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Computer Science

Course Orientation and Core Principles

Abstraction, Data, and Representation

Algorithms and Data Structures

Algorithmic Thinking and Analysis

Core Data Structures and Their Applications

Computational Complexity and Paradigms

Computability and Complexity

Software Construction for Algorithmic Solutions

Applying CS Principles Across Domains

Data-Centric Problem Solving

Systems and Networks for Scalable Solutions

Formative Quizzes

Quiz 1: Abstraction and Data Representation

Quiz 2: Algorithm Design and Analysis

Quiz 3: Data Structures in Practice

Quiz 4: Complexity and Computability

Quiz 5: Systems, Concurrency, and Scaling

Course Wrap-Up and Feedback

Capstone Reflection and Integration

End-of-Course Survey

Master the core pillars of computer science with Applied Computer Science: Algorithms, Data Structures, and Complexity Analysis. This hands-on course takes you from foundational principles to practical implementation, teaching you how to design efficient algorithms, choose the right data structures, and reason about performance at scale. You’ll learn to analyze problems formally with asymptotic notation, validate correctness with invariants and proofs, and make informed trade-offs between time, space, and precision. Beyond theory, you’ll translate ideas into high-quality code using modular design, testing strategies, and performance-aware techniques. You’ll also explore computability and complexity classes, when exact solutions are infeasible, and how approximation and randomized algorithms offer practical paths forward. Finally, connect CS principles to real-world domains—data pipelines, systems and networks, and distributed scalability—while keeping ethics, privacy, and bias top of mind. By the end, you’ll think like a computer scientist: decomposing problems, modeling with abstract data types, and building robust, scalable solutions backed by rigorous analysis. Ideal for developers, analysts, and aspiring software engineers who want to strengthen their algorithmic intuition and deliver production-ready systems.

Applied Computer Science: Algorithms, Data Structures, and Complexity Analysis

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            <p>Applied Computer Science: Algorithms, Data Structures, and Complexity Analysis equips you with the conceptual foundations and practical techniques to design correct, efficient, and ethical computational solutions. The course begins by grounding you in core principles—abstraction, problem decomposition, and rigorous reasoning—while clarifying expectations, assessment methods, and professional responsibilities in computing. You will examine how information is represented across levels of abstraction, from bits to structured data, and how modeling with abstract data types such as stacks, queues, maps, and sets leads to clear interfaces and informed trade-offs among space, precision, and performance.</p>

<p>The heart of the course develops algorithmic thinking and data-structure fluency. You will practice major design paradigms—greedy, divide-and-conquer, dynamic programming, and backtracking—while establishing correctness through invariants and induction. Asymptotic analysis (Big-O, Big-Theta, Big-Omega) and empirical benchmarking will guide your understanding of time–space trade-offs. Building on this, you will implement and compare core structures—arrays, linked lists, trees (BST, AVL, heaps), tries, hash tables, graphs—and learn to select them based on workload characteristics, from iterative to recursive patterns and from BFS/DFS traversals to shortest paths and minimum spanning trees.</p>

<p>Beyond implementation detail, the course situates your work within the broader landscape of computability and complexity. You will explore finite automata, Turing machines, and decidability; map problems across complexity classes (P, NP, NP-complete) using reductions; and leverage approximation and randomized strategies when exact solutions are infeasible. Practical constraints inform software construction techniques: design by contract, modular interfaces, systematic testing (unit, property-based, integration), and performance-aware coding that respects memory locality and weighs recursion against iteration. An introduction to concurrency highlights threads, synchronization, and the hazards of data races.</p>

<p>Applications anchor theory in real-world contexts. You will build data-centric pipelines—from ingestion and cleaning to analysis and visualization—selecting algorithms for sorting, indexing, streaming, and sketching under resource constraints. Foundational ideas in search and recommendation connect similarity, ranking, and evaluation to responsible data practices emphasizing privacy, bias mitigation, and reproducibility. Systems and networking modules reveal how architecture, operating system primitives, and network protocols influence algorithmic performance at scale, leading to distributed patterns like sharding, caching, load balancing, and fault tolerance.</p>

<p>Regular formative quizzes reinforce mastery at key milestones: abstraction and representation; algorithm design and analysis; data structures in practice; complexity and computability; and systems, concurrency, and scaling. The course concludes with a capstone reflection that synthesizes algorithms, data structures, abstraction, and complexity into coherent solution strategies, prompting you to articulate trade-offs made across domains and to chart next steps toward advanced topics and practical projects.</p>
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