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Foundations of Computer Science

Computational Thinking and Abstraction

Algorithms: Design, Analysis, and Complexity

Core Data Structures and Their Applications

Programming and Software Construction

Programming Paradigms and Quality

Memory, Resource Management, and Concurrency

Systems and Computation Models

Computer Architecture and Operating Systems Essentials

Automata, Computability, and Complexity

Data, Graphs, and Optimization

Graph Algorithms and Combinatorial Optimization

Data Processing, Models, and Evaluation

Capstone Integration and Assessment

Capstone: From Problem to Efficient Solution

Quiz 1: Computational Thinking and Abstraction

Quiz 2: Algorithms and Complexity

Quiz 3: Data Structures and Programming Paradigms

Quiz 4: Systems and Computation Models

Quiz 5: Graphs, Optimization, and Data Processing

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Master the core of applied computer science with a practical, systems-aware approach. Applied Computer Science: Algorithms, Data Structures, and Complexity takes you from computational thinking to production-grade solutions. You will design and analyze algorithms, select the right data structures for real workloads, and connect theory to performance across memory, concurrency, and operating systems. Through hands-on exercises and a culminating capstone, you will translate problem statements into computational models, justify choices with asymptotic analysis, and validate them with profiling and tests. The curriculum blends essentials—Big-O analysis, graphs, dynamic programming, and automata—with software construction practices like modular design, testing, and version control. You will also build intuition for when exact optimization is infeasible and how to use heuristics or approximations responsibly. By the end, you’ll be equipped to reason rigorously about correctness and efficiency, communicate trade-offs, and implement maintainable solutions that scale. Ideal for engineers, analysts, and scientists seeking a deep yet applied foundation that strengthens everyday coding, system design, and data-intensive workflows.

Applied Computer Science: Algorithms, Data Structures, and Complexity

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            <p>Applied Computer Science: Algorithms, Data Structures, and Complexity grounds you in the core ideas that make computing effective, scalable, and reliable. The course begins by sharpening computational thinking—abstraction, decomposition, pattern recognition, and algorithmic reasoning—so you can translate real problems into precise computational models with clear inputs, outputs, and constraints. You will differentiate models, specifications, and implementations, choosing the right level of abstraction for each task. From there, you’ll design and analyze algorithms in pseudocode, reason rigorously about time and space using Big-O/Theta/Omega, and learn when to use greedy, divide-and-conquer, dynamic programming, backtracking, or randomized strategies. Alongside design, you’ll build fluency with the core data structures—arrays, linked lists, stacks, queues, hash tables, heaps, trees, and graphs—evaluating their performance, memory layouts, and suitability under diverse workloads. Canonical structures like balanced search trees, priority queues, and union-find will be applied to problems in shortest paths, scheduling, and connectivity, tying theory directly to practice.</p>

<p>Beyond algorithms and structures, the course develops the craft of software construction. You will compare imperative, object-oriented, functional, and concurrent paradigms, adopting idioms that improve clarity and safety; apply testing (including property-based), debugging, and version control to maintain correctness; and design with modules, interfaces, and contracts for maintainability and reuse. A systems lens connects code to hardware and operating systems: you will study instruction pipelines, caching, virtual memory, processes, scheduling, filesystems, and I/O—and relate these concepts to algorithm performance and scalability. Formal computation models broaden your perspective with automata, grammars, decidability, reductions, and complexity classes (P, NP, NP-complete), helping you recognize fundamental limits and problem hardness.</p>

<p>The data- and graph-focused segment equips you to implement BFS/DFS, topological sort, and connectivity; apply Dijkstra, Bellman–Ford, Kruskal, Prim, and max-flow/min-cut; and formulate optimization problems when exact solutions are infeasible, including approximation and heuristic approaches. You will design practical data pipelines for ingestion, transformation, and representation with awareness of complexity and memory, compare basic predictive models and evaluation metrics, and consider fairness, robustness, and reproducibility as essential quality dimensions. The capstone synthesizes everything: you will select a real-world problem, specify requirements, model it computationally, design algorithms and data structures backed by asymptotic and empirical evidence, implement and test a solution, and report trade-offs and limitations grounded in computer science principles. Quizzes throughout reinforce mastery across computational thinking, algorithms, data structures, systems, and graph/data processing, culminating in a coherent, practice-ready understanding of applied computer science.</p>
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