Advance Python Programming
Course At a Glance
Category
Programming
Level
Advanced
Age Group
15–17 years
Prerequisite
Python Fundamentals (Basic & Intermediate)
Duration
40 Hours
Modules
4 Modules
Program Outcomes
By the end of this course, students will be able to:
- 1
Design and implement structured Python applications using advanced programming concepts and object-oriented principles.
- 2
Apply data structures and algorithmic thinking to analyse and solve complex computational problems efficiently.
- 3
Develop real-world software projects that demonstrate independent coding ability, modular design, and professional programming practices.
Object-Oriented Programming (OOP) Deep Dive
Students move from procedural programming to object-oriented software design. They master classes, objects, instance attributes, methods, inheritance, encapsulation, and polymorphism.
| # | Lesson Title | What Students Learn | Activity / Project | Key Concepts / Syntax |
|---|---|---|---|---|
| 1.1 | Introduction to OOP & Classes | Understand why OOP is used in industry software. Define classes using class, instantiate objects, and access attributes. Contrast procedural vs object-oriented design. | Build: 'Car Showroom' — create a Car class with make, model, year; instantiate 3 car objects and display their details. | class, __init__, self, instance |
| 1.2 | Constructors & Instance Methods | Write __init__() constructors with parameters. Define instance methods that modify object state using self. Understand object lifecycle. | Build: 'Bank Account Class' — methods for deposit(), withdraw(), and get_balance(). Handle negative withdrawals with error messages. | def __init__(self, ...), self.attr |
| 1.3 | Encapsulation & Private Attributes | Protect data using private attributes (_prefix and __prefix). Write getter and setter methods to control how attributes are accessed and updated. | Build: 'Secure User Profile' — private password and balance attributes; validate password strength before updating. | self._attr, self.__attr, getters/setters |
| 1.4 | Single & Multiple Inheritance | Create child classes using class Child(Parent). Inherit attributes and methods. Use super().__init__() to call parent constructors cleanly. | Build: 'RPG Character System' — Character base class; Warrior and Mage subclasses with unique abilities and stats. | class Child(Parent):, super().__init__() |
| 1.5 | Method Overriding & Polymorphism | Override parent methods in child classes. Understand polymorphism — calling the same method name on different object types producing distinct behaviors. | Build: 'Shape Calculator' — Shape base class with area(); Circle, Rectangle, and Triangle subclasses overriding area(). | method overriding, polymorphism |
| 1.6 | Special Methods (Dunder Methods) | Implement dunder methods: __str__(), __repr__(), __len__(), __eq__(), __add__(). Make custom objects behave like native Python types. | Build: 'Custom Vector Class' — implement __add__() and __str__() to allow vector addition v1 + v2. | __str__, __repr__, __len__, __add__ |
| 1.7 | Class vs Instance Variables & Methods | Distinguish between instance variables and class variables shared across all instances. Use @classmethod and @staticmethod decorators. | Build: 'Employee Management System' — class variable tracks total employee count and company name across all instances. | @classmethod, @staticmethod, cls |
| 1.8 | OOP Architecture Review & Refactoring | Review SOLID principles simplified for high schoolers. Refactor a messy procedural script into a clean, modular class hierarchy. | Refactoring Lab: Convert a 200-line procedural game script into 4 interacting classes. | OOP Design & Refactoring |
Advanced Data Structures & Algorithms
Students master advanced data manipulation: stack/queue operations, recursion, searching and sorting algorithms, and basic Big-O time complexity analysis.
