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)

Debugging Example – Finding the Maximum in a List

Below is a Python script that is supposed to find the maximum value in a list of numbers.
However, it contains a bug (or maybe a couple of bugs).
Your task is to run this code, observe what goes wrong, and fix the issues step by step.

def find_maximum(values):
    max_val = 0
    for val in values:
        if val > max_val:
            max_val = val
    return val  # Bug likely here

nums = [4, 1, 9, 3]
print("Max of", nums, ":", find_maximum(nums))

empty_list = []
print("Max of empty list:", find_maximum(empty_list))

Step 1 – Observe the Error

Running the script produces:

Max of [4, 1, 9, 3] : 3
Traceback (most recent call last):
  File "buggy_script.py", line 8, in <module>
    print("Max of empty list:", find_maximum(empty_list))
  File "buggy_script.py", line 5, in find_maximum
    return val
UnboundLocalError: local variable 'val' referenced before assignment

Observation:

  1. The first list returns 3 instead of 9 (logic problem).

  2. An UnboundLocalError occurs when calling with an empty list.

Step 2 – Investigate the Logic for Non-Empty List

  • The loop correctly updates max_val when a larger value is found.

  • But at the end, the function returns val, which is just the last loop value, not the maximum.

  • For [4, 1, 9, 3], the loop ends with val = 3, so it returns 3.

Step 3 – Understand the Empty List Issue

  • For [], the loop never runs.

  • val is never assigned, so return val fails with UnboundLocalError.

  • Even if we returned max_val instead, starting max_val = 0 could be wrong if all values are negative.

Step 4 – Fix the Bugs

  1. Return max_val instead of val.

  2. Handle empty list by raising an exception.

  3. Initialize max_val with the first element of the list to handle negative numbers.

Revised Function:

def find_maximum(values):
    if len(values) == 0:
        raise ValueError("Cannot find max of an empty list")
    max_val = values[0]
    for val in values:
        if val > max_val:
            max_val = val
    return max_val

Step 5 – Testing the Fix

nums = [4, 1, 9, 3]
print("Max of", nums, ":", find_maximum(nums))

empty_list = []
try:
    print("Max of empty list:", find_maximum(empty_list))
except ValueError as e:
    print("Error:", e)

Expected Output:

Max of [4, 1, 9, 3] : 9
Error: Cannot find max of an empty list

Step 6 – Reflection

  • Bug #1: Returned the wrong variable (val instead of max_val).

  • Bug #2: No handling for empty input.

  • Fixing logic and adding input validation makes the function more robust.

  • This debugging process involved:

    • Observing the traceback.

    • Checking variable states.

    • Updating logic to avoid hidden edge case failures.

 

Exercise Files
try_InspectingExceptionDetails.zip
Size: 378.00 B