Full Python Course: Zero to Hero
Course Curriculum
7 modules designed to master the subject.
Module 1: Programming Fundamentals with Python
Learn core programming concepts using Python, including variables, data types, operators, and control flow.
Lesson 1: What Programming Is and Why Python
An absolute beginner's guide to understanding programming and why Python is the perfect first language.
Lesson 2: Installing Python and Setting Up the Environment
A step-by-step guide to installing Python and setting up your coding environment correctly.
Lesson 3: Running Your First Python Program
Learn how to create, save, and execute your first Python script using VS Code.
Lesson 4: Variables and Data Types
Understand how Python stores and manages information using variables and different data types.
Lesson 5: Input and Output
Make your Python programs interactive by learning how to accept user input and format output.
Lesson 6: Operators and Expressions
Master the tools of logic and math in Python, from addition and subtraction to complex comparisons.
Lesson 7: Conditional Statements
Give your programs the power to make decisions using if, elif, and else statements.
Lesson 8: Loops
Master automation by learning how to repeat code segments using while and for loops.
Lesson 9: Writing Simple Programs
Learn the art of problem-solving and how to combine variables, logic, and loops into functional Python scripts.
Lesson 10: Common Beginner Mistakes
Save time and frustration by learning how to identify and fix the most common Python errors made by beginners.
Lesson 11: Module 1 Hands-on Projects
Apply everything you've learned in Module 1 by building four practical Python projects from scratch.
Module 1: Exercises and Quiz
Test your knowledge of Python fundamentals with these practical exercises and a module-wide quiz.
Module 2: Data Structures and Algorithms
Understand lists, tuples, dictionaries, sets, and basic algorithms to write efficient Python code.
Module 2 Lesson 1: Introduction to Data Structures
Why data structures matter and how they help us organize information for efficiency and speed.
Module 2 Lesson 2: Lists
Master Python's most versatile data structure: the List. Learn indexing, slicing, and manipulation techniques.
Module 2 Lesson 3: Tuples
Discover the power of immutability. Learn what Tuples are, how to use them, and why they are faster than Lists.
Module 2 Lesson 4: Dictionaries
Learn to store data using meaningful labels. Master Python dictionaries, key-value pairs, and nested data structures.
Module 2 Lesson 5: Sets
Master unique collections in Python. Learn how Sets work, how they handle duplicates, and how to use Set operations like Union and Intersection.
Module 2 Lesson 6: Choosing the Right Data Structure
A practical guide to picking the perfect container for your data. Learn through real-world scenarios and a visual decision matrix.
Module 2 Lesson 7: Introduction to Algorithms
Demystifying algorithms. Learn what they are, how they work with data structures, and why they are the foundation of smart software.
Module 2 Lesson 8: Searching Techniques
Learn how computers find data efficiently. Compare Linear Search and Binary Search and see why sorted data is a superpower.
Module 2 Lesson 9: Sorting Techniques
Learn how computers put data in order. Explore Bubble Sort for understanding logic and Python's sorted() for real-world efficiency.
Module 2 Lesson 10: Time Complexity Basics (Big O)
Learn how to measure the efficiency of your code. Understand Big O notation and why it matters for scaling software.
Module 2 Lesson 11: Hands-on Projects
Put your data structure and algorithm skills to work. Build a Contact Book, a Grade Tracker, and an Inventory System.
Module 3: Functions and Modular Programming
Learn how to write reusable functions, work with parameters, return values, and organize code into modules.
Module 3 Lesson 1: Why Functions Matter
Stop repeating yourself. Learn how functions make your code modular, readable, and easier to debug.
Module 3 Lesson 2: Defining and Calling Functions
Master the syntax of functions in Python. Learn how to use the 'def' keyword, the importance of colons, and how to execute your code.
Module 3 Lesson 3: Parameters and Return Values
Turn your functions into powerful processing engines. Learn how to pass data into functions and get results back using return.
Module 3 Lesson 4: Default and Keyword Arguments
Make your functions more flexible. Learn how to provide default values and how to pass arguments using their names instead of just their position.
Module 3 Lesson 5: Variable Scope
Understand where your variables live and die. Learn the difference between Global and Local scope to avoid accidental bugs.
Module 3 Lesson 6: Writing Reusable Code
Going from 'it works' to 'it's good.' Learn the best practices for writing functions that are reusable, testable, and clean.
Module 3 Lesson 7: Importing Modules
Don't reinvent the wheel. Learn how to use Python's 'import' system to tap into thousands of pre-written functions and libraries.
