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Foundations of Computer Science

Computational Thinking and Abstraction

Algorithms: Design and Analysis

Data Structures and Memory Models

Computation and Complexity

Computational Complexity and Problem Classes

Models of Computation and Formal Languages

Programming and Software Construction

Programming Paradigms and Abstraction Mechanisms

Algorithmic Problem Solving in Practice

Data, Graphs, and Systems

Graph Algorithms and Networked Data

Concurrency and Performance

Assessment: Quizzes and Survey

Quiz 1: Algorithms and Asymptotic Analysis

Quiz 2: Data Structures and Memory Trade-offs

Quiz 3: Complexity and Reductions

Quiz 4: Formal Languages and Automata

Quiz 5: Graphs, Concurrency, and Performance

Course Feedback Survey

Master the essential pillars of computer science—algorithms, data structures, and computational complexity—and learn to apply them to real problems. Core Computer Science: Algorithms, Data Structures, and Complexity Applications is a rigorous, practice-driven course that takes you from foundational thinking to performance-aware implementation. You’ll learn to express algorithms with clear contracts, choose the right data structures, analyze time and space, and reason about correctness using invariants and induction. Explore graph algorithms, hashing, trees, and memory models that matter for real systems. Build intuition for growth rates, reductions, and complexity classes like P, NP, and NP-complete, and understand when approximation or heuristics are the pragmatic choice. You’ll also connect theory to software construction: functional vs. imperative styles, abstraction, testing strategies, concurrency, and profiling. By the end, you’ll be fluent in selecting, designing, and justifying algorithmic solutions; diagnosing bottlenecks; and communicating trade-offs that drive reliable, efficient software. Quizzes reinforce learning across algorithms, data structures, complexity, formal languages, graphs, and performance—preparing you to tackle interviews, academic study, and production engineering with confidence.

Core Computer Science: Algorithms, Data Structures, and Complexity Applications

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            <p>Core Computer Science: Algorithms, Data Structures, and Complexity Applications is a rigorous, application-minded journey through the intellectual backbone of computing. The course begins by developing computational thinking—clarifying what counts as a computation, a problem, and an algorithm—while distinguishing specification from implementation. You will practice structuring problems with decomposition, patterns, and abstraction, and trace simple procedures using input/output contracts, invariants, and models of state. With this foundation, we move into algorithm design and analysis: expressing solutions in pseudocode with clear preconditions and postconditions; comparing strategies such as brute force, greedy, divide-and-conquer, dynamic programming, and backtracking; and proving correctness via loop invariants, induction, and counterexamples. Asymptotic analysis with Big-O, Theta, and Omega will equip you to reason about best, average, and worst-case behavior in a principled way.</p>
<p>The course then builds the data-structure intuition required to realize algorithms efficiently. You will study linear structures (arrays, linked lists, stacks, queues, deques), hierarchical and indexed structures (BSTs, AVL trees, heaps, tries), and graphs with both adjacency lists and matrices, weighing their operational trade-offs. Hashing fundamentals—functions, collisions, load factor—connect to memory and locality, pointers and references, and cache-aware design. From there, we broaden to computational complexity: formal models (Turing machines, RAM), decision problems, reductions, and completeness; core classes P, NP, NP-complete, and NP-hard; and practical methods—approximations and heuristics—when exact solutions are intractable. You will also explore formal languages and automata, from regular expressions and finite automata to context-free grammars and pushdown automata, using these tools to specify inputs and protocols and to reason about decidability and the halting problem.</p>
<p>Turning theory into practice, the course surveys programming paradigms—imperative, functional, and object-oriented—and the abstraction mechanisms that make large programs reliable: procedures, modules, types, interfaces, and data encapsulation. You will employ immutability, higher-order functions, and generics, supported by disciplined testing (unit, property-based, and test-driven development) to guard correctness as you optimize. In the applied modules, you will choose data structures and algorithms for tasks like searching, sorting, scheduling, and routing; analyze bottlenecks; and profile and optimize hotspots while preserving correctness and numerical stability. Ethical and safety considerations—fairness, reproducibility, and verifiability—are interwoven throughout.</p>
<p>Real-world systems often revolve around networks and parallel activity, so you will develop fluency with graph algorithms—BFS/DFS, shortest paths (Dijkstra, Bellman-Ford), minimum spanning trees (Kruskal, Prim), topological sorting, connectivity, flows, and cuts—alongside strategies for scaling graph computations in practice. The concurrency and performance module contrasts parallel and concurrent models; clarifies tasks, threads, and processes; and covers synchronization primitives, race conditions, and deadlocks. You will reason about speedup with Amdahl’s and Gustafson’s insights, understand cache effects, and balance asymptotic analysis with empirical profiling, distinguishing latency from throughput.</p>
<p>Assessment is incremental and targeted. Quizzes focus on algorithms and asymptotics; data structures and memory trade-offs; complexity and reductions; formal languages and automata; and graphs, concurrency, and performance. A closing survey gathers feedback to refine future iterations. By the end, you will not only understand how to design correct and efficient algorithms and data structures, but also how to select, implement, and evaluate them under real constraints—linking foundational theory to dependable, high-performance software systems.</p>
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