Arrays, linked lists, stacks, queues, trees, and fundamental algorithms
← Back to Computer ScienceLearn to analyze algorithm efficiency and understand time/space complexity.
Master the fundamental sequential data structure and its operations.
Understand pointer-based data structures and dynamic memory allocation.
Learn the Last-In-First-Out (LIFO) data structure and its applications.
Master the First-In-First-Out (FIFO) data structure and its variants.
Understand recursive problem-solving and algorithm design.
Learn fundamental sorting algorithms and their analysis.
Master efficient divide-and-conquer and specialized sorting algorithms.
Explore various searching techniques and their optimizations.
Understand hierarchical data structures and tree terminology.
Learn ordered binary trees for efficient searching and sorting.
Master heap data structure and priority queue implementations.
Understand hash-based data structures for constant-time operations.
Introduction to graph theory and graph data structure representations.
Learn fundamental graph traversal techniques and their applications.
Learn to analyze algorithm efficiency and understand time/space complexity.
Measure how algorithm runtime grows with input size, identify bottlenecks in code.
Analyze memory usage patterns, understand auxiliary space vs input space.
Express upper bounds on algorithm performance, compare algorithms objectively.
Understand different scenarios for algorithm performance and their implications.
Focus on dominant terms in complexity analysis, ignore constants and lower-order terms.
Learn O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ) and their characteristics.
Analyze nested loops, understand how loop structure affects time complexity.
Solve recurrence relations for divide-and-conquer algorithms systematically.
Master the fundamental sequential data structure and its operations.
Understand contiguous memory layout, zero-based indexing, and constant-time access.
Compare fixed-size arrays with resizable arrays, understand trade-offs.
Implement insertion, deletion, and search operations with proper complexity analysis.
Work with 2D arrays, matrices, and understand row-major vs column-major layout.
Build resizable arrays from scratch, handle memory allocation and deallocation.
Learn doubling strategy, amortized analysis, and when to shrink arrays.
Understand spatial locality, cache-friendly algorithms, and memory access patterns.
Master two-pointer technique, sliding window, and array manipulation algorithms.
Understand pointer-based data structures and dynamic memory allocation.
Build and manipulate one-way linked structures with head pointers.
Implement bidirectional links for efficient backward traversal and deletion.
Create circular structures where the last node points back to the first.
Design node classes with data and pointer fields, understand reference semantics.
Insert at beginning, end, and arbitrary positions while maintaining list integrity.
Remove nodes safely, handle edge cases, and prevent memory leaks.
Iterate through lists iteratively and recursively, detect cycles.
Compare performance characteristics, choose appropriate data structure.
Learn the Last-In-First-Out (LIFO) data structure and its applications.
Understand push, pop, peek, isEmpty operations and LIFO principle.
Implement stacks using arrays with top pointer, handle overflow conditions.