Module 3 Lesson 8: Creating Custom Modules
Take control of your project structure. Learn how to split your code into multiple files and build your own reusable Python libraries.
Module 3 Lesson 9: Python Standard Library
Explore the 'Batteries Included' philosophy of Python. Learn about the most useful built-in modules for everything from math to data networking.
Module 3 Lesson 10: Refactoring Code
The art of cleaning up your code. Learn how to identify 'code smells' and rewrite your logic to be more readable without changing how it works.
Module 3 Lesson 11: Hands-on Projects
Build a professional Utility Library. Apply everything you've learned about functions, modules, and refactoring to create a suite of reusable tools.
Module 4: Object-Oriented Programming in Python
Explore classes, objects, inheritance, encapsulation, and polymorphism to build structured Python applications.
Module 4 Lesson 1: Intro to OOP (Thinking in Objects)
Change the way you solve problems. Learn the philosophy of Object-Oriented Programming and why it’s the secret to building massive apps.
Module 4 Lesson 2: Classes and Objects
From theory to code. Learn how to define a Class in Python, create Objects from it, and use dot notation to access data.
Module 4 Lesson 3: The __init__ Method
Master the constructor. Learn how to use the special __init__ method and the 'self' keyword to initialize objects with data.
Module 4 Lesson 4: Instance Attributes vs. Methods
Differentiate between an object's state and its behavior. Learn how to define methods, use the self keyword for interaction, and update attributes dynamically.
Module 4 Lesson 5: Encapsulation and Data Hiding
Protect your data. Learn how to use 'private' attributes to prevent accidental changes and ensure your objects follow their own rules.
Module 4 Lesson 6: Inheritance: Reusing Code
Don't repeat yourself in OOP. Learn how to create Parent and Child classes to share attributes and methods across your entire application.
Module 4 Lesson 7: Polymorphism and Method Overriding
Same action, different behavior. Learn how to override parent methods and use polymorphism to write flexible and powerful code.
Module 4 Lesson 8: Composition vs. Inheritance
Choosing the right way to build complex systems. Learn the 'is-a' vs. 'has-a' rule and why Composition is often better than Inheritance.
Module 4 Lesson 9: Class and Static Methods
Not everything needs an instance. Learn how to use @classmethod and @staticmethod for shared logic and utility functions.
Module 4 Lesson 10: Modularizing OOP Projects
Organize your code like a professional. Learn how to split complex classes into different modules and use __init__.py for clean imports.
Module 4 Lesson 11: Hands-on Projects
Building an RPG Combat System. Apply inheritance, encapsulation, and polymorphism to build a modular game engine from scratch.
Module 5: File Handling and Error Management
Work with files, read and write data, handle exceptions, and build more reliable Python programs.
Module 5 Lesson 1: Reading Text Files
Give your data a permanent home. Learn how to open, read, and process information from text files using Python's built-in tools.
Module 5 Lesson 2: Writing and Appending to Files
Save your data. Learn how to create new files, overwrite existing ones, and safely add new information to the end of a file.
Module 5 Lesson 3: Working with CSV Data
Process spreadsheets like a pro. Learn how to use Python's built-in csv module to read and write structured data for analysis.
Module 5 Lesson 4: Handling JSON Files
Master the language of the web. Learn how to parse JSON data into Python dictionaries and save complex objects into JSON files.
Module 5 Lesson 5: The with Statement (Context Managers)
Why clean code matters. Deep dive into Python context managers and how the 'with' statement automates resource cleanup and prevents memory leaks.
Module 5 Lesson 6: Handling Exceptions (Try-Except)
Stop your code from crashing. Learn how to catch errors gracefully and provide helpful feedback instead of the 'Red Screen of Death'.
Module 5 Lesson 7: Specific vs. Generic Exceptions
Precision error handling. Learn why catching 'every' exception is risky and how to target specific errors like FileNotFoundError and ZeroDivisionError.
Module 5 Lesson 8: Raising Custom Exceptions
You're the boss. Learn how to use the 'raise' keyword to stop your program when your own business rules are broken, even if Python doesn't think it's an error.
Module 5 Lesson 9: Finally and Else Blocks
The complete error handling loop. Learn how to use 'else' for success and 'finally' for guaranteed cleanup, ensuring your program leaves no loose ends.
Module 5 Lesson 10: Logging and Debugging Files
Keep a paper trail. Learn how to use Python's logging module to record errors and system events into files, making debugging much easier.
Module 5 Lesson 11: Hands-on Projects
Build a Secure Data Archive. Combine JSON handling, file system operations, and error management to create a professional data storage system.
