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Data Structures and Algorithms - CS 201

Covers arrays, linked lists, stacks, queues, trees, hash tables, sorting, searching, recursion, and Big-O analysis.

Study Tools

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Study guides

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10 Data Structure and Algorithm Trade-offs

A practical guide to choosing data structures and algorithms by comparing operations, complexity, memory behavior, ordering needs, and implementation trade-offs.
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05 Recursion

A progressive guide to designing, tracing, analyzing, and evaluating recursive algorithms, including call-stack behavior, common patterns, memoization, and the trade-offs between recursion and iteration.
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08 Searching Algorithms

A practical guide to sequential and binary searching, including prerequisites, correctness, complexity, implementation choices, and duplicate-handling variants.
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03 Linked Lists

A progressive guide to linked-list structure, traversal, update operations, complexity, and practical trade-offs among singly linked, doubly linked, and circular designs.
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04 Stacks and Queues

A progressive guide to the behavior, implementations, performance, applications, and design trade-offs of stacks and queues.
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09 Sorting Algorithms

A progressive guide to sorting principles, stability, elementary and advanced sorting algorithms, complexity trade-offs, memory use, and algorithm selection.
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07 Trees and Binary Search Trees

A progressive guide to tree structure, binary-tree traversals, binary search tree operations, deletion, balancing, and the way tree height determines time and space costs.
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06 Hash Tables

A progressive guide to hash tables that explains hashing, collisions, collision-resolution strategies, load factors, resizing, performance limits, and practical data-structure choices.
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02 Arrays and Dynamic Arrays

Learn how arrays store ordered data, how indexing and shifting affect performance, and how dynamic, multidimensional, and array-based structures support different workloads.
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01 Algorithm Analysis and Big-O Notation

A progressive guide to designing, proving, and analyzing algorithms using data structures, complexity measures, asymptotic notation, and recurrence analysis.
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Quizzes

10

08 Searching Algorithms

A 14-question assessment on sequential search, binary search, correctness, complexity, implementation choices, and search variants. Questions progress from foundational applications to more involved analysis.
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07 Trees and Binary Search Trees

A medium-difficulty quiz on tree terminology, binary trees, traversals, binary search trees, deletion, balance, and complexity. Questions progress from foundational application to multi-step reasoning.
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04 Stacks and Queues

A medium-difficulty quiz on stacks and queues as abstract data types, their operations, implementations, performance, edge cases, and applications. Questions progress from direct application to more involved implementation reasoning.
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02 Arrays and Dynamic Arrays

A medium-difficulty quiz on array indexing, traversal, insertion and deletion, dynamic-array capacity, amortized analysis, multidimensional storage, and choosing an array-based structure.
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10 Data Structure and Algorithm Trade-offs

A medium-difficulty assessment of data structure and algorithm trade-offs, emphasizing operation patterns, complexity analysis, implementation constraints, and practical design choices.
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09 Sorting Algorithms

A medium-difficulty quiz on sorting algorithms, emphasizing algorithm behavior, complexity, stability, memory use, and choosing an appropriate algorithm for a situation.
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03 Linked Lists

A medium-difficulty quiz on linked-list structure, traversal, updates, complexity, circular links, and practical trade-offs. The questions progress from core concepts to applied reasoning.
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06 Hash Tables

A medium-difficulty quiz on hash tables covering hash functions, collisions, collision-resolution strategies, load factor, probing, resizing, and performance trade-offs.
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05 Recursion

A medium-difficulty quiz on recursion, including recursive design, call-stack behavior, recursive patterns, complexity analysis, memoization, and implementation trade-offs.
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01 Algorithm Analysis and Big-O Notation

A medium-difficulty quiz on algorithmic thinking, abstract data types, correctness, complexity analysis, asymptotic notation, recurrences, and data-structure performance. Questions progress from foundational concepts to applied analysis.
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Flashcards

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08 Searching Algorithms

A focused set of flashcards covering sequential and binary search, their correctness, implementation choices, complexity, variants, and practical applications.
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07 Trees and Binary Search Trees

A focused review of tree terminology, binary-tree traversals, binary search tree operations, deletion cases, and height-dependent complexity.
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03 Linked Lists

A focused review of linked-list structure, traversal, updates, variants, complexity, and practical trade-offs.
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01 Algorithm Analysis and Big-O Notation

Review algorithmic thinking, abstract data types, correctness proofs, complexity measures, asymptotic notation, recurrences, and data-structure performance.
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09 Sorting Algorithms

Builds practical understanding of sorting correctness, stability, core algorithms, complexity, and algorithm selection.
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10 Data Structure and Algorithm Trade-offs

A focused review of data-structure and algorithm trade-offs, complexity bounds, implementation patterns, and practical combinations for common workloads.
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05 Recursion

A focused set of flashcards covering recursive design, call-stack behavior, common patterns, complexity analysis, memoization, and practical trade-offs.
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06 Hash Tables

A focused review of hash-table structure, hashing, collision resolution, load factor, resizing, performance, and appropriate use cases.
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04 Stacks and Queues

A focused review of stack and queue abstract data types, their operations, implementations, complexities, edge cases, and common applications.
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02 Arrays and Dynamic Arrays

A focused review of array indexing, memory layout, operations, dynamic resizing, multidimensional representations, and performance trade-offs.
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