Data Structure & Algorithms
A pattern-based approach to mastering the DSA questions that form the core of technical interviews.
Prerequisites: A foundational understanding of programming concepts, basic data structures (e.g., arrays, linked lists, trees, graphs), and algorithm analysis (time and space complexity) is recommended.
Warning: This course is still under development, but the good stuff is already here.
Time-Space Tradeoffs
Using extra space to save time.
Linear Patterns (1D)
Iterating through 1D data.
Divide and Conquer
Breaking down problems into smaller pieces.
Stacks
LIFO structure for managing nested data.
Heaps (Priority Queues)
Finding the largest or smallest elements efficiently.
Intervals
Managing and merging ranges of data.
Trees & Graphs: Traversal & Backtracking
Exploring hierarchical and networked data.
Matrix/Grid Patterns (2D)
Navigating and searching in 2D data structures.
Dynamic Programming
Breaking down complex problems into simpler subproblems to find optimal solutions
Beyond This Course
This course focuses on the most frequently encountered DSA patterns in technical interviews. Topics like advanced graph algorithms (e.g., network flow, minimum spanning trees), advanced string algorithms (KMP, Rabin Karp), and bit manipulation are not covered here, but are excellent areas to explore for further mastery.