| # | Lesson Title | What Students Learn | Activity / Project | Key Concepts / Syntax |
|---|---|---|---|---|
| 2.1 | Stacks & Queues | Implement Stack (LIFO) and Queue (FIFO) data structures using Python lists and collections.deque. Understand push/pop/enqueue/dequeue. | Build: 'Browser History Back Button' (Stack) & 'Print Queue Simulator' (Queue). | list.append(), list.pop(), deque |
| 2.2 | Recursion Fundamentals | Understand recursive functions — functions that call themselves. Identify base cases to prevent infinite recursion and stack overflow. | Exercises: Calculate factorial(n), Fibonacci sequence, and countdown timer recursively. | def f(n): if base: return; return f(n-1) |
| 2.3 | Recursive Problem Solving | Apply recursion to complex problems: string reversal, sum of nested lists, and directory traversal. | Build: 'Recursive File Tree Searcher' — search for a file in subfolders recursively. | recursive calls, call stack |
| 2.4 | Linear vs Binary Search | Implement linear search O(n) and binary search O(log n). Understand why binary search requires a sorted list and compare performance. | Benchmark Lab: Search for a target in a list of 10,000 numbers — compare linear vs binary search execution time. | binary_search(arr, target) |
| 2.5 | Bubble Sort & Selection Sort | Understand basic sorting algorithms. Implement Bubble Sort and Selection Sort step by step. Trace element swaps manually. | Build: 'Visual Step-by-Step Sorter' — print array state after every pass to observe elements bubbling to correct positions. | nested loops, element swapping |
| 2.6 | Intro to Divide & Conquer (Merge Sort) | Explore Merge Sort — how divide-and-conquer splits lists into halves, sorts recursively, and merges them back in O(n log n) time. | Build: Implement merge_sort(arr) and benchmark against Python's built-in sorted(). | divide & conquer, merge_sort() |
| 2.7 | Introduction to Big-O Notation | Understand Big-O time and space complexity: O(1), O(log n), O(n), O(n²). Analyze how execution time scales with input size n. | Analysis Workshop: Classify 6 code snippets by their Big-O complexity. | O(1), O(n), O(n²), O(log n) |
| 2.8 | Algorithm Design Challenge | Combine data structures and algorithms to solve a complex challenge: remove duplicates, find two numbers that sum to a target value (Two-Sum). | Challenge: Solve the Two-Sum problem in O(n) time using a dictionary hash map. | Hash maps, algorithmic efficiency |
Web APIs, JSON & Data Persistence
Students connect Python applications to live internet data. They learn HTTP requests, REST APIs, JSON parsing, environment variables, and persistent data storage.
| # | Lesson Title | What Students Learn | Activity / Project | Key Concepts / Syntax |
|---|---|---|---|---|
| 3.1 | HTTP Basics & REST APIs | Understand how the web works: HTTP requests (GET, POST), URLs, endpoints, headers, and status codes (200, 404, 500). | Lab: Use browser developer tools and API client to inspect raw HTTP responses. | HTTP GET/POST, Status Codes |
| 3.2 | Python Requests Library | Use the third-party requests library (import requests). Send GET requests to live public web APIs and inspect response.status_code and response.text. | Build: 'Public IP & Geo Locator' — fetch client IP and location info from a free API. | import requests, requests.get(url) |
| 3.3 | Parsing JSON Data | Understand JSON data format. Use response.json() and Python's json module to parse nested JSON objects into dictionaries and lists. | Build: 'Live Weather Dashboard' — fetch live weather data for any city and display temperature, humidity, and forecast. | json.loads(), json.dumps(), dict parsing |
| 3.4 | Working with API Parameters & Headers | Pass query parameters in requests.get(url, params={...}) and custom headers. Understand API keys and secure practices. | Build: 'Trivia Quiz App' — fetch 10 random trivia questions dynamically from Open Trivia DB API. | params={...}, headers={...} |
| 3.5 | Error Handling for Web Requests | Handle network failures, timeouts, and HTTP errors using try/except with requests.exceptions.RequestException. | Build: Robust API client with timeout protection, retries, and fallback offline mode. | try / except requests.exceptions |
| 3.6 | JSON File Persistence | Save API data and local data objects permanently to disk as formatted .json files using json.dump() and json.load(). | Build: 'Offline Weather Cache' — save API responses to local JSON files to reduce API calls. | json.dump(data, file), json.load(file) |
| 3.7 | Building a Multi-Source API Tool | Combine data from two independent APIs into a single unified report (e.g. Weather + Currency Exchange Rate). | Build: 'Travel Advisor Tool' — combines city weather data and currency exchange rate for a target country. | Multi-API Integration |
| 3.8 | Module 3 Integration Lab | Package API interaction logic into a clean, reusable Object-Oriented Python class. | Build: WeatherApiClient class with methods get_city_weather() and save_history(). | OOP + API Integration |
Advanced Capstone Software Suite
Students design, build, test, and showcase a full-fledged Object-Oriented Python software application with file persistence, API integration, and modular architecture.
