Part 1: Preliminaries

(Recap of core python syntax and concepts)

Module 1: Overview

• Python vs Micropython
• Python features and applications
• A demo on Python development environments:
  • IPython, Jupyter and Google Colaboratory
  • VSCode, Thonny, IDLE
  • Running Python code in command-line

Module 2: Getting started on Python environment

• Setup Anaconda Python and VSCode
• Try out simple python programs
• Using Jupyter notebook and Google Colaboratory
• Try out simple python code snippets in the notebook interface

Module 3: Simple python programs

• Statements and expressions
• Variables and literals
• Objects and types
• Arithmetic operators and expressions
• Boolean operators and expressions
• Simple statements and compound statements
• Comments

Module 4: Flow control

• Conditional statements – if...elif...else and match...case
• Loops – for and while
• Small exercises for learning flow control

Module 5: Python exception handling

• An overview on Errors / Exceptions
• Raising exceptions using raise
• Handling exceptions using try ... except

Module 6: Modular development

• Function definition
• Function prototypes
• Scope of variables
• Creating python modules and importing modules

Module 7: Interesting builtin functions

• Iteration helpers – range(), reversed(), sorted(), enumerate(), zip()
• Reduction functions – sum(), min(), max(), all(), any()
• Math helpers – abs(), round(), pow(), divmod()
• String helpers – ord(), chr(), hex(), bin(), oct()

Module 8: Core Python miscellaneous features

• Functional dialects: pure functions and higher-order-functions
• The lambda expressions
• map() and filter() functions
• Comprehensions
• Introspection helpers – id(), type(), hash(), isinstance(), callable(), hasattr(), vars(), globals(), locals()

Part 2: OOP using Python

Module 9: Overview on programming styles

• Procedural programming and modular design
• Object-Oriented-Programming
• Imperative and Functional programming dialects
• Understanding the benefits of using different programming paradigms

Module 10: Introduction to OOP

• Classes and Objects
• Attributes and methods
• Three pillars of OOP
• The concept of Inheritance, Composition and Abstraction

Module 11: OOP using Python – an overview

• Python classes and objects
• Exploring various builtin classes in python
• Special attributes (dunder attributes) and their uses

Module 12: Creating custom classes

• Creating custom classes and constructing objects
• The __init__ method
• Class attributes vs instance attributes
• Instance methods, class methods and static methods
• Functions vs methods: understanding similarites and differences

Module 13: Abstraction, Encapsulation and Inheritance

• Implementing inheritance
• Extending and overriding methods
• Implementing abstraction and encapsulation
• Implementing re-usability using composition

Module 14: Special methods – Part 1

• Implementing support for arithmetic operators
• Implementing support for comparison operators

Module 15: Special methods – Part 2

• Creating custom iterable objects
• Creating custom container objects

Module 16: Special methods – Part 3

• Creating custom callable objects
• Helpers for string, int and float representation
• Attribute accessor methods

Module 17: Decorators and AOP – Basics

• An overview on decorator pattern
• Creating simple decorators

Module 18: OOP odds and ends

• Polymorphism
• Abstract Base Classes
• Static methods and Class methods
• Creating custom exceptions

Part 3: Managing data using Python

Module 19: Structured data in Python

• Using dictionaries, lists, sets and tuples
• Modelling nested data-structures for representing data
• Choosing between tuples, lists, sets and dictionaries for modelling data
• Interesting methods and operations on collection data types

Module 20: Managing strings

• The str vs bytes objects
• String operators and features
• Interesting string methods

Module 21: Basics of Regular expressions

• Overview on regular expressions
• Regex anchors, character classes, quantifiers, alternators and grouping
• Pattern based search, extraction and substitution

Module 22: Text file I/O

• Reading and Writing to text files
• Context management using with clause
• Appending to text files
• Using seek and tell operations

Module 23: More on File I/O

• Working with binary files
• Serializing python data-structures using pickle
• Parsing and Storing structured data into files
• Managing file/directory operations using pathlib module

