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Foundations of Computer Science and Problem Solving

Computational Thinking and Problem Decomposition

Asymptotic Analysis and Complexity Classes

Core Data Structures for Efficient Computation

Linear Structures and Dictionaries

Trees, Heaps, and Graph Representations

Algorithm Design Techniques and Analysis

Sorting and Searching Strategies

Greedy, Divide and Conquer, and Dynamic Programming

Graph Algorithms and Shortest Paths

Advanced Topics in Performance and Trade-offs

Amortized Analysis and Probabilistic Techniques

Memory Models, Caching, and Practical Efficiency

Capstone: Algorithm Selection and Evaluation

Methodology for Choosing and Evaluating Solutions

Quizzes and Survey

Quiz 1: Asymptotic Analysis and Complexity

Quiz 2: Core Data Structures and Trade-offs

Quiz 3: Sorting, Searching, and Selection

Quiz 4: Dynamic Programming, Greedy, and Divide and Conquer

Quiz 5: Graph Algorithms and Advanced Topics

End-of-Course Survey

Master the tools and thinking patterns that power high-performance software. Algorithm Design and Data Structures for Efficient Problem Solving is a practitioner-focused course that teaches you how to analyze problems, choose the right data structures, and design algorithms that scale. Starting from computational thinking and asymptotic analysis, you’ll build a rigorous foundation for evaluating time and space trade-offs. You’ll then dive into core data structures—arrays, lists, stacks, queues, hash tables, trees, heaps, and graphs—and learn when and why to use each. The course moves from sorting, searching, greedy methods, divide and conquer, and dynamic programming to graph algorithms for traversal, shortest paths, and spanning trees. Advanced modules cover amortized analysis, randomization, memory models, caching, and practical efficiency on modern hardware. A capstone brings everything together with a structured methodology for mapping problem characteristics to solutions, designing experiments, and interpreting results. By the end, you’ll be confident not only implementing algorithms, but also selecting the most efficient approach for real-world constraints like latency, memory, and parallelism.

Algorithm Design and Data Structures for Efficient Problem Solving

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            <p>Algorithm Design and Data Structures for Efficient Problem Solving equips you with the mindset and toolkit to transform ambiguous real-world challenges into precise, scalable solutions. We begin by sharpening computational thinking—defining problems, inputs, outputs, and constraints—then decomposing tasks through abstraction and pattern recognition. Alongside this, you’ll build an intuition for time and space growth, learning to reason about scalability and trade-offs. You will formalize these ideas with asymptotic analysis, mastering Big-O/Theta/Omega, cost models, and the differences between best, average, worst-case, and amortized analysis. A high-level tour of P, NP, and NP-complete decision problems will guide disciplined algorithm selection.</p>

<p>The course then grounds theory in powerful, practical data structures. You’ll compare arrays, linked lists, stacks, and queues; design robust dictionaries using hashing and ordered maps; and work with trees, heaps, tries, and graph representations, understanding how structure choices shape performance. With this foundation, you will study essential algorithmic strategies: sorting and searching (from mergesort and quicksort to radix and selection), classic paradigms like greedy methods, divide-and-conquer with the Master Theorem, and dynamic programming with a repeatable design process for states, transitions, and reconstruction. Graph algorithms add breadth and depth—BFS/DFS, topological sort, shortest paths (Dijkstra, Bellman–Ford), minimum spanning trees, and an introduction to flows and cuts for routing and scheduling.</p>

<p>To prepare you for production settings, the advanced segment emphasizes real-world efficiency: amortized analysis for dynamic structures and union–find; probabilistic techniques including randomized algorithms, universal hashing, Bloom filters, and sketches; and when to embrace or avoid randomness. You’ll also connect algorithmic thinking to hardware realities—locality, cache-aware and cache-oblivious approaches, data layout impacts, memory budgeting, and a primer on parallel performance using the work–span model and scalable speedup laws. Throughout, you’ll balance empirical benchmarking with theoretical bounds to interpret results responsibly.</p>

<p>The capstone consolidates your learning into a systematic methodology for choosing and evaluating solutions. You will map problem characteristics to data structures and paradigms, establish baselines and metrics under real constraints, and design reproducible experiments across input distributions and scales. Case studies provide end-to-end practice from specification to trade-off analysis. Regular quizzes reinforce mastery across complexity, data structures, sorting/searching, algorithmic paradigms, graph algorithms, and advanced topics—ensuring you graduate with both the principles and the judgment to build efficient, reliable systems.</p>
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