> For the complete documentation index, see [llms.txt](https://docs.outtalent.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.outtalent.com/guides/common-data-structures-and-algorithms.md).

# Data structures and algorithms

* List-based data structures
  * List
  * Array
  * Python List
  * Linked List
  * Queue, Stack 
* Set, Map, and hashing
* Bit manipulation
* Tree - one of the most popular topics at big tech interviews
  * Tree algorithms
    * Tree traversal (BFS, DFS)
    * Search and delete
    * Insert
  * Specialized trees
    * Binary Search and Binary Search Tree
    * Heaps - one of the most popular topics at big tech interviews
    * Heapify and HeapSort
* Graph
  * BFS & DFS for graphs, Adjacency matrix
* Computation complexity and asymptotic notation
* Basic sorting algorithms
  * Insertion Sort
  * Selection Sort
  * Bubble Sort
  * Merge Sort
* Intermediary algorithms
  * Recursion, dynamic programming, backtracking - popular at Google
  * Advanced sorting algorithms: Radix Sort, QuickSort

In addition to that in coding interviews, you will get problems that require mathematical calculations (including basics of statistics and geometry).

Topics we recommend deprioritizing while preparing for interviews:

1. Sweep line algorithm
2. Dijkstra's algorithm

Most recommended resources:

* [Google’s Intro to Data Structures and Algorithms course on Udacity](https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513). It is focused on helping you pass a Google interview.
* [Data Structures and Algorithms Specialization](https://www.coursera.org/specializations/data-structures-algorithms). This was most recommended by Outtalent graduates.