Computer Science

In computer science central role plays ==algorithms and data structures==.

Active sources:

• Wengrow-Data structures and algorithms
• LeetCode - research and solve problems.
• Jay Vs. Leetcode
• Code Interview University
• informatika

High-level

• algorithm
• big O notation
• computational complexity
• Asymptotic analysis
• data structure
• More Knowledge
  • linear search
  • binary search
  • Bitwise operations
• sorting algorithm
  • selection sort
  • insertion sort
  • heapsort
  • quicksort
  • mergesort
• Even More Knowledge

  • recursion

  • dynamic programming

  • programming principles and design patterns

  • ❗ combinatorics

  • NP, NP-Complete and Approximation Algorithms

  • How computers process a program

  • cache computing

  • Processes and Threads

  • software testing

  • String searching & manipulations

  • 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
  • concurency and parallel computing
  • Messaging, Serialization, and Queueing Systems
  • A*
  • Fast Fourier Transform
  • Bloom Filter
  • HyperLogLog
  • Locality-Sensitive Hashing
  • van Emde Boas Trees
  • Augmented Data Structures
  • Skip lists
  • Network Flows
  • Disjoint Sets & Union Find
  • Math for Fast Processing
  • Treap
  • Linear Programming
  • Geometry, Convex hull
  • Discrete math

Inbox

• Cracking the Coding Interview

• InterviewCake Team - Coding Interview Practice

• Aziz and Lee and Prakash-Elements of Programming Interviews

• Dewdney - The New Turing Omnibus

• Bentley - Programming Pearls

• Dromey - How to Solve It by Computer

• is looping through an array easier on the memory than a long recursion

• CRDT: Conflict-free Replicated Data Types / Хабр

• Cormen et al-Introduction to algorithms, read and take notes.

• ❗ Реализация алгоритма Дейкстры на Python — Записки преподавателя, html archive

• Основы алгоритмов — Хендбук от Яндекс Образования

• LeetCode, research a topic

• Jay Vs. LeetCode

• Data Structure Visualization

• Visualising data structures and algorithms through animation

• Red Blob Games

• Algorithms Specialization (Stanford) Coursera

• The Last Algorithms Course You’ll Need. I’m planning to use it as verification of Wengrow book.

• Understanding the formal definition of Big-O

• Flake - The computational beauty of nature

• Cormen-Algorithms unlocked

• Competitive Programmer’s Handbook

• Christian - Algorithms to Live By

• Laaksonen - Olimpiadnoie proghrammirovaniie

• Wikipedia-Glossary of computer science

• Wikipedia - List of algorithms

• Nova Byrne-Tech yourself computer science

• The Man Who Revolutionized Computer Science With Math - YouTube

• Computerphile - Turing Machines Explained

• The Last Algorithms Course You’ll Need

• Polya - How to solve it

• Warren - Hacker’s Delight

• 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 et al-Introduction to automata theory languages and computation

• 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

• Continually updated, interactive, and test-driven coding challenges, with Anki flashcards.

Core concepts in computer science

Core computer science concepts from Wikipedia ¹.

The theory of computation helping to create theoretical models (abstract models) of how computers work (computation) and understanding the types of problems (general classes of problems) these computers can solve. So it’s about creating abstract computing models and understand which general classes of problems these models can solve.

Cryptography and computer security fields is needed to study (and practice) how prevent security vulnerabilities and how to communicate securely (secure data transmission).

Computer graphics and computational geometry address the generation of images.

Programming language theory considers different ways to describe computational processes. Different programming languages can implement computational processes in different ways and can be used to solve different classes of problems.

Database theory concerns the management of repositories of data. For example, you have a bunch of messy data (desktop shortcuts), and you can organize them in some structures (group, sort, etc.) to manage them more effectively.

Human–computer interaction investigates the interfaces through which humans and computers interact. There many UI specialists, which usually haven’t developing software skills, but they know how to create effective UI.

Software engineering focuses on the design and principles behind developing software. And usually software engineers solving problems in real world with requirements from customers.

There are many areas (complex systems), which investigate the principles and design in their own area. Main areas are: operating systems, computer network and embedded systems.

What describes Computer Architecture?
Computer architecture describes the construction of computer components and computer-operated equipment (Input, Output, CPU, RAM).

Goal of Artificial intelligence?
Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. In other words replicate humans and animals processes/behavior.

Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

The Turing Award is generally recognized as the highest distinction in computer science.

References

Footnotes

1. Computer science - Wikipedia ↩