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Foundations of Computer Science

Computational Thinking and Abstraction

Algorithms: Design, Analysis, and Complexity

Data Structures and Memory Models

Programming and Software Construction

Programming Paradigms and Type Systems

Testing, Debugging, and Software Quality

Systems and Architecture

Computer Organization and Architecture

Operating Systems and Concurrency

Data, Networks, and Security

Databases and Data Management

Networking and the Internet

Foundations of Cybersecurity

Theory and Advanced Topics

Formal Languages, Automata, and Computability

Computational Complexity and Reductions

Applied Practice and Integration

Capstone: From Problem Statement to Solution

Knowledge Checks

Quiz: Algorithms and Complexity Basics

Quiz: Data Structures Selection

Quiz: Systems and OS Concepts

Quiz: Databases and Networking

Quiz: Theory and Computability

Course Feedback

End-of-Course Survey

Master the core ideas that power every great software system. Foundations of Computer Science: Algorithms, Structures, and Complexity takes you from computational thinking to hands-on software construction, systems, data, networks, security, and theory—so you can analyze problems, design efficient solutions, and build robust applications with confidence. You will learn to express algorithms in clear pseudocode, reason about time and space with Big-O, and choose data structures that fit real performance constraints. Go beyond coding syntax to understand how computers execute instructions, how operating systems schedule and synchronize work, and how networks move data at scale. Along the way, you’ll practice testing, debugging, and version control workflows used by professional teams. The course also introduces databases and SQL, the internet’s layered protocols, and the essentials of cybersecurity, including cryptographic building blocks and secure design principles. Rounding out your foundation, you will explore formal languages, automata, computability, and computational complexity to recognize fundamental limits and choose practical strategies—such as approximations or heuristics—when problems are inherently hard. A guided capstone project helps you integrate everything: define requirements, select algorithms and architectures, implement and test, and evaluate trade-offs. Whether you’re preparing for technical interviews, upskilling for software engineering roles, or seeking a rigorous academic grounding, this course gives you the mental models and tools to think like a computer scientist and deliver high-quality software.

Foundations of Computer Science: Algorithms, Structures, and Complexity

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            <p>Foundations of Computer Science: Algorithms, Structures, and Complexity builds a rigorous mental toolkit for thinking about computation end to end. The journey begins with computational thinking—problem decomposition, pattern recognition, abstraction, and algorithmic design—while connecting these skills to concrete models of computation and the layered abstractions that span hardware, operating systems, and software. You will express algorithms in clear pseudocode, trace their execution on inputs, and analyze their time and space behavior using Big-O, Big-Theta, and Big-Omega. Alongside classical strategies such as divide-and-conquer, greedy methods, dynamic programming, and backtracking, the course introduces the limits of computability and the role of reductions. Complementing algorithmic thinking, you will learn to select and implement core data structures—arrays, linked lists, stacks, queues, hash tables, heaps, trees, and graphs—while weighing trade-offs in access, updates, iteration, and memory locality, and understanding how immutability, references, pointers, and garbage collection influence performance.</p>

<p>From theory to practice, the course develops software construction skills across paradigms—imperative, object-oriented, functional, and concurrent—and shows how types, generics, and interfaces enforce invariants and enable reuse across compiled and interpreted environments. You will cultivate software quality through specifications, contracts, unit and property-based testing, integration testing, code coverage, version control workflows, and continuous integration. At the systems level, you will relate computer organization concepts—fetch-decode-execute, instruction sets, microarchitecture, and memory hierarchy—to program performance and understand concurrency in pipelines, SIMD, and multicore processors. Operating systems topics cover processes, threads, scheduling, synchronization, deadlocks, virtual memory, filesystems, I/O, and safe use of concurrency primitives to avoid races and ensure liveness. Data and networks modules introduce relational modeling, SQL, transactions, indexing, and when to choose non-relational stores; they also explain the Internet’s layered architecture, core protocols (IP, TCP/UDP, HTTP), routing, DNS, congestion control, and the trade-offs among latency, throughput, reliability, and scalability. A foundational security thread clarifies confidentiality, integrity, availability, cryptographic primitives, secure design, and risk assessment. The theory block sharpens your analytical edge with formal languages, automata, decidability, complexity classes (P, NP, NP-complete, NP-hard), polynomial-time reductions, and practical strategies such as approximation and parameterized algorithms. The capstone integrates these themes as you move from requirements and specifications to architecture, implementation, testing, profiling, and complexity-driven trade-offs, reinforced by targeted knowledge checks throughout the course.</p>
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