AP Computer Science A is an introductory college-level computer science course. Students cultivate their understanding of coding through analyzing, writing, and testing code as they explore concepts like modularity, variables, and control structures.

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Introduces the fundamental concepts of a high‐level programming language and provides a comprehensive introduction to programming for STEM majors. Topics include data types, flow of control, functions, I/O streams, arrays, and the mechanics of running, testing, and debugging.

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The class provides a basic explanation of how a computer is built and runs. The class also introduces the basic concepts, principles, methods, implementation techniques, and applications of C++. Details of the syntax of the C++ programming language including virtually all keywords and operators are taught. Loops, Arrays, and Functions are covered in depth. String manipulation functions are explained and re-implemented. Includes basic analysis of algorithms, searching and sorting techniques, and an introduction to software engineering.

Low-level computer design, basics of digital design, and hardware/software interface; Principles of pipelining and caching, instruction pipelining, SIMD and multiprocessor systems.

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Introduction to fundamental data structures: arrays, lists, stacks, queues, hash tables, trees; sorting and searching; graph algorithms; design, analysis, and comparison of algorithms. Correctness verification techniques such as assertions and invariants. Review program specification, unit testing, and debugging.

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Algorithm analysis and design, heuristics; advanced tree structures; advanced hashing techniques; sorting and searching; graphs, sets. NP-Completeness, Time and Space complexities.

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INTRODUCTION TO DBMS - A study of contemporary database management system concepts, terminology, and methodology for use and implementation. Commercially available systems are discussed and used with emphasis upon the relational model.

Data science concepts including exploratory data analysis, data visualization, statistical inference and modeling, machine learning, clustering, post-processing and interpreting results.

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Introduction to automata theory (finite-state automata, push-down automata, Turing machines); formal systems (regular and context-free languages and grammars); computability, Church-Turing thesis.

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Operating system; sequential processes, concurrent processes, deadlock, mutual exclusion, semaphores; memory management, processor management, peripheral device management.

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Techniques for file organizations on secondary storage, performance, design, and management of large integrated databases, data models, query languages.

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Data communications; network protocols and architecture; local and wide-area networks; internetworking.

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Essential concepts of digital image processing, image acquisition and processing, practical applications, and elementary image analysis algorithms.

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