I originally created this as a short to-do list of study topics for my personal use while in campus, but eventually to be used by my community DSC Kabarak University You probably won't have to study as much as I did. Anyway, feel free to contribute to anything you might feel to have been left out.
The items listed here should be a source of inspiration for your venture into deep computer science concepts in algorithms and data structure
Best of luck to you!
In preparation for this study plan, I have been using Introduction to Algorithms [CLRS09] by four devilishly handsome fellows. The book is commonly called “CLRS,” after the initials of the authors. Written in pseudocodes, I have been doing the implimentations in python programmming language, this has given me an in depth understanding of the topics covered
This is my multi-month study plan for going from web developer(self taught) to software engineer. It is meant for anyone starting out on algos or those switching from software/web development to software engineering (where computer science knowledge is required).
When I started this project, I didn't know a stack from a heap, didn't know Big-O anything, anything about trees, or how to traverse a graph. If I had to code a sorting algorithm, I can tell you it wouldn't have been very good. Every data structure I've ever used was built into the language, and I didn't know how they worked under the hood at all. I've never had to manage memory unless a process I was running would give an "out of memory" error, and then I'd have to find a workaround. I've used a few multidimensional arrays in my life and some of associative arrays, but I've never created data structures from scratch.
It's a long plan. It may take you months. If you are familiar with a lot of this already it will take you a lot less time.
This repository contains Python based examples of many popular algorithms and data structures.
Each algorithm and data structure has its own separate README with related explanations and links for further reading
☝ Note that this project is meant to be used for learning and researching purposes only and it is not meant to be used for production.
- Successful software engineers are smart, but many have an insecurity that they aren't smart enough.
- The myth of the Genius Programmer
- It's Dangerous to Go Alone: Battling the Invisible Monsters in Tech
- Believe you can change
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.
An algorithmic paradigm is a generic method or approach which underlies the design of a class of algorithms. It is an abstraction higher than the notion of an algorithm, just as an algorithm is an abstraction higher than a computer program.
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 precisely, 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.
▶ Data Structures and Algorithms on YouTube
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 you may find 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 | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
|---|---|---|---|
| O(1) | 1 | 1 | 1 |
| O(log N) | 3 | 6 | 9 |
| O(N) | 10 | 100 | 1000 |
| O(N log N) | 30 | 600 | 9000 |
| O(N^2) | 100 | 10000 | 1000000 |
| O(2^N) | 1024 | 1.26e+29 | 1.07e+301 |
| O(N!) | 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 |
- Growth of functions
- Divide and Conquer
- Probabilistic Analysis and Randomized Algorithms
- Heapsort
- Quicksort
- Sorting in Linear time
- Medians and order statistics
- Elementary Data Structures
- Hash Tables
- Binary Search Trees
- Red-Black Trees
- Augmenting Data Structures
- Dynamic Programming
- Greedy Algorithms
- Amortized Analysis
- B-Trees
- Fibonacci Heaps
- Van Emde Boas Trres
- Data Structures for Disjoint Sets
- Elementary Graphs and Algorithms
- Minimum Spanning Trees
- Single-Source Shortest Paths
- All-Pairs Shortest Paths
- Maximum Flow
- Multithread Algorithms
- Matrix Operations
- Linear Programming
- Polynomial and the FFT
- Number-Theoretic Algorithms
- String Matching
- Computational Geometry
- Np-Completeness
- Approximation Algorithms