Module 6: Python for Data Science
Get introduced to data analysis using Python with libraries like NumPy and Pandas.
Module 6 Lesson 1: Intro to Data Science with Python
Turn numbers into insights. Learn why Python is the #1 language for Data Science and get an overview of the ecosystem including NumPy, Pandas, and Matplotlib.
Module 6 Lesson 2: NumPy: Arrays and Vectorization
Meet the foundation of numerical Python. Learn how NumPy arrays differ from lists and how 'vectorization' makes your math 100x faster.
Module 6 Lesson 3: NumPy Math and Stats
Harness the statistical power of NumPy. Learn how to calculate means, medians, and standard deviations with one line of code across millions of data points.
Module 6 Lesson 4: Pandas Basics: DataFrames and Series
Welcome to the DataFrame. Learn about the two core building blocks of Pandas and how to navigate tables like a data professional.
Module 6 Lesson 5: Loading Data with Pandas
Unlock your data. Learn how to import real-world information from CSV, Excel, and JSON files into your Python scripts for analysis.
Module 6 Lesson 6: Data Cleaning and Manipulation
The secret to accurate analysis. Learn how to handle missing data, drop duplicates, and transform your columns using powerful Pandas techniques.
Module 6 Lesson 7: Grouping and Aggregation
Summarize your data. Learn how to use 'groupby' to find totals, averages, and counts across different categories in your dataset.
Module 6 Lesson 8: Matplotlib: Basic Plotting
Visualize your data. Learn the fundamentals of Matplotlib to create line charts, bar graphs, and scatter plots that turn numbers into stories.
Module 6 Lesson 9: Seaborn: Statistical Visualization
Beautiful data at your fingertips. Learn how to use Seaborn to create high-level statistical graphics like heatmaps and violin plots with minimal code.
Module 6 Lesson 10: Exploratory Data Analysis (EDA) Project
The data detective's guide. Follow a step-by-step Exploratory Data Analysis (EDA) to find trends, handle outliers, and visualize insights in a real-world dataset.
Module 6 Lesson 11: Hands-on Projects
Master real-world data science. Choose from three advanced projects: Stock Market Tracking, Weather Pattern Analysis, or a Personal Habit Tracker.
Module 7: Introduction to AI and Machine Learning
Learn the fundamentals of AI and machine learning concepts using Python with simple, practical examples.
Module 7 Lesson 1: What is Artificial Intelligence?
Demystifying the hype. Learn what AI actually is, the difference between Rule-Based systems and Learning systems, and why Python is the driving force of the AI revolution.
Module 7 Lesson 2: Machine Learning Basics (Supervised vs. Unsupervised)
How computers learn. Explore the two main pillars of machine learning: learning with a teacher (Supervised) and finding patterns on your own (Unsupervised).
Module 7 Lesson 3: Scikit-Learn: The ML Toolkit
Meet your AI engine. Learn the consistent interface of Scikit-Learn and master the four patterns: Import, Instantiate, Fit, and Predict.
Module 7 Lesson 4: Linear Regression: Predicting Numbers
Your first predictive model. Learn how to use Linear Regression to find the 'Line of Best Fit' and predict numerical values like prices and temperatures.
Module 7 Lesson 5: Logistic Regression: Classification
Binary decisions made simple. Learn how to use Logistic Regression to categorize data into 'Yes or No' classes like Spam vs. No Spam or Pass vs. Fail.
Module 7 Lesson 6: Decision Trees and Random Forests
Branching out into AI logic. Learn how Decision Trees make choices like a human and how 'Random Forests' combine a hundred trees to create a genius-level model.
Module 7 Lesson 7: Evaluating Models (Accuracy, Precision, Recall)
Is your AI actually good? Learn how to look beyond 'Accuracy' and understand 'Precision' and 'Recall' to ensure your model isn't missing critical patterns.
Module 7 Lesson 8: Building a Spam Filter Project
AI in action. Build a fully functional SMS/Email spam filter using the Naive Bayes algorithm and learn how computers process human language.
Module 7 Lesson 9: Real-world AI: Ethics and Bias
With great power comes great responsibility. Explore the vital topics of algorithmic bias, data privacy, and the ethical dilemmas facing AI developers today.
Module 7 Lesson 10: The Future of Python and AI
The journey is just beginning. Explore the cutting-edge trends in Large Language Models (LLMs), Generative AI, and how Python continues to evolve as the heart of tech.
Module 7 Lesson 11: Hands-on Projects
Apply your AI skills. Build a Real Estate Price Predictor, a Customer Segmenter, or a Sentiment Analysis tool using Scikit-Learn.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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