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AP Computer Science (Python Version)

Course Title

AP Computer Science (Python Version) College-Level Introduction to Computer Science Using Python

Audience

This course is designed for:

  • High school students (grades 10–12) seeking a rigorous, college-level computer science experience
  • Students interested in STEM, engineering, mathematics, data science, AI, or software development
  • Students preparing for university-level CS1 courses taught in Python
  • Schools seeking a modern alternative to Java-based AP Computer Science A

This course mirrors the scope and rigor of College Board AP Computer Science A, but uses Python as the primary language of instruction.

Prerequisites

Students should have:

  • Successful completion of Algebra I (Algebra II recommended)
  • Strong logical reasoning and problem-solving skills
  • Comfort with abstract thinking
  • Basic computer literacy

Prior programming experience is helpful but not required.

Overview

AP Computer Science (Python Version) is a college-level course that introduces students to:

  • Computational thinking
  • Algorithm design
  • Object-oriented programming (OOP)
  • Data structures
  • Abstraction and modular design
  • Program analysis and testing

The course emphasizes disciplined software engineering practices while leveraging Python’s readability and expressive power. Students learn to design, implement, test, and debug multi-module programs.

Although this is not an official AP exam course, it aligns conceptually with the depth and rigor of AP Computer Science A and first-semester university CS courses taught in Python.

Topics Covered

1. Computational Thinking and Problem Solving

  • Decomposition
  • Pattern recognition
  • Abstraction
  • Algorithm design
  • Stepwise refinement

2. Python Fundamentals

  • Variables and assignment
  • Numeric types (int, float)
  • Boolean logic
  • Strings and string methods
  • Type conversion
  • Input and output

3. Control Structures

  • Conditional statements (if, elif, else)
  • Loops (for, while)
  • Nested control structures
  • Logical operators
  • Short-circuit evaluation

4. Functions and Modular Design

  • Function definition and invocation
  • Parameters and return values
  • Scope and lifetime of variables
  • Default parameters
  • Docstrings and documentation
  • Modular program structure

5. Data Structures

  • Lists
  • Tuples
  • Dictionaries
  • Sets
  • Traversal patterns
  • Nested data structures

6. Object-Oriented Programming

  • Classes and objects
  • Instance attributes
  • Methods
  • Constructors (__init__)
  • Encapsulation conventions
  • Composition
  • Inheritance
  • Polymorphism
  • Method overriding

7. Algorithms

  • Searching (linear, binary)
  • Sorting (selection, insertion, merge sort)
  • Algorithm correctness
  • Big-O notation (introductory level)

8. Recursion

  • Recursive definitions
  • Base and recursive cases
  • Tracing recursive calls
  • Comparing recursive vs iterative solutions

9. File I/O

  • Reading from files
  • Writing to files
  • Structured data formats (introductory JSON concepts)

10. Testing and Debugging

  • Unit testing basics
  • Assertions
  • Edge cases
  • Defensive programming
  • Code tracing

Topics NOT Covered

The following topics are generally outside the scope of this course:

  • Advanced data structures (trees, graphs, hash tables)
  • Database systems
  • Web frameworks
  • Machine learning
  • Artificial intelligence algorithms
  • Multithreading and concurrency
  • Operating systems
  • Network protocol implementation
  • Cybersecurity engineering
  • Advanced numerical computing

Learning Objectives (2001 Bloom’s Taxonomy)

1. Remember

Students will be able to:

  • Recall Python syntax and semantics
  • Identify built-in data types and structures
  • List common list, dictionary, and string methods
  • Define key terms such as abstraction, recursion, and polymorphism

2. Understand

Students will be able to:

  • Explain how control flow affects execution
  • Describe how objects encapsulate state and behavior
  • Interpret code segments and predict output
  • Summarize how recursion operates
  • Explain the purpose of modular design

3. Apply

Students will be able to:

  • Write programs using conditionals and loops
  • Implement functions with parameters and return values
  • Create classes that model real-world entities
  • Manipulate lists and dictionaries to solve problems
  • Read from and write to files

4. Analyze

Students will be able to:

  • Decompose complex problems into smaller components
  • Compare iterative and recursive implementations
  • Identify logical, syntax, and runtime errors
  • Evaluate algorithm efficiency at an introductory level
  • Trace object interactions in multi-class systems

5. Evaluate

Students will be able to:

  • Assess readability and maintainability of code
  • Critique algorithmic alternatives
  • Justify design decisions in object-oriented systems
  • Validate test cases for completeness
  • Determine whether a solution satisfies given specifications

6. Create

Students will be able to:

  • Design and implement complete multi-module applications
  • Develop reusable classes and data abstractions
  • Construct recursive algorithms
  • Integrate multiple data structures into cohesive solutions
  • Build software systems that model real-world processes

Course Outcomes

By the end of the course, students will:

  • Demonstrate mastery of foundational computer science principles
  • Develop proficiency in Python programming
  • Understand object-oriented design concepts
  • Be prepared for college-level CS1 courses taught in Python
  • Be capable of transitioning to Java-based AP Computer Science A if needed

Alignment to College-Level Expectations

This course is equivalent to a first-semester college course in computer science taught in Python. It emphasizes:

  • Formal problem solving
  • Structured programming
  • Object-oriented design
  • Algorithm development
  • Foundational software engineering principles