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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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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Basic Command for Command prompt, PowerShell, Zsh(macOS)
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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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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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Day 1 Summary
We covered Modules 1, 2 & Module 3 (Lesson 1 & 2)
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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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Day 2 Summary
Summary for Day 21 Aug 2025
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Day 3 Summary
Summary of Day 28 Aug 2025
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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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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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Day 4 Summary
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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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Day 5 Summary
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Mini Project: Build your own Automation Tool
The project incorporates two common automation tasks – Contact Management and Student Tasks Tracking
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Day 6 Summary
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Introduction to Python Programming (Copy 1)

Return values and early exits

Most functions perform some operations and then return a value back to the caller. In Python, this is done using the return statement. When a return statement is executed, the function terminates immediately at that point and the specified value (if any) is given back to the caller. Any code after a return in the function body will not run, since the function has already exited.

If a function has a return <expression>, it will evaluate the expression and send that result back to the caller.

If a return statement has no expression (just return by itself), or if the function reaches the end without encountering any return statement, the function returns the special value None by default. None in Python represents the absence of a value. This is why by default Python functions that don’t explicitly return anything will output None if you try to capture their return value.

Basic Example of Return:

def add(a, b):
    return a + b

sum_val = add(3, 4)
print(sum_val)   # Outputs: 7

Here, return a + b exits the function add and sends the sum of a and b back to where the function was called. We store that in sum_val, which becomes 7.

Early Exit with Return: You can use return to exit a function early under certain conditions. This is often used for input validation or breaking out of a function when a result is known partway through. For example:

def check_positive(x):
    if x < 0:
        print("Number is negative, stopping early.")
        return        # exit the function early, implicitly returns None
    print("Number is positive or zero.")
    return x

Incheck_positive, if x is negative, the function prints a message and executes return with no value, causing an immediate exit (and yielding None). If x is not negative, the function proceeds to print the second message and then returns x. We can see how return allows us to skip the rest of the function when a certain branch of logic is taken. Using early returns can make code cleaner by handling “edge cases” or guard conditions upfront.

Returning Multiple Values: Python has a convenient feature where you can return multiple values from a function by separating them with commas. For example:

def get_user():
    first_name = "Jane"
    last_name = "Doe"
    age = 30
    return first_name, last_name, age

Here, get_user() Returns three values at once. In reality, Python packs these into a tuple and returns the tuple. You could capture the returned tuple and index it, or use tuple unpacking:

fname, lname, age = get_user()

After this call, fname will be "Jane", lname will be"Doe", and age will be 30. This can be convenient for functions that naturally produce multiple related results.

No Explicit Return: If you don’t include any return statement in a function, Python will automatically return None when the function finishes executing the last statement in its block. For instance:

def say_hello(name):
    print("Hello,", name)

result = say_hello("world")
print(result)   # Outputs: None

The function say_hello prints a greeting but does not return anything, so when we call it, result gets the value None. Many functions are used for their side effects (like printing or modifying a global variable) and thus return None.

Key Point: The return keyword is used to terminate a function’s execution and optionally send back a value. Once a return is executed, the function is done. If you need to stop execution early under certain conditions, you can place a condition return inside an if or other control structure. This “early return” pattern is common for input validation: check for an invalid case and return early to avoid doing the normal processing on bad data.

In summary, understanding how to use return allows you to both retrieve results from functions and control the flow of your function’s execution.