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Basic Python Course
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Basic Python Course

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Learn Python from the ground up and start writing real programmes fast. This course takes you from installation to object-oriented programming, covering data structures, file handling, automation, and more. Whether you are switching careers or adding a powerful skill to your toolkit, this is the Python foundation you need.

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What your team will master:

You will learn how to set up Python, write and run scripts, and work with core data types including strings, numbers, and booleans. You will control program logic using loops and conditionals, then organise your code into reusable functions. The course covers lists, dictionaries, sets, and tuples so you can handle any data structure Python throws at you. You will read and write files, handle exceptions, and apply object-oriented programming principles. Supplementary modules introduce data analysis with pandas, visualisation with matplotlib, automation scripting, testing, and version control with Git.

How your team learns in practice Basic Python Course

How your team practises Basic Python Course

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Course content

8 Chapters • 35 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

Python Environment and First Steps

  • Lesson 1 • Installing Python and Tools

    Covers Python installation on major operating systems and selecting an IDE. Establishes the foundation every subsequent chapter depends on.

  • Lesson 2 • Writing and Running Your First Script

    Teaches script creation, saving, and execution from the command line. Connects REPL experimentation to structured, repeatable programmes.

  • Lesson 3 • The Python Interpreter and REPL

    Introduces the interactive interpreter for immediate code execution. Students use the REPL to test expressions before writing full scripts.

  • Lesson 4 • Python Syntax Fundamentals

    Explains indentation rules, comments, and statement structure. Correct syntax habits prevent the most common beginner errors.

Chapter 2See details

Variables, Data Types, and Operators

  • Lesson 1 • Variables and Assignment

    Covers variable naming rules, assignment syntax, and dynamic typing. Provides the data-storage model all later chapters rely on.

  • Lesson 2 • Numeric Types and Arithmetic

    Explores int, float, and complex types alongside arithmetic operators. Students perform calculations and understand operator precedence.

  • Lesson 3 • Type Conversion and Input

    Covers explicit type casting and reading user input with input(). Students build interactive programmes that handle real data.

  • Lesson 4 • Strings and Text Manipulation

    Teaches string creation, indexing, slicing, and common methods. String skills are essential for input/output and data processing tasks.

  • Lesson 5 • Booleans and Comparison Operators

    Introduces True/False values, comparison operators, and logical operators. These concepts directly enable conditional logic in the next chapter.

Chapter 3See details

Control Flow and Decision Making

  • Lesson 1 • Conditional Statements

    Teaches if, elif, and else blocks with proper indentation. Conditional logic is the primary tool for decision-driven programmes.

  • Lesson 2 • While Loops

    Introduces condition-based repetition with while loops and loop control. Students build input-validation and countdown programmes.

  • Lesson 3 • Loop Patterns and Best Practices

    Explores accumulator patterns, enumerate(), and zip() for cleaner loops. These patterns reduce errors and improve code readability.

  • Lesson 4 • For Loops and Iteration

    Covers iterating over sequences and using range(). For loops are the standard Python pattern for processing collections.

Chapter 4See details

Functions and Code Reusability

  • Lesson 1 • Lambda Functions and Higher-Order Functions

    Introduces anonymous functions and passing functions as arguments. These patterns enable concise, functional-style data transformations.

  • Lesson 2 • Parameters and Arguments

    Teaches positional, keyword, default, and variable-length arguments. Flexible argument handling makes functions broadly reusable.

  • Lesson 3 • Recursion Basics

    Covers recursive function design with base cases and recursive steps. Students solve problems like factorial and Fibonacci recursively.

  • Lesson 4 • Scope and Namespaces

    Explains local, enclosing, global, and built-in scopes using the LEGB rule. Scope knowledge prevents subtle variable-shadowing bugs.

  • Lesson 5 • Defining and Calling Functions

    Covers def syntax, function calls, and the return statement. Functions are the primary unit of code organisation in Python.

Chapter 5See details

Data Structures: Lists, Tuples, and Sets

  • Lesson 1 • Lists: Creation and Basic Operations

    Teaches list literals, indexing, slicing, and mutation. Lists are Python's most versatile sequence type and appear in nearly every programme.

  • Lesson 2 • List Methods and Comprehensions

    Covers append, insert, remove, sort, and list comprehensions. Comprehensions replace verbose loops with concise, readable expressions.

  • Lesson 3 • Sets: Unique Collections

    Introduces set creation, membership testing, and set operations. Sets efficiently handle deduplication and mathematical set logic.

  • Lesson 4 • Tuples: Immutable Sequences

    Explains tuple creation, unpacking, and immutability benefits. Tuples are preferred for fixed data and function return values.

Chapter 6See details

Dictionaries and Data Mapping

  • Lesson 1 • Nested Data Structures

    Covers dicts of lists, lists of dicts, and multi-level nesting. Nested structures model real-world data like JSON records and config files.

  • Lesson 2 • Iterating Over Dictionaries

    Teaches iteration with keys(), values(), and items(). Efficient traversal is essential for processing configuration and record data.

  • Lesson 3 • Dictionary Basics

    Covers dictionary creation, key-value access, and mutation. Dictionaries are Python's primary tool for labelled, structured data.

  • Lesson 4 • Dictionary Methods and Comprehensions

    Explores get(), setdefault(), update(), and dict comprehensions. These tools reduce boilerplate and handle missing keys safely.

Chapter 7See details

File Handling and Exceptions

  • Lesson 1 • Reading and Writing Text Files

    Teaches open(), read modes, and the with statement for safe file access. File I/O connects programmes to persistent storage and external data.

  • Lesson 2 • Custom Exceptions and Best Practices

    Teaches defining custom exception classes and when to use them. Custom exceptions make error handling expressive and domain-specific.

  • Lesson 3 • Exception Handling with Try and Except

    Introduces try, except, else, and finally blocks for error management. Proper exception handling prevents crashes and improves user experience.

  • Lesson 4 • Working with CSV Files

    Covers the csv module for reading and writing tabular data. CSV is the most common format for exchanging structured data between tools.

Chapter 8See details

Object-Oriented Programming in Python

  • Lesson 1 • Classes and Objects

    Covers class definition, instantiation, and the __init__ method. Classes are the blueprint for creating structured, stateful objects.

  • Lesson 2 • Methods and Properties

    Teaches instance methods, class methods, static methods, and properties. These tools control how objects expose and protect their data.

  • Lesson 3 • Modules, Packages, and Project Structure

    Teaches importing modules, creating packages, and organising multi-file projects. Good structure makes OOP projects scalable and maintainable.

  • Lesson 4 • Inheritance and Polymorphism

    Explains single inheritance, method overriding, and super(). Inheritance enables code reuse and polymorphic behaviour across related classes.

  • Lesson 5 • Encapsulation and Special Methods

    Covers name mangling, dunder methods, and operator overloading. Special methods integrate custom classes with Python's built-in syntax.

Certification

Your valid completion certificate

This course is for you:

  • Career changer: wants a marketable technical skill to enter the job market.

  • Marketing professional: needs to automate reports and handle data independently.

  • College student: building a programming foundation before entering the workforce.

  • Small business owner: looking to streamline operations through simple custom scripts.

  • Aspiring data analyst: needs Python basics before tackling machine learning tools.

  • Hobbyist: eager to build personal projects like games, bots, or utilities.

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