Unit 4: Exploring Directories: Listing & Filtering
Unit 5: Path Info & Metadata: What's There?
HTTP Requests with `requests`: Consuming APIs and Web Content
Unit 1: HTTP Basics for Pythonistas
Unit 2: Handling Response Data
Unit 3: Sending Data: POST & Beyond
Unit 4: Advanced Request Control
Unit 5: Robustness and Best Practices
Introduction to Data Analysis with NumPy: Numerical Operations
Unit 1: NumPy Basics: The N-Dimensional Array
Unit 2: Array Operations: Element-wise and Broadcasting
Unit 3: Array Indexing, Slicing, and Reshaping
Unit 4: Aggregation and Advanced Operations
Pandas DataFrames: Tabular Data Manipulation Fundamentals
Unit 1: DataFrame Basics: Your Tabular Playground
Unit 2: Loading Data: From Files to Frames
Unit 3: Inspecting Your Data: First Look
Unit 4: Selecting Data: Precision Access
Unit 5: Cleaning Up: Missing Data & Renaming
Advanced Pandas: Data Cleaning and Transformation
Unit 1: Cleaning Up Your Data
Unit 2: String Operations and Custom Logic
Unit 3: Combining DataFrames
Unit 4: Reshaping Data
Unit 5: Putting It All Together
Pandas for Data Aggregation and Grouping
Unit 1: Introduction to Grouping Data
Unit 2: Advanced Aggregation Techniques
Unit 3: Filtering and Pivoting Data
Unit 4: Time-Series and Hierarchical Operations
Building Command-Line Tools: `argparse` and Scripting
Unit 1: CLI Fundamentals and Basic Scripting
Unit 2: Introducing `argparse` for Robust CLIs
Unit 3: Advanced `argparse` Features
Unit 4: Input Validation and Script Structure
Unit 5: Executing and Enhancing CLIs
Introduction to AI with Python: Leveraging Pre-trained Models
Unit 1: AI Fundamentals: What's the Buzz?
Unit 2: Types of Pre-trained AI Models
Unit 3: Connecting to AI Services
Unit 4: Advanced Interaction & Ethics
Unit 5: Deployment & Beyond
Prompt Engineering for Large Language Models (LLMs)
Unit 1: Prompt Engineering Fundamentals
Unit 2: Advanced Prompting Techniques
Unit 3: Managing Conversations & Issues
Practical AI Application: Building an AI-Powered Tool
Unit 1: Designing Your AI Tool
Unit 2: Core AI Integration
Unit 3: User Interaction and Validation
Unit 4: Robustness and Efficiency
Unit 5: Refinement and Best Practices
Automation Project: Integrating Skills for Real-World Tasks
