Data Structures and Algorithms
The problem-solving toolkit behind efficient code and technical interviews.
CurrentintermediateFull course available
Overview
Data structures (arrays, linked lists, trees, graphs, hash maps) organize data for efficient access; algorithms (sorting, searching, graph traversal) solve problems using them. Understanding time and space complexity (Big O) lets you reason about whether a solution will scale before you find out the hard way in production.
- What it is
- The study of how to organize data efficiently and reason about the cost of operations on it.
- Why it's used
- The same problem can run instantly or time out depending on the data structure and algorithm chosen -- this is the vocabulary for making (and explaining) that choice.
- Where it fits
- Language-independent; the concepts here apply whether you're writing Python, Java, or C++. Also the near-universal format of technical coding interviews. This platform's Data Structures and Algorithms course teaches this in full, browser-executable JavaScript/TypeScript, from Big O through graphs and dynamic programming.
Core concepts
- Big O notation (time and space complexity)
- Arrays, linked lists, stacks, queues
- Trees and graphs
- Hash maps
- Sorting and searching algorithms
- Recursion
Example
The same problem (find a value) has solutions with dramatically different scaling: O(n) checks every element, while O(log n) (binary search, requiring sorted data) eliminates half the remaining possibilities each step.
// Linear search: O(n) -- checks every element in the worst case
function linearSearch(arr, target) {
for (let i = 0; i < arr.length; i++) {
if (arr[i] === target) return i;
}
return -1;
}
// Binary search on a SORTED array: O(log n) -- halves the search space each stepCommon use cases
- Writing performant code at scale
- Technical interview preparation
- Recognizing when a data structure choice is the actual bottleneck
Project ideas
- Implement a linked list from scratch, including insert/delete/search operations
- Implement and compare linear search vs. binary search on the same dataset, timing both