Course Content
Module 1 – Getting Started with Python
introduced the fundamentals of Python, giving beginners a clear understanding of how the language works and how to start writing simple programs. Python was highlighted as a beginner-friendly language with simple syntax, making it easy to read and write code.
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Lesson 1: Introduction to Python, Variables, and Syntax
Lesson 2: Python Data Types
Lesson 3: Type Conversion & Type Checking
Lesson 4: Strings and Operations
Summary
Quiz: Getting Started with Python
Lab Activity : Getting Started with Python
Module 2 – Introduction to Python Programming
In this Introduction to Python module, learners explore Python’s clear, readable syntax and powerful features. Beginning with installation and a simple “Hello, World!” script, you will progress through variables, control flow and functions using step-by-step examples. By the end, you will be equipped to write your own Python programmes, automate routine tasks and tap into an extensive library ecosystem for real-world projects.
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Lesson 1: Introduction to Python & Setup
Lesson 2: Python Real-World Applications
Lesson 3: Getting Started with Python
Lesson 4: Installing Python Packages and Modules
Summary
Quiz: Introduction to Python and Setup
Lab Activity : Intro to Python
Basic Command for Command prompt, PowerShell, Zsh(macOS)
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Basic Commands
Module 3 – Variables, Data Types and Basic Operations
In the Variables, Data Types and Basic Operations in Python module, learners explore how to store and manage data using variables, master fundamental types such as integers, floats, strings and booleans, and perform arithmetic, comparison and logical operations step by step. Clear explanations, real world examples and hands on exercises guide you through writing and debugging code. By the end of this module, you will be ready to build dynamic Python programs and automate everyday tasks.
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Lesson 1: Python Variables
Lesson 2: Basic Arithmetic Operations
Lesson 3: Working with String
Summary
Quiz: Variables, Data Types and Basic Operations
Lab Activity : Variables, Data Types and Basic Operations
Module 4 – Control Flow – Conditions and Loops
Control flow structures determine the order in which your program’s code executes. With conditional statements, you can make decisions and execute certain code blocks only when specific conditions are met. Loops allow you to repeat actions efficiently without writing redundant code. In this module, we will explore fundamental control flow concepts in Python in a step-by-step manner, similar to Microsoft’s learning curriculum. By the end, you’ll understand how to use if, elif, and else statements (including nested conditions) for decision-making, how truthy and falsy values work in Boolean logic, how to construct for loops (using range() and iterating over collections), how to use while loops along with loop control statements (break and continue), and how to leverage list comprehensions and generator expressions for concise looping. Finally, we’ll apply these concepts in a practical exercise to build an interactive decision-making system. Each section below includes explanations, code examples, and mini-exercises to reinforce the concepts, all formatted for clarity and easy follow-along.
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Lesson 1: if, elif, else statements and nesting
Lesson 2: Truthiness, boolean logic, and chaining comparisons
Lesson 3: for loops with range() and iterating over collections
Lesson 4: while loops and loop control (break, continue)
Lesson 5: List comprehensions and generator expressions
Summary
Quiz: Control Flow – Conditions and Loops
Lab Activity : Building Calculator
Day 1 Summary
We covered Modules 1, 2 & Module 3 (Lesson 1 & 2)
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Day 1 – Recording
Module 5 – Functions and Code Organisation
Imagine you need to clean up a messy data set or send a personalised email to each customer. Instead of writing the same steps over and over, you can create a function and call it whenever you need. In this lesson on Functions and Code Organisation, you will learn how to define functions, pass and return information, document your work and group related code into modules for easy reuse and maintenance.
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Lesson 1: Defining and calling functions
Lesson 2: Function Arguments: Positional, Keyword, Default, and Variable-Length
Lesson 3: Return values and early exits
Lesson 4: Variable scope: local vs. global
Lesson 5: Lambda (anonymous) functions
Lesson 6: Organizing code into modules and packages
Lesson 7: Docstrings, type hints, and basic doc generation
Summary
Quiz: Functions and Code Organisation
Lab Activity : Functions and code Organisation
Day 2 Summary
Summary for Day 21 Aug 2025
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Day 2 – Recording
Module 6 – Automating a Daily Task with Conditionals & Loops
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Lesson 1: Identifying a repetitive, manual task to automate
Lesson 2: Reading and validating input data
Lesson 3: Applying conditional logic for decision-making
Quiz: Automating a Daily Task with Conditionals & Loops
00:05:00
Lab: build a CLI script to bulk-rename or categorize items
Day 3 Summary
Summary of Day 28 Aug 2025
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Day 3 Recording
Module 7 – Working with Files and Folders
In this lesson, we will learn how to manipulate files and directories using Python. We’ll explore common file operations using the os module, and see how the pathlib module provides an object-oriented way to handle file paths. We’ll also use the glob module for pattern-based file searches and learn file I/O operations for text, CSV, and binary files. Additionally, we’ll introduce the calendar and time modules to work with dates and timestamps. Finally, an interactive lab will tie everything together by automating a folder backup and cleanup task. Follow the step-by-step sections below for each subtopic, try out the code examples, and explore the guided lab at the end.
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Lesson 1: The os module: navigating, creating, renaming, deleting
Lesson 2: The pathlib Module for Object-Oriented File Paths
Lesson 3: Using glob for Pattern-Based File Matching
Lesson 4: Reading and Writing Text Files
Lesson 5: Working with CSV files via the csv module
Lesson 6: Introduction to the calendar and time Modules for Timestamps
Summary
Quiz: Working with Files and Folders
Lab Activity – Automate Backup and Cleanup of a Target Folder
Module 8 – Error Handling and Debugging Techniques
In this lesson, we will learn how to handle errors in Python programs and how to debug code effectively. Errors are inevitable, but knowing how to manage them ensures our programs don't crash unexpectedly. We will cover the difference between syntax errors and exceptions, how to use try, except, else, and finally blocks to catch and handle exceptions, and how to raise your own exceptions (including creating custom exception classes). We’ll also explore debugging strategies: using simple print statements or the logging module to trace your program’s execution, and using Python’s interactive debugger pdb to step through code. By following best practices for error handling and debugging, you can write resilient, maintainable code. Throughout this lesson, try the examples and exercises to practice these techniques.
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Lesson 1: Exceptions vs. errors: understanding tracebacks
Lesson 2: try, except, else, and finally blocks
Lesson 3: Catching specific exceptions and using generic handlers
Lesson 4: Raising exceptions and creating custom exception classes
Lesson 5: Debugging with print statements, logging module setup
Lesson 6: Debugging exercise: locate and fix bugs in a sample script
Summary
Quiz: Error Handling and Debugging Techniques
Lab Activity : Practice Error Handling and Debugging
Day 4 Summary
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Day 4 – Recording
Module 9 – Automating Excel and PDFs with Python
In this lesson, you will learn how to automate common communication and reporting tasks using Python. We will cover sending notifications via email, messaging platforms, and SMS, as well as manipulating Excel spreadsheets and PDF files programmatically. Each section below includes step-by-step explanations, code examples, and interactive exercises to reinforce your understanding. By the end of this lesson, you’ll be able to send emails with attachments, integrate with Slack/Microsoft Teams, send SMS alerts, and automate Excel/PDF workflows.
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Lesson 1: Reading Text from PDF Files
Lesson 2: Merging Multiple PDF Files
Lesson 3 : Reading & Writing Excel Files with openpyxl
Lesson 4: Modifying Excel Files
Lesson 5 : Automating Excel Formulas
Lesson 6 : Generating an Excel Report
Summary
Quiz: Automating Excel and PDFs with Python
Lab Activity : Automating Excel with Python
Day 5 Summary
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Day 5 – Recording
Module 10 – Automating Email and Notifications
Automating email notifications with Python lets you generate and dispatch messages automatically, saving time and cutting down on mistakes. In this lesson, you will learn how to compose emails, add attachments and send them through an SMTP server—all with just a few lines of code.
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Lesson 1: Introduction to Automating Email Notifications
Lesson 2: What You Will Need
Lesson 3: Import Required Libraries
Lesson 4: Securely Load Credentials
Lesson 5: Compose the Email
Lesson 6: Attach a File to the Email
Lesson 7: Handling attachments and inline images with MIME types
Lesson 8: Send the Email
Lesson 9: Real-World Scenario: Bulk Personalisation
Lesson 10: Best Practices
Quiz: Automating Email Notifications
Lab: Automating Email with Attachments and Inline Images
Summary
Mini Project: Build your own Automation Tool
The project incorporates two common automation tasks – Contact Management and Student Tasks Tracking
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Building a Modular Contact Manager in Python
Student Task Tracker
Day 6 Summary
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Day 6 Recording
Introduction to Python Programming (Copy 1)

