Python for Beginners: Master Coding Basics in No Time

Lesson Plan

Module 1:  Introduction to Python

• What is Python? History and Evolution
• Key Features of Python: Simplicity, Versatility, and Community Support
• Real-World Applications: Web Development, Data Science, Automation, and More
• Comparing Python with Other Languages (Java, JavaScript, C++)

Module 2. Setting Up Python and IDEs

• Installing Python on Different Operating Systems (Windows, macOS, Linux)
• Setting Up Virtual Environments (venv, conda)
• Overview of Popular IDEs (PyCharm, VSCode, Jupyter Notebook)
• Python Interpreter vs. Integrated Development Environments

Module 3:  Understanding Variables and Data Types

• Python Syntax and Code Structure
• Variables, Constants, and Naming Conventions
• Primitive Data Types: Integers, Floats, Strings, and Booleans
• Type Casting and Conversion (str(), int(), float())
• Best Practices for Writing Clean and Readable Python Code

Module 4:  Control Flow and Decision Making

• Writing Conditional Statements (if, elif, else)
• Understanding Logical Operators (and, or, not)
• Looping Constructs: for Loops, while Loops, and Nested Loops
• List Comprehensions and Generator Expressions

Module 5:  Functions in Python

• Defining and Calling Functions
• Understanding Function Scope (Local, Global)
• Default Arguments, Keyword Arguments, and Arbitrary Arguments (*args, kwargs)
• Lambda Functions and Anonymous Functions
• Best Practices for Writing Reusable Functions

Module 6:  Working with Libraries and Modules

• What are Libraries and Modules? Importing Libraries (math, datetime, random)
• Creating and Importing Custom Modules
• Introduction to the Python Standard Library
• Using Third-Party Libraries (pip, PyPI)
• Managing Project Dependencies with Requirements Files

Module 7: File Handling and Input/Output Operations

• Reading and Writing Files (Text, CSV, JSON)
• Using Context Managers for File Handling (with open)
• Handling Large Files and Data Streams
• Error Handling with Files (FileNotFoundError, IOError)
• Working with Directories and File Paths (os, pathlib)

Module 8: Basic Data Structures

• Introduction to Lists, Tuples, Dictionaries, and Sets
• List and Dictionary Comprehensions
• Understanding Mutable vs. Immutable Data Structures
• Sorting, Slicing, and Iterating through Data Structures
• Using Collections Module (namedtuple, defaultdict, deque)

Module 9: Introduction to Object-Oriented Programming (OOP)

• Understanding Classes, Objects, and Methods
• Attributes and Methods: Defining and Accessing
• Inheritance, Encapsulation, and Polymorphism
• Using Magic Methods (dunder methods like __init__, __str__, __repr__)
• Creating and Managing Class Hierarchies

Module 10: Python Project: Building a Simple Application

• Project Planning: Gathering Requirements and Designing the App
• Structuring the Codebase for Scalability
• Building a Command-Line or GUI Application (using tkinter)
• Implementing Core Functionality (e.g., Calculator, To-Do List)
• Adding Input Validation and Error Handling

Module 11: Debugging and Error Handling

• Understanding Common Errors (SyntaxError, TypeError, ValueError)
• Using try-except Blocks for Exception Handling
• Raising and Creating Custom Exceptions
• Debugging Tools and Techniques (pdb, breakpoints)
• Logging for Better Error Tracking (logging module)

Module 12:  Basic Web Scraping with Python (Bonus)

• Introduction to Web Scraping: Ethics and Best Practices
• Understanding HTML Structure for Scraping
• Using BeautifulSoup to Parse Web Pages
• Extracting Data from Web Pages (tags, attributes, text)
• Handling Web Scraping Challenges (pagination, headers)

Module 13: Exploring Automation with Python

• Automating File Operations (Copying, Renaming, Deleting)
• Working with APIs (requests module)
• Automating Excel and CSV Manipulations (pandas, openpyxl)
• Creating Scheduled Scripts with cron (Linux) or Task Scheduler (Windows)

Module 14: Introduction to Data Analysis and Visualization (Bonus)

• Basics of Data Manipulation with pandas
• Visualizing Data with matplotlib and seaborn
• Performing Simple Statistical Analysis
• Cleaning and Preparing Data for Analysis

Module 15:  Building and Consuming REST APIs with Flask (Bonus)

• Introduction to Flask for Web Development
• Creating Endpoints and Handling Requests
• Parsing JSON Data and Structuring API Responses
• Building a Simple CRUD API
• Deploying a Flask App to Heroku or AWS

Module 16: Access to Real-World Python Projects for Practice

• Example Projects: Web Scrapers, Data Analysis Scripts, API Clients
• Building a Simple Web Application with a Database (SQLite)
• Git Version Control for Project Management

Module 17: Tips and Tricks for Improving Coding Efficiency

• Using Pythonic Idioms (list comprehensions, context managers)
• Writing Cleaner Code with PEP 8 Guidelines
• Refactoring Code for Readability and Performance
• Using linters and formatters (pylint, black)

Module 18: Final Project: Building a Portfolio-Worthy Application

• Choosing a Project Aligned with Your Career Goals (Web, Data Science, Automation)
• Project Documentation and Code Comments
• Preparing a GitHub Repository for Portfolio
• Presenting and Explaining the Project in Interviews

Bonus Content:

Module 19:  Tips for Securing Python Programming Jobs

• Understanding Different Python Job Roles (Data Analyst, Web Developer, Automation Engineer)
• Preparing for Technical Interviews (LeetCode, HackerRank)
• Building a Strong LinkedIn Profile and Networking Online

Module 20:  Access to a Free Cheat Sheet of Python Commands and Functions

• Cheat Sheets for Commonly Used Functions and Libraries
• Resources for Ongoing Learning (documentation, online courses)

Module 21: Introduction to Python for Data Science (Optional Module)

• Overview of numpy and pandas for Data Manipulation
• Basics of Machine Learning with scikit-learn
• Visualization with seaborn and matplotlib