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AP Computer Science Principles - Algorithms and Programming

The heaviest sub-module; teaches foundational coding and algorithmic problem-solving logic. • Key Topics: Variables, data types, expressions, Boolean logic, conditional statements (if-else), iteration (loops), procedures/functions, lists/arrays, binary search, and evaluating algorithm efficiency (decidability).

Study Tools

All

Study guides

10

09. Evaluating Algorithm Efficiency

A structured guide to evaluating algorithm efficiency through input-size modeling, operation counting, asymptotic growth, time and space complexity, and careful interpretation of bounds.
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10. Integrated Problem-Solving Practice

A progressive guide to designing, implementing, testing, debugging, and evaluating algorithms with Python-like examples, including search, Boolean logic, loop behavior, and complexity.
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01. Introduction to Programming and Algorithmic Problem Solving

A progressive introduction to turning problems into correct programs, controlling execution, testing solutions, analyzing efficiency, and understanding the limits of algorithms.
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07. Lists, Arrays, and Sequential Data

A practical guide to storing, accessing, traversing, searching, and modifying ordered collections while avoiding common indexing and boundary errors.
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02. Variables, Data Types, and Input–Output

Learn how Python programs store values, distinguish data types, convert and validate input, perform assignment, and produce useful output.
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08. Searching Algorithms

A practical guide to choosing, explaining, verifying, and implementing linear and binary search algorithms, with attention to ordering, efficiency, boundaries, duplicates, and data structures.
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06. Procedures and Functions

Learn how procedures and functions organize programs, accept inputs, return results, manage scope, limit side effects, and support decomposition and abstraction in Python.
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03. Expressions and Boolean Logic

A progressive guide to Python expressions, arithmetic and comparison operators, Boolean logic, strings, evaluation order, and common expression errors.
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05. Iteration and Loops

A practical guide to designing, choosing, tracing, and testing loops, with emphasis on control flow, counting, accumulation, termination, and common errors.
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04. Conditional Statements

A practical guide to Python conditional logic, including branching, Boolean expressions, validation, guard clauses, and common decision-making errors.
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Quizzes

10

01. Introduction to Programming and Algorithmic Problem Solving

A medium-difficulty assessment of programming fundamentals, algorithmic problem solving, control flow, data structures, testing, debugging, complexity, and decidability. Questions progress from foundational understanding to applied reasoning.
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07. Lists, Arrays, and Sequential Data

A 14-question quiz on lists, arrays, indexing, traversal, collection-processing patterns, searching, mutation, and nested data. The required questions include three single-select, two multi-select, two true/false, two short-text, two fill-blank, and one open-ended question, plus two alternate candidates.
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08. Searching Algorithms

A medium-difficulty assessment of linear search, binary search, correctness, efficiency, implementation details, and algorithm selection. Questions progress from foundational concepts to applied reasoning about traces, boundaries, duplicates, data structures, and search strategy.
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02. Variables, Data Types, and Input–Output

A 14-question quiz on variables, identifiers, assignment, data types, type conversion, input, output, and the input–process–output pattern in Python. The questions progress from foundational applications to more integrated reasoning.
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09. Evaluating Algorithm Efficiency

A medium-difficulty quiz on evaluating algorithm efficiency, including cost models, asymptotic growth, case analysis, nested loops, and auxiliary space.
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06. Procedures and Functions

A medium-difficulty quiz on defining, calling, and designing procedures and functions; parameters, return values, scope, side effects, decomposition, abstraction, and common function errors in Python.
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10. Integrated Problem-Solving Practice

A 14-question assessment on integrated programming problem solving, including representation, control flow, debugging, searching, testing, complexity, termination, and algorithm design. Questions progress from foundational application to more integrated reasoning.
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05. Iteration and Loops Quiz

A medium-difficulty quiz on iteration, loop selection, counters, accumulators, termination, nested loops, and loop correctness in Python-like pseudocode.
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03. Expressions and Boolean Logic

A medium-difficulty quiz on Python expressions, Boolean logic, strings, operator precedence, evaluation, and common expression errors. Questions progress from direct application of one rule to multi-step reasoning and explanation.
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04. Conditional Statements

A 14-question assessment on Python conditional statements, including branching, Boolean expressions, validation, nesting, guard clauses, range classification, and common conditional errors.
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Flashcards

10

10. Integrated Problem-Solving Practice

A focused set of flashcards on integrating Python programming concepts, debugging algorithms, testing boundary cases, searching data, and evaluating correctness, termination, and efficiency.
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08. Searching Algorithms

Learn how linear and binary search work, when each is appropriate, how their correctness and efficiency differ, and how to handle boundaries, duplicates, keys, and implementation choices.
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06. Procedures and Functions

A focused review of Python procedures and functions, including calls, parameters, return values, scope, side effects, decomposition, abstraction, and common errors.
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04. Conditional Statements

A focused review of Python conditional statements, Boolean logic, validation, branching patterns, and common decision-making errors.
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07. Lists, Arrays, and Sequential Data

Builds practical understanding of lists, indexing, traversal, collection-processing patterns, searching, nested data, and common errors in sequential data structures.
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05. Iteration and Loops

A focused review of loop design, Python-like iteration patterns, state tracking, control flow, common errors, and correctness reasoning.
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09. Evaluating Algorithm Efficiency

A focused review of input size, operation counting, asymptotic growth, time and space complexity, analysis cases, and common efficiency bounds.
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01. Introduction to Programming and Algorithmic Problem Solving

A focused set of flashcards covering core programming concepts, problem-solving workflow, algorithm analysis, binary search, and the limits of computation.
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02. Variables, Data Types, and Input–Output

Learn how Python programs name, store, convert, receive, process, and display values using variables, data types, assignment, and input–output.
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03. Expressions and Boolean Logic

Learn how Python expressions produce values through arithmetic, comparison, Boolean logic, string operations, precedence, evaluation, and common error prevention.
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