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)

Catching Specific vs. Generic Exceptions

When handling exceptions, it’s usually best to catch only the exceptions you expect and know how to handle. Catching exceptions specifically means naming the exception type in the except clause, like except ValueError: or except ZeroDivisionError:. This way, you handle the error appropriately for that particular situation. For example, if you expect a ValueError from converting input to int, you catch that and perhaps prompt the user again, but you wouldn’t want to catch a KeyboardInterrupt or a MemoryError the same way.

You can have multiple except blocks after a single try to handle different exceptions separately:

try:
    num = int(input("Enter a number: "))
    result = 100 / num
except ValueError:
    print("That's not a valid integer.")
except ZeroDivisionError:
    print("You entered 0, division is not possible!")

In this snippet:

  • If the int() conversion fails, it raises ValueError and the first except runs.

  • If the conversion succeeds but the division fails (num was 0), the second except runs.

Order matters: Python will check each except in turn and execute the first one that matches the raised exception. Therefore, list more specific exceptions first, and more general catches later. If we had an except Exception: at the top, it would catch everything, even the ValueError and ZeroDivisionError, and those specific handlers below would never run.

Generic exception handlers:
Using except Exception: or the even broader except: (which catches all exceptions including system-exiting ones like KeyboardInterrupt) is generally not recommended unless you have a very good reason. A generic handler might be useful to log an unexpected error or to ensure some cleanup, but it can also catch errors you didn’t anticipate, potentially masking bugs. If you do use a generic catch-all, it’s good practice to at least log or display the error so it’s not swallowed silently, and possibly re-raise it after handling if you can’t fully resolve it.

For example:

try:
    # some code
except Exception as err:
    print(f"Unexpected error: {err}")  # Log the error details
    raise  # re-raise the exception to not suppress it

This will catch any exception, print a message with the error, then re-raise it so it can propagate or be caught elsewhere. The re-raise is done by just calling raise with no argument inside an except block, which throws the same exception again.

When to use generic handlers:
Sometimes in a top-level program loop you might catch Exception so that your program can continue running (or to fail gracefully) instead of crashing. In those cases, handle what you can (like logging or cleaning up) and consider exiting or re-raising after logging, so the error isn’t hidden. Avoid patterns like:

except Exception:
    pass

which completely ignore errors – this makes bugs very hard to find because the program will fail silently.

Catching multiple exception types in one block:
You can have a single except handle multiple exception types by using a tuple, e.g.:

try:
    # ...
except (TypeError, ValueError) as e:
    print("Invalid input:", e)

This except will catch either a TypeError or a ValueError and handle them in the same way.

Summary:
Prefer specific exceptions in except clauses – it’s a best practice to only catch what you can actually handle. Use broad except Exception as a fallback or for logging, and avoid the bare except: (which even catches things like KeyboardInterrupt or SystemExit that you usually shouldn’t interfere with). Being specific prevents hiding unexpected errors and makes your code’s error handling intentions clear.