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Computer Science (WiP),

Computer Science (WiP)

Algorithms & Data Structures

Algorithms

Sorting

divide-&-conquer O(n log n) Merge Sort
Divides the array into halves, recursively sorts each half, and then merges the sorted halves

divide-&-conquer O(n log n) Quick Sort
Selects a pivot element and partitions the array around it.
All elements less than come before it, all elements greater than come after it.
It then recursively applies the same process to the sub-arrays

Heap Sort
Builds a max (or min) heap from the array and repeatedly extracts the maximum (or minimum) element, placing it at the end of the array

Radix / Counting / Bucket / Shell Sort

O(n²) Bubble Sort
Compares adjacent elements and swaps them if they are in the wrong order

O(n²) Selection Sort
Identifies for each pass the largest (or smallest) element and swap them with the last (or first) element of the unsorted part of the list

O(n²) Insertion Sort
Builds the final sorted array one element at a time by inserting each element into its correct position within the sorted portion of the array

Search

Breadth-First Search (BFS)

Depth-First Search (DFS)

Binary Search

Linear Search

Graph

Dijkstra's Algorithm (Shortest Path)

Bellman-Ford Algorithm (Shortest Path)

Prim's Algorithm (Minimum Spanning Tree)

Kruskal's Algorithm (Minimum Spanning Tree)

Concepts

Brute Force
Solving a problem by trying all possible solutions without employing optimization techniques

Bit Manipulation
Manipulating bits in binary to perform various operations efficiently (bitwise AND, OR, XOR, shifting, and masking)

Dynamic Programming
Recursion w/ storing the subproblems solutions to avoid redundant computations

Time/Space Complexity (Big O)
Measure how the performance of an algorithm or data structure scales with the input size

Recursion
A procedure that calls itself to solve a problem by breaking it down into smaller, similar subproblems

Backtracking
Explores all possible solutions and backtracking when one solution yield only a partial result

String Matching
Finding a string occurrence or pattern within a larger string

Computational Geometry
Design and analysis of algorithms for solving geometric problems (intersections, closest pairs of points)

Divide & Conquer
Breaking a problem into smaller subproblems, solving them independently, and combining their solutions to solve the original problem

Data Structures

Hash Map / Hash Table / Dictionary

Lists

Linked List (single, double, circular)

Stack

Queue

Array / Vector / List

Set

Basics

String

Integer

Float

Tree

Heap

Binary Tree

Binary Search Tree

Trie (Prefix Tree)

Graph

Programming

Design Patterns

Paradigms

Agent-oriented programming

Aspect-oriented programming (AOP)

Component-based software engineering

Functional programming (FP)

Literate programming

Logic programming

Modular programming

Object-oriented programming (OOP)

Reactive programming

Computer Architecture

Computer Networking

HTTP

Status Codes

1xx Informational

2xx Success

200 OK

201 Created

3xx Redirection

301 Moved Permanently

4xx Client Error

400 Bad Request

401 Unauthorized

403 Forbidden

404 Not Found

5xx Server Error

500 Internal Server Error

502 Bad Gateway

Methods

GET

POST (create)

PUT (update/replace)

PATCH (partial update)

DELETE

Headers

Caching

CORS

Content Info (Size, Type)

application/json

application/x-www-form-urlencoded

multipart/form-data (file exchanges)

text/plain

text/html

image/png

Cookies

Authentication

HTTP 2/3

URL

Authentication

Learning Authentication

OAuth2

OIDC

SAML

Databases

SQL

Basics

Create Table
CREATE TABLE [ IF NOT EXISTS ] users (
id INTEGER/SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL,
dateCreated TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Delete Table
DROP TABLE [ IF EXISTS ] users;

Insert Row
INSERT INTO users (name) VALUES (’John’);

Distributed Systems

Operating Systems

Machine Learning

Move to CS MM:

Relational DB concepts

NoSQL DB concepts

Postgres

Mongodb

Redis

TypeScript

JavaScript

Python

Postgres:

Migration Strategy

Schemas

Roles

Postgis

Full text search

JSON Blobs

CTE (WITH AS)

Logical Replication (pub/sub)

Window functions

Redis:

High Availability

High Performance

High level data structure storage (strings, lists, sets, hashes, and sorted sets)

JavaScript
Single threaded, non-blocking, async, C like language

Language Features

ES6+ additions, with notably:

Syntax - let/const variables, arrow functions, for of loops, template string, destructuring/spreading for array object & arguments

Standard lib - Array, Object and String prototype improvements, new Set & Map objects

New Concepts - Classes, ES Modules, Promise, Generators, async/away flow

Language Basics

Syntax - Conditions, Comparisons, Variables, Operators, Functions

Data types - Number, Object, String, Array, Boolean, Date

Advanced - Modules, JSON, RegExp, Prototype inheritance

Standard Library

Event loop (video)

Python

Language Features

Decorators

Context Managers

Class internals overriding

Language Basics

Syntax - Conditions, Comparisons, Variables, Operators, Functions, Class

Data types - Number, String, List, Boolean, Tuple, Dictionary

Advanced - Modules, Exception, Regex

Standard library - os, sys, re, math, random, time, etc.

Ressources:

CS learning path (Info)

How to teach yourself CS (Info)

Teach yourself CS (Info)

Coursera (Videos)

Web Dev in 2020 (Mind Map)

Java Dev in 2018 (Mind Map)

Scaling to 100k users (Article)

LeetCode / HackerRank (Interview training)

Intro to Distributed Systems (Article)

What is big data?

