Age group: 15-19
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# 🐍 Python Bronze Curriculum ## Module 1: Python Overview - The Python Interpreter - Preview of a Python Program - Objects in Python - Identifiers, Objects, and the Assignment Statement - Creating and Using Objects - Python’s Built-In Classes - Expressions, Operators, and Precedence - Compound Expressions and Operator Precedence - Control Flow (Conditionals and Loops) - Functions and Information Passing - Python’s Built-In Functions - Simple Console Input and Output ## Module 2: Exception Handling - Raising an Exception - Iterators and Generators - Additional Python Conveniences - Conditional Expressions - Comprehension Syntax - Packing and Unpacking Sequences - Scopes and Namespaces - Modules and the Import Statement - Using Existing Modules ## Module 3: Algorithms - Algorithm Analysis - Experimental Studies - Beyond Experimental Analysis - Comparing Growth Rates - Asymptotic Analysis and Big-O Notation - Comparative Analysis - Examples and Justification Techniques - Induction and Loop Invariants ## Module 4: Recursion - The Factorial Function - Drawing an English Ruler - Binary Search - File Systems - Analyzing Recursive Algorithms - Recursive Pitfalls - Maximum Recursive Depth - Types: Linear, Binary, and Multiple Recursion - Designing Recursive Algorithms - Eliminating Tail Recursion ## Module 5: Arrays - Low-Level Arrays - Referential Arrays - Compact Arrays in Python - Dynamic Arrays and Amortization - Implementing a Dynamic Array - Amortized Analysis ## Module 6: Stacks - Stack Abstract Data Type - Array-Based Stack Implementation - Reversing Data - Matching Parentheses and HTML Tags ## Module 7: Queues - Queue Abstract Data Type - Array-Based Queue Implementation - Double-Ended Queues (Deque ADT) - Implementing Deque with Circular Array - Using Python’s Collections Module ## Module 8: Linked Lists - Singly Linked Lists - Stack/Queue Implementation with Singly Linked List - Circularly Linked Lists and Round-Robin Scheduling - Doubly Linked Lists - Deque Implementation - The Positional List Abstract Data Type ## Module 9: Sorting - Sorting a Positional List - Using Priority Queues - Selection Sort - Insertion Sort - Heap Sort - Adaptable Priority Queues - Merge Sort - Quick Sort - Python’s Built-In Sorting Functions ## Module 10: Searching - Insertions and Deletions - Binary Search Tree Performance - Balanced Search Trees Framework - AVL Trees and Update Operations - Splay Trees - Multiway Search Trees - Red-Black Trees and Their Operations
Basic Computer Knowledge: Learners should be familiar with operating a computer, navigating folders, and installing software.
Logical Thinking and Problem-Solving Interest: A curiosity about how things work and a willingness to solve puzzles or problems using logic will be helpful.
Achieve Academic and Career Recognition: Earn scholarships, enhance college admissions prospects, and gain recognition in the tech community through high performance in USACO.
Advance through Competition Levels: Progress through the USACO levels (Bronze, Silver, Gold, Platinum) by achieving success at each stage.
Increase Coding Proficiency in Python: Gain expertise in Python programming, including syntax, debugging, and optimization, through regular practice.
Enhance Problem-Solving Skills: Improve logical thinking and problem-solving strategies by tackling a wide range of challenging problems.
Develop Strong Algorithmic Thinking: Cultivate the ability to design and implement efficient algorithms through practice and competition.
Academic and Career Benefits: High performance can lead to scholarships, better college admissions prospects, and recognition in the tech community.
Competition Advancement: Success at one level (Bronze, Silver, Gold, Platinum) allows participants to progress to more challenging levels.
Increased Coding Proficiency: Regular practice in Python improves coding skills, including syntax, debugging, and optimization.
Improved Problem-Solving Skills: Exposure to diverse problems sharpens logical thinking and problem-solving strategies.
Enhanced Algorithmic Thinking: Participants develop the ability to design and implement efficient algorithms.
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Do I need any prior coding experience?
No, prior programming knowledge is not required. The course starts from scratch and progresses step-by-step.
What will I be able to do after completing this course?
You’ll be able to write Python programs, understand core data structures, solve algorithmic problems, and prepare for more advanced coding or interview prep.
How much time should I dedicate to the course?
Dedicating 5–7 hours per week is a good pace for most learners to grasp the concepts and complete exercises comfortably.
Are there practical exercises and projects included?
Yes, each module includes coding exercises, examples, and small projects to help reinforce the topics learned.
Who is this course suitable for?
This course is ideal for beginners and intermediate learners who want to build a strong foundation in Python and data structures.
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