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)

Raising Exceptions

Sometimes you might want to trigger an exception intentionally in your code. This is done with the raise statement. You raise exceptions to signal that an error condition has occurred that should be handled by the caller or at a higher level. For example, if a function gets an argument value that is invalid and it cannot continue, it can raise a ValueError to indicate this:

def square_root(x):
    if x < 0:
        raise ValueError(f"Cannot compute square root of negative number: {x}")
    # continue with normal computation
    import math
    return math.sqrt(x)

In this function, we check for a negative input. Instead of returning an error code, we raise a ValueError with a descriptive message. The raise keyword can be used with either an exception instance or an exception class. For instance, raise ValueError("message") creates a ValueError exception with that message and raises it. If you use raise ValueError (providing a class), Python will instantiate it for you (essentially the same as raising ValueError() with no arguments).

When you raise an exception, normal execution stops and the exception propagates up to the nearest try/except that can handle that exception type. If none is found, it will terminate the program and show a traceback.

Creating custom exceptions
Python allows you to define your own exception types by creating a class that inherits from the built-in Exception class (or one of its subclasses). Custom exceptions are useful to represent errors that are specific to your application or domain, making them clearer and allowing callers to catch your specific errors separately if needed.

Creating a custom exception is straightforward:

class InvalidOperationError(Exception):
    """Custom exception for invalid operations."""
    pass

This defines a new exception type InvalidOperationError. By convention, custom exception class names end in “Error” to make it obvious they’re exceptions. We inherit from Exception which is the base class for most regular exceptions. We don’t need to add any special behavior – often a simple pass is enough, unless you want to override the __str__ or add attributes.

You can now use raise InvalidOperationError("Detailed message") to throw this error. Anyone catching exceptions can catch it specifically with except InvalidOperationError:.

Example: Using a custom exception

class NegativeNumberError(Exception):
    pass

def compute_log(x):
    if x < 0:
        raise NegativeNumberError("Cannot take logarithm of a negative number")
    import math
    return math.log(x)

try:
    compute_log(-5)
except NegativeNumberError as e:
    print("Error:", e)

In this example, compute_log(-5) will raise our NegativeNumberError. The except NegativeNumberError catches it and prints the error message. If we didn’t catch it, we’d see a traceback just like with built-in exceptions, and the type would be __main__.NegativeNumberError with our message.

Custom exceptions let you differentiate error causes. For instance, a function might raise NegativeNumberError for negatives, ZeroDivisionError for divide-by-zero (built-in), etc., and the caller can handle these separately.

Raising exceptions in except blocks (exception chaining)
If you catch an exception and decide to raise a different exception in response, Python will by default attach the original exception as context, so you don’t lose the traceback of what happened first. You can also explicitly chain exceptions with raise NewException(...) from exc. For example:

try:
    ...  # some operation
except FileNotFoundError as e:
    raise RuntimeError("Failed to process data") from e

This will raise a RuntimeError but the traceback will show that a FileNotFoundError was the direct cause. This can be useful to abstract lower-level errors into higher-level exceptions without losing the original info. If for some reason you want to suppress the context, you can raise from None, but that’s less common.

Key points:

  • Use raise to trigger exceptions when needed (like to enforce an error condition).

  • Create custom exception classes by subclassing Exception for clearer, domain-specific errors.

  • When raising exceptions in your code, include an informative message to make debugging easier.

  • Know that raising an exception interrupts function execution; any code after a raise in that function won’t run unless the exception is caught somewhere above.