| # | Lesson Title | What Students Learn | Activity / Project | Key Concepts / Syntax |
|---|---|---|---|---|
| 4.1 | Capstone Track Selection & System Design | Select capstone project track (Library System, Weather Dashboard, Finance Tracker, or Custom). Design UML-style class diagram. | Design Workshop: Draft class hierarchy, data storage schema, and API endpoint specs. | Software Architecture & Design |
| 4.2 | Core Data Models & Classes | Implement base classes, subclasses, attributes, methods, and dunder methods. Enforce encapsulation and type hints. | Build Session: Write and unit-test all core OOP domain models. | Classes, Inheritance, Encapsulation |
| 4.3 | API Integration & Data Storage | Connect application to external Web APIs and implement JSON/CSV data persistence layer. | Build Session: Implement API client module and local file storage manager. | Requests, JSON File Persistence |
| 4.4 | Application Controller & Business Logic | Build controller module that connects user interactions, data models, and storage functions cleanly. | Build Session: Write controller logic with input validation and exception safety. | Controller Logic, Exceptions |
| 4.5 | CLI / Interactive User Interface | Design clean console interface or rich text UI loop with formatted outputs, tables, and menus. | Build Session: Assemble main application entry point and test end-to-end user workflows. | CLI Interface & UX |
| 4.6 | Testing, Code Quality & PEP 8 | Perform systematic edge-case testing. Format code according to PEP 8 style guides and add docstrings. | Code Quality Lab: Run linter, refactor complex methods, and write comprehensive docstrings. | PEP 8, Docstrings, Refactoring |
| 4.7 | Documentation & Readme | Create a professional project README.md with installation steps, features list, sample output screenshots, and architecture overview. | Documentation Workshop: Write complete GitHub-ready README file for the capstone project. | Technical Writing & Documentation |
| 4.8 | Final Capstone Presentation & Demo | Present finished software suite to instructor, peers, and parents. Demonstrate live features, discuss design decisions, and answer Q&A. | Demo Day: Live 5-minute software demonstration + Technical Q&A. Receive Advanced Python Certificate. | Software Portfolio Showcase |
Teaching Notes & Tips
Pacing Guidance
Each module contains 8 lessons of approximately 50–60 minutes each, totalling ~40 hours. Module 1 (OOP) is foundational — do not rush Lessons 1.4–1.6. Module 4 lessons are extended project sessions; hold flexible checkpoints rather than strict timings.
Differentiation
Advanced students can explore: multiple inheritance, decorators, context managers, pandas for data analysis, Flask for web APIs, or SQLite for database persistence. Students needing support should focus on core OOP and avoid over-engineering their capstone.
Assessment Criteria
Module projects assessed on: (1) Functionality — does it work correctly? (2) OOP Design — proper class structure and relationships. (3) Code Quality — PEP 8, docstrings, meaningful names. (4) Error Handling — graceful failure on bad input. (5) Presentation — clarity of explanation.
Tools & Environment
Required: VS Code with Python + Pylance extensions, Python 3.10+. Students should be comfortable using the terminal. Module 3 requires pip access to install requests. API lessons use free, keyless APIs (Open-Meteo, Open Trivia DB) to avoid sign-up barriers.
Capstone Project Tracks
Track A — Library Management System: OOP-designed system to manage books, members, and loans with CSV persistence. Track B — Weather Dashboard: Fetches multi-city weather via API, stores history in JSON, displays trends. Track C — Personal Finance Tracker: OOP-driven expense/income manager with category analysis and file persistence. Track D — Student-proposed project (requires teacher approval and design sign-off by Lesson 4.2).
Prior Knowledge Expected
Students must be confident with: all Python syntax (variables, loops, conditions), defining and calling functions with parameters and return values, reading/writing text files, try-except error handling, and working with lists and dictionaries (Python Fundamentals Basic + Intermediate courses).
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Advance Python Programming · Advanced · Ages 15–17 · © Course Curriculum
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