Identifying a Repetitive, Manual Task to Automate

Identifying a Repetitive, Manual Task to Automate:

The first step is to pick a task that is repetitive and done manually. Good candidates are tasks you perform frequently (daily or weekly) that follow a consistent process and don’t require complex human judgment. Automating such tasks can lead to immediate gains in productivity and accuracy. Common examples include:

  • File Management: Renaming or organizing batches of files (e.g., renaming hundreds of photos with a consistent prefix).

  • Data Entry or Cleanup: Copying data between systems, formatting spreadsheets, or merging text files.

  • System Maintenance: Clearing logs, backing up files, or running daily reports at a set time.

  • Communication: Sending routine emails or notifications based on a template.

  • Web Actions: Downloading a set of files from a website, or scraping data (like checking prices or news headlines) on a schedule.

Why these tasks? They often involve repeating the same steps over and over, which is exactly what computers excel at. For example, renaming files one by one is tedious, but a short script can handle it in seconds. As another example, manually sorting files into folders is error-prone, but a script can reliably do it every time, reducing human error and saving effort.

Key Characteristics of an Automation-Worthy Task:

  • High Frequency: The task is performed many times (e.g., daily, hourly) or on many items at once (bulk operations).

  • Predictable Steps: The process is the same each time, or follows a set pattern without needing creative decision-making.

  • Low Complexity: It’s straightforward, though possibly time-consuming. If a task is extremely complex or requires nuanced judgment calls, full automation might not be suitable (or might require advanced AI, which is beyond our scope).

  • Error-Prone or Boring: Tasks that are dull for humans often lead to mistakes or fatigue. Automating ensures consistency and frees you from drudgery.

  • Clear Benefit: Automation should meaningfully save time or reduce errors. A task taking you hours each week but only minutes by a script is a clear win.

For instance, suppose every week you manually sort downloaded files into folders by file type – images to an “Images” folder, documents to a “Docs” folder, etc. This involves checking each file’s extension and moving it. This is an ideal candidate to automate with a script that loops through files and uses conditionals to decide where each file goes.

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