Computer science learning path

My number #2 learning path.

Learning goals:

• Unlock potential to solve complex, challenging problems.
• Give me opportunity for properly organize the architecture of program/computing system.
• Make world better with efficient written programs, which solve human problems.

Computer science is closely related to programming learning path.

High-level topics

• computational complexity
• big O notation
• Asymptotic analysis
• Data Structures
  • array
  • set
  • Linked Lists
  • stack
  • queue
  • hash table
• More Knowledge
  • linear search
  • binary search
  • Bitwise operations
• Trees
  • tree
  • Binary search tree: BST
  • heap / Priority Queue / Binary Heap
  • balanced search trees (general concept, not details)
  • traversals: preorder, inorder, postorder, BFS, DFS
• sorting algorithm
  • selection sort
  • insertion sort
  • heapsort
  • quicksort
  • mergesort
• graph
  • directed
  • undirected
  • adjacency matrix
  • adjacency list
  • traversals: BFS, DFS
• Even More Knowledge
  • recursion
  • dynamic programming
  • Design Patterns
  • combinatorics
  • NP, NP-Complete and Approximation Algorithms
  • How computers process a program
  • cache computing
  • Processes and Threads
  • software testing
  • String searching & manipulations
  • Tries
  • floating-point arithmetic
  • Unicode
  • Endianness
  • Networking

Optional Extra Topics & Resources

• System Design, Scalability, Data Handling
• Additional Learning
  • compiler
  • Emacs and Neovim
  • Unix commands
  • information theory
  • Parity & Hamming Code
  • Entropy
  • cryptography
  • Compression
  • computer security
  • garbage collection
  • Parallel Programming
  • Messaging, Serialization, and Queueing Systems
  • A*
  • Fast Fourier Transform
  • Bloom Filter
  • HyperLogLog
  • Locality-Sensitive Hashing
  • van Emde Boas Trees
  • Augmented Data Structures
  • Balanced search trees
    • AVL trees
    • Splay trees
    • Red/black trees
    • 2-3 search trees
    • 2-3-4 Trees (aka 2-4 trees)
    • N-ary (K-ary, M-ary) trees
    • B-Trees
  • k-D Trees
  • Skip lists
  • Network Flows
  • Disjoint Sets & Union Find
  • Math for Fast Processing
  • Treap
  • Linear Programming
  • Geometry, Convex hull
  • Discrete math

Cheat sheets, glossaries, outlines

• Wikipedia-Glossary of computer science
• Algorithm
• Wikipedia - List of algorithms
• Nova Byrne-Tech yourself computer science

Resources

• The Man Who Revolutionized Computer Science With Math - YouTube

• Computerphile - Turing Machines Explained

• [/] Wengrow - Data structures and algorithms

• https://education.yandex.ru/handbook/algorithms

• [/] The Last Algorithms Course You’ll Need

• [/] Чарльз Петцольд - Код Тайный Язык Информатики

• [<] Polya - How to solve it

• [<] Warren - Hacker’s Delight

• [?] Cormen-Algorithms unlocked

• [!] Cormen et al-Introduction to algorithms. In fact, I need to improve my mathematics skills for this book.

• Washam Coding Interview University

• InterviewCake Team - Coding Interview Practice

• Martin-Interactive Coding Challenges

• Martin-System Design Primer

• Sedgewick and Wayne-Algorithms Part 1

• Sedgewick and Wayne-Algorithms Part 2

• Gusfield-Algorithms on Strings Trees and Sequences

• Hopcroft and Motwani and Ullman-Introduction to Automata Theory Languages and Computation

• LeetCode

• Data Structure Visualization

• Red Blob Games

• Visualising data structures and algorithms through animation

External links

• Хирьянов - Алгоритмы и структуры данных.
• Khan Academy - Computer science
• OpenCourseWare - Introduction to Computer Science and Programming in Python
• OpenCourseWare - Introduction to Computer Science and Programming
• San Diego University-Algorithmic Toolbox
• Stepic - computer science
• Algorithms Specialization
• Coursera - Algorithms on Graphs
• AlgoList - алгоритмы, методы, исходники
• Keon-Pythonic Data Structures
• Introduction to Computer Science and Programming in Python
• Python Data Structures/Algorithms Class
• Problem Solving with Algorithms and Data Structures using Python