Many developers who are "self-educated", feel that one of the main disadvantages they face compared to college/university educated graduates in computer science is that they don't have knowledge about algorithms, data structures, and the notorious/famous Big-O Notation. Get on the same level as someone with a computer science degree by learning the fundamental building blocks of computer science, which will boost you during real-life problem solving.
☝ Note that this repository is meant to be used for learning and researching purposes only.
Big O notation is used to classify algorithms according to how their running time or space requirements grow as the input size grows. On the chart below it is showing the most common orders of growth of algorithms specified in Big O notation.
Source: Big O Cheat Sheet.
Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.
| Big O Notation | Type | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
|---|---|---|---|---|
| O(1) | Constant | 1 | 1 | 1 |
| O(log N) | Logarithmic | 3 | 6 | 9 |
| O(N) | Linear | 10 | 100 | 1000 |
| O(N log N) | n log(n) | 30 | 600 | 9000 |
| O(N^2) | Quadratic | 100 | 10000 | 1000000 |
| O(2^N) | Exponential | 1024 | 1.26e+29 | 1.07e+301 |
| O(N!) | Factorial | 3628800 | 9.3e+157 | 4.02e+2567 |
| Data Structure | Access | Search | Insertion | Deletion | Comments |
|---|---|---|---|---|---|
| Array | 1 | n | n | n | |
| Stack | n | n | 1 | 1 | |
| Queue | n | n | 1 | 1 | |
| Linked List | n | n | 1 | n | |
| Hash Table | - | n | n | n | In case of perfect hash function costs would be O(1) |
| Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(n)) |
| B-Tree | log(n) | log(n) | log(n) | log(n) | |
| Red-Black Tree | log(n) | log(n) | log(n) | log(n) | |
| AVL Tree | log(n) | log(n) | log(n) | log(n) | |
| Bloom Filter | - | 1 | 1 | - | False positives are possible while searching |
| Name | Best | Average | Worst | Memory | Stable | Comments |
|---|---|---|---|---|---|---|
| Bubble sort | n | n2 | n2 | 1 | Yes | |
| Insertion sort | n | n2 | n2 | 1 | Yes | |
| Selection sort | n2 | n2 | n2 | 1 | No | |
| Heap sort | n log(n) | n log(n) | n log(n) | 1 | No | |
| Merge sort | n log(n) | n log(n) | n log(n) | n | Yes | |
| Quick sort | n log(n) | n log(n) | n2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space |
| Shell sort | n log(n) | depends on gap sequence | n (log(n))2 | 1 | No | |
| Counting sort | n + r | n + r | n + r | n + r | Yes | r - biggest number in array |
| Radix sort | n * k | n * k | n * k | n + k | Yes | k - length of longest key |
A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More exactly, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.
- ✅ done
- ⬜ Arrays
- ⬜ Hash Tables
- ⬜ Singly Linked Lists
- ⬜ Doubly Linked Lists
- ⬜ Queues
- ⬜ Stacks
- ⬜ Trees
- ⬜ BST
- ⬜ AVL
- ⬜ Trees
- ⬜ Red Black Trees,
- ⬜ Binary Heaps
- ⬜ Tries
- ⬜ Graphs
An algorithm is an unambiguous specification of how to solve a class of problems. It is a set of rules that precisely define a sequence of operations.
What you will learn in here.
Technical:
-
Big O notation
-
Data structures:
- Arrays
- Hash Tables
- Singly Linked Lists
- Doubly Linked Lists
- Queues
- Stacks
- Trees (BST, AVL Trees, Red Black Trees, Binary Heaps)
- Tries
- Graphs
- Algorithms:
- Recursion
- Sorting
- Searching
- Tree Traversal
- Breadth First Search
- Depth First Search
- Dynamic Programming