SQL Article

https://gist.github.com/jboner/2841832

SQL:
Create table
CREATE TABLE [ IF NOT EXISTS ] users (
id INTEGER/SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL,
dateCreated TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
Options: https://www.postgresql.org/docs/9.1/static/sql-createtable.html

Delete table
DROP TABLE [ IF EXISTS ] users;
Options: https://www.postgresql.org/docs/9.1/static/sql-droptable.html

Edit table
ALTER TABLE users ADD COLUMN address varchar(30);
ALTER TABLE users
ALTER COLUMN address TYPE varchar(80),
ALTER COLUMN name TYPE varchar(100);
Options: https://www.postgresql.org/docs/9.1/static/sql-altertable.html

Insert row
INSERT INTO users (name) VALUES (’Allan’);
Options: https://www.postgresql.org/docs/9.1/static/sql-insert.html

Select rows
SELECT * FROM users;
SELECT id, name, dateCreated AS ‘date_created’
FROM USERS
WHERE name = ‘Allan’
OR name = ‘Bto’
AND id > 5
ORDER BY dateCreated ASC/DESC, name;
Options: https://www.postgresql.org/docs/9.1/static/sql-select.html

Update rows
UPDATE users
SET name = ‘Allan’
WHERE name = ‘Bto’
[RETURNING ];
Options: https://www.postgresql.org/docs/9.1/static/sql-update.html

Delete rows
DELETE FROM users
WHERE name = ‘Allan’
[RETURNING ];
Options: https://www.postgresql.org/docs/9.1/static/sql-delete.html

Joins
https://www.tutorialspoint.com/postgresql/postgresql_using_joins.htm
CROSS JOIN
A CROSS JOIN matches every row of the first table with every row of the second table. If the input tables have x and y columns, respectively, the resulting table will have x+y columns.
/!\ Because CROSS JOINs have the potential to generate extremely large tables, care must be taken to only use them when appropriate.
INNER JOIN
A INNER JOIN creates a new result table by combining column values of two tables (table1 and table2) based upon the join-predicate.
Most common.

LEFT OUTER JOIN
In case of LEFT OUTER JOIN, an inner join is performed first. Then, for each row in table T1 that does not satisfy the join condition with any row in table T2, a joined row is added with null values in columns of T2. Thus, the joined table always has at least one row for each row in T1.

RIGHT OUTER JOIN
First, an inner join is performed. Then, for each row in table T2 that does not satisfy the join condition with any row in table T1, a joined row is added with null values in columns of T1. This is the converse of a left join; the result table will always have a row for each row in T2.

FULL OUTER JOIN
First, an inner join is performed. Then, for each row in table T1 that does not satisfy the join condition with any row in table T2, a joined row is added with null values in columns of T2. Also, for each row of T2 that does not satisfy the join condition with any row in T1, a joined row with null values in the columns of T1 is added.

WORK IN PROGRESS

Legend:

:blue_book:: Proficient Knowledge

:octopus:: Usable Knowledge

:green_book:: Learning In Progress

:orange_book:: Would like to learn

:black_circle:: To check later

:grey_question:: Mind Map in Progress

TODO:

Read about & Write summary for each concepts

Read about & Write summary for each algs

Read about & Write summary for each data structures

Implement all algs listed here in Python

Implement all data structures listed here in Python

Do a few exercices from LeetCode / HackerRank

Sorting Algorithms:
Bubble Sort
Insertion Sort
Selection Sort
Merge Sort
Quick Sort
Heap Sort
Radix Sort
Counting Sort
Searching Algorithms:
Linear Search
Binary Search
Depth-First Search (DFS)
Breadth-First Search (BFS)
Graph Algorithms:
Dijkstra's Algorithm (Shortest Path)
Bellman-Ford Algorithm (Shortest Path)
Prim's Algorithm (Minimum Spanning Tree)
Kruskal's Algorithm (Minimum Spanning Tree)
Depth-First Search (DFS)
Breadth-First Search (BFS)
Dynamic Programming:
Fibonacci Sequence
Knapsack Problem
Longest Common Subsequence
Matrix Chain Multiplication
Edit Distance
Divide and Conquer:
Binary Search
Merge Sort
Quick Sort
Greedy Algorithms:
Kruskal's Algorithm (Minimum Spanning Tree)
Prim's Algorithm (Minimum Spanning Tree)
Dijkstra's Algorithm (Shortest Path)
Huffman Coding (Data Compression)
Backtracking:
N-Queens Problem
Sudoku Solver
Knight's Tour
String Matching:
Brute Force
Knuth-Morris-Pratt (KMP) Algorithm
Boyer-Moore Algorithm
Rabin-Karp Algorithm
Computational Geometry:
Convex Hull
Line Intersection
Closest Pair of Points
Numerical Algorithms:
Binary Exponentiation
Euclidean Algorithm (GCD)
Sieve of Eratosthenes (Prime Numbers)
Newton's Method (Root Finding)
Machine Learning Algorithms:
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Neural Networks
Clustering Algorithms (e.g., K-Means, DBSCAN)
Cryptography:
RSA Algorithm
Diffie-Hellman Key Exchange
AES Encryption
Hash Functions (e.g., MD5, SHA-1, SHA-256)