Course Curriculum & Learning Progression
The Code with Python curriculum at Axilearn is hands-on, project-based learning aligned with NEP 2020. Each level builds on the one before:
- Level 1: programming fundamentals through games and Turtle graphics
- Level 2: functions, data structures, files, object-oriented programming and GUI apps
- Level 3: data analysis, automation, web data and machine learning, ending in an end-to-end capstone
LMS Video Format:
Students can join anytime. They get on-demand video tutorials, coding exercises, self-check quizzes and downloadable starter code on the Axilearn LMS.
Structured Level Breakdown
Level 1
OPEN & FREE LEVEL
Py Spark 🐍
Python syntax, variables, input and output, conditions, loops and lists, taught through Turtle graphics art and text-based games.
Prerequisites: None
Tools: Python 3, Thonny IDE (or LMS sandbox)
Topics Covered:
- Getting Started: installing Python and Thonny, your first program, print(), and comments
- Variables & Data Types: int, float, str and bool, plus naming rules
- Input & Type Conversion: input(), int() and float(), and building interactive programs
- Operators: arithmetic, comparison and logical operators
- Strings: f-strings, indexing, slicing and common string methods
- Decisions: if / elif / else and nested conditions
- Loops: for with range(), while, break and continue
- Lists Basics: creating, adding, removing and looping through lists
- The random Module: randomness in games
- Turtle Graphics: shapes, colours, patterns and loop art
- Debugging: reading error messages and fixing common mistakes
Mini Projects:
Personal Greeter · Simple Calculator · Number Guessing Game · Times-Table Generator · Turtle Rainbow Spiral · Rock-Paper-Scissors
Project Output:
Text Adventure Quest, a branching story game with player choices, an inventory list, random events, and a score
Level 2
SESSIONS WITH INSTRUCTOR
Py Craft 🛠️
Writing organised, reusable code: functions, modules, lists, tuples, sets and dictionaries, file handling, error handling, object-oriented programming, basic algorithms, and desktop GUI apps with Tkinter.
Prerequisites: Py Spark, or Python basics
Tools: Python 3, Thonny or VS Code
Topics Covered:
- Functions: parameters, return values, default arguments and variable scope
- Modules: import, math, random and datetime, creating your own modules, and installing packages with pip
- Lists in Depth: slicing, list methods, nested lists and list comprehensions
- Tuples & Sets: immutability, unique values and set operations
- Dictionaries: key–value data, nested dictionaries and dictionary methods
- String Processing: split(), join() and text-cleaning techniques
- File Handling: reading and writing text and CSV files
- Error Handling: try / except / else / finally and input validation
- Object-Oriented Programming: classes, objects, __init__, methods and inheritance
- Algorithms: linear and binary search, bubble sort, and efficiency basics
- GUI Apps: windows, buttons, entries and events with Tkinter
Mini Projects:
Password Generator & Strength Checker · Hangman · Contact Book (saved to file) · Student Report Card from CSV · Bank Account Simulator (OOP) · To-Do List App (Tkinter)
Project Output:
School Library Manager, a Tkinter desktop app built with OOP to add, search, issue and return books, with all data saved to CSV files
Level 3
SESSIONS WITH INSTRUCTOR
Py Mind 🧠
Python for real-world data and AI: NumPy, Pandas, Matplotlib, APIs and JSON, web scraping, automation scripts, and machine learning with scikit-learn, ending in an end-to-end data science capstone.
Prerequisites: Py Craft, or intermediate Python
Tools: Jupyter Notebook / Google Colab, VS Code, Git & GitHub
Topics Covered:
- Working in Notebooks: Jupyter and Google Colab workflows
- NumPy: arrays, vectorised operations and basic statistics
- Pandas: DataFrames, reading CSV/Excel files, cleaning data, filtering, groupby and merging
- Data Visualisation: line, bar, scatter and histogram charts with Matplotlib and Seaborn
- APIs & JSON: fetching live data with requests
- Web Scraping: BeautifulSoup basics and ethical scraping (robots.txt, terms of use)
- Automation: organising files with os and shutil, and generating Excel reports with openpyxl
- Intro to Machine Learning: the ML workflow, features and labels, and train/test split
- Regression & Classification: Linear Regression, Decision Trees and k-NN with scikit-learn
- Evaluating Models: accuracy, confusion matrix and overfitting
- Version Control: Git and GitHub basics, plus publishing a project portfolio
- Responsible AI & Data Ethics: bias, privacy and data sources
Mini Projects:
Exam Results Analyser · Live Weather Dashboard (API) · News Headline Scraper · Smart File Organiser Bot · House Price Predictor · Iris Flower Classifier
Project Output:
Data-to-Insight Capstone, an end-to-end project that collects real data (CSV or API), cleans it, visualises the findings and builds an ML prediction model. Example topics include a City Air Quality Predictor, a Student Performance Predictor and a Crop Yield Forecaster. Learners submit it as a Jupyter notebook published on GitHub.
Course Summary
Target Age:
Ages 10–18 / Diploma
Levels:
3 Levels: Py Spark → Py Craft → Py Mind
Total Duration:
30 Weeks (~60 hours)
Format:
Self-Paced Video + Practice
Tools:
Python 3, Thonny, VS Code, Colab
Requirements:
Laptop/Desktop & Internet
Certificate:
Axilearn Certified (Each Level)
Access:
Level 1 Free on LMS
Key Highlights:
- Level 1 video modules are free on the Axilearn LMS
- A clear path from first program to machine learning
- Games, Turtle art, GUI apps and automation scripts
- Object-oriented programming and core algorithms
- Data analysis with NumPy, Pandas and Matplotlib
- Machine learning foundations with scikit-learn
- 18 mini projects and 3 level-end projects, with a GitHub portfolio