Module 24: Basic database management using SQLite3 – Part 1

• An overview on Relational Databases
• Tables, Rows and Columns
• An overview on SQL for managing databases

Module 25: Basic database management using SQLite3 – Part 2

• Creating and managing databases using SQLite3
• CRUD operations on SQLite3 using sqlite module

Module 26: Working with CSV files

• Using the csv module to parse and store CSV format files (demo)
• A basic overview on pandas framework
• Loading CSV files into pandas DataFrames
• Basic data analytics on pandas DataFrames

Part 5: Web development using Python

Module 31: An overview on how websites work

• The Internet and the World-Wide-Web
• An overview on Client-Server architecture
• Exploring technologies that drive the WWW

Module 32: Basics of HTTP protocol

• Exploring interesting HTTP methods
• HTTP servers, clients/user-agents, URLs and request parameters
• HTTP request headers, response headers, cookies and payload

Module 33: Web applications

• An overview on web applications
• Server-side and Client-side technologies overview
• Using the httpx library to fetch web pages

Module 34: Parsing common web data formats – Part 1

• An overview on JSON data and parsing JSON formats
• Consuming Web APIs – examples and use-cases
• Interesting projects:
  • Get weather report in a given city
  • Get news feeds
  • Currency converter

Module 35: Parsing common web data formats – Part 2

• An overview on HTML and CSS
• Parsing HTML data using lxml.html
• An overview on XPath and CSS Selectors
• Scraping data from websites – examples, use-cases and ethical considerations

Module 36: Creating static websites

• Hosting static web pages using http.server
• An overview on real-world web servers and hosting

Module 37: Creating simple web applications – Part 1

• An overview on Python web application frameworks
• Getting started with lona framework
• Handling URL routing, user input, events and sessions

Module 38: Creating simple web applications – Part 2

• Create a simple web application frontend to manage a user database
  • Allow adding new user records, modifying and deleting existing user records
  • Provide a mechanism to authenticate a user

Part 6: Graphics and GUI using Python

Module 39: An overview on GUI applications

• Building command-line applications vs GUI applications vs Web applications
• Best practices and use-cases
• An exploration of popular GUI frameworks: tkinter, pydeargui, kivy

Module 40: Simple GUI applications using tkinter

• Getting started with tkinter
• Exploring Tk widgets and callbacks
• Creating simple GUI forms using Tk

Module 41: Graphics using turtle graphics

• Getting started with turtle
• Basic movement and positioning
• Simple algorithmic patterns
• Pen control
• Managing events
• Drawing various shapes

Module 42: Graphics using arcade framework

• An overview on arcade framework
• Exploring features and game demos built using arcade
• Getting started with arcade
• Drawing basic shapes

Module 43: Animation using arcade framework

• Drawing and moving sprites
• Managing user control (keyboard and mouse events)
• Sound effects
• Physics Engine and Camera control

Module 44: Creating simple games

• TicTacToe
• Nibbles
• Simple shooter game (mimic of para-trooper)

Part 7: Image Processing, Data Visualization and basic Machine Learning

Module 45: An overview on numpy module

• Basics of numpy arrays
• Matrix and math operations on numpy arrays (overview)
• Representing images as numpy arrays and manipulating them

Module 46: Image Processing and manipulation

• How are images represented in a computer
• An overview of image file formats
• Image processing and manipulation using pillow library

Module 47: Data Visualization

• Visualizing data using charts and plots using pandas
• Basics of matplotlib
  • Line plots
  • Scatter plots
  • Bar charts
  • Pie charts
• Exploring various features of matplotlib (Axes, ticks, spines)

Module 48: Basics of Machine Learning and AI

• An overview on Machine Learning
• Classification, Regressing and Clustering
• Mathematical models and Neural Networks
• Generative AI using Transformers and LLMs
• Simple examples for Classification and Regression (using scikit-learn)
• Exploring LLMs using python llm framework