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Table of Contents

1.0 Introduction to Computer Science

1.1 What is Computer Science?

• 0.0.0 About this Wiki

• 1.1.1 Definition and Scope
• 1.1.2 Problem-Solving - Computational Thinking
• 1.1.3 Interdisciplinary Nature

1.2 History of Computing

• 1.2.1 Early Calculating Devices (Abacus, Pascaline, Leibniz Wheel)
• 1.2.2 Analytical Engine (Babbage & Lovelace)
• 1.2.3 Alan Turing: The Brilliant Mind Who Shaped Our Digital World
• 1.2.4 Early Electronic Computers (ENIAC, UNIVAC)
• 1.2.5 Transistors & Integrated Circuits
• 1.2.6 Personal Computers & the Internet

1.3 Key Figures in Computer Science

• 1.3.1 Ada Lovelace, Alan Turing, Grace Hopper, Dennis Ritchie, Linus Torvalds, etc.

1.4 Branches of Computer Science

• 1.4.1 Theoretical CS, Algorithms, Data Structures, AI, ML, Cybersecurity, Networking, etc.

2.0 Foundational Concepts

2.1 Data Representation

• 2.1.1 Binary Numbers (Bits, Bytes)
• 2.1.2 Number Systems (Decimal, Binary, Octal, Hexadecimal)
• 2.1.3 Character Encoding (ASCII, Unicode, UTF-8)
• 2.1.4 Image, Audio, and Video Representation (Basic)

2.2 Boolean Algebra & Logic Gates

• 2.2.1 AND, OR, NOT, XOR, NAND, NOR gates
• 2.2.2 Truth Tables
• 2.2.3 Boolean Expressions & Simplification

2.3 Algorithms & Pseudocode

• 2.3.1 Definition of an Algorithm
• 2.3.2 Characteristics of Good Algorithms
• 2.3.3 Representing Algorithms (Flowcharts, Pseudocode)

2.4 Computational Thinking

• 2.4.1 Decomposition
• 2.4.2 Pattern Recognition
• 2.4.3 Abstraction
• 2.4.4 Algorithms

3.0 Programming Fundamentals

3.1 Programming Paradigms

• 3.1.1 Procedural Programming
• 3.1.2 Object-Oriented Programming (OOP) - Basic Concepts
• 3.1.3 Functional Programming (Basic Concepts)

3.2 Data Types & Variables

• 3.2.1 Primitive Data Types (Integers, Floats, Booleans, Characters)
• 3.2.2 Reference Data Types (Strings, Objects)
• 3.2.3 Variable Declaration and Initialization

3.3 Control Structures

• 3.3.1 Conditional Statements (if, else if, else, switch)
• 3.3.2 Looping Constructs (for, while, do-while)

3.4 Functions/Methods

• 3.4.1 Definition and Purpose
• 3.4.2 Parameters and Return Values
• 3.4.3 Scope

3.5 Basic Data Structures

• 3.5.1 Arrays (One-dimensional, Multi-dimensional)
• 3.5.2 Strings (Manipulation, Common Operations)

3.6 Error Handling & Debugging

• 3.6.1 Types of Errors (Syntax, Runtime, Logic)
• 3.6.2 Exception Handling (try-catch)
• 3.6.3 Debugging Techniques and Tools

4.0 Data Structures Algorithms (DSA)

4.1 Data Structures

• 4.1.1 Linear Data Structures:
  • 4.1.1.1 Arrays (Fixed-size, Dynamic Arrays)
  • 4.1.1.2 Linked Lists (Singly, Doubly, Circular)
  • 4.1.1.3 Stacks (LIFO)
  • 4.1.1.4 Queues (FIFO, Priority Queues)

• 4.1.2 Non-Linear Data Structures:
  • 4.1.2.1 Trees (Binary Trees, Binary Search Trees, AVL Trees, Red-Black Trees)
  • 4.1.2.2 Graphs (Directed, Undirected, Weighted)
  • 4.1.2.3 Hash Tables (Hashing Functions, Collision Resolution)
  • 4.1.2.4 Heaps (Min-Heap, Max-Heap)
• 4.2 Algorithms
  • 4.2.1 Sorting Algorithms:
    • 4.2.1.1 Bubble Sort, Selection Sort, Insertion Sort
    • 4.2.1.2 Merge Sort, Quick Sort (Divide and Conquer)
    • 4.2.1.3 Heap Sort, Radix Sort
  • 4.2.2 Searching Algorithms:
    • 4.2.2.1 Linear Search
    • 4.2.2.2 Binary Search
  • 4.2.3 Graph Algorithms:
    • 4.2.3.1 Breadth-First Search (BFS)
    • 4.2.3.2 Depth-First Search (DFS)
    • 4.2.3.3 Dijkstra's Algorithm (Shortest Path)
    • 4.2.3.4 Minimum Spanning Trees (Prim's, Kruskal's)
  • 4.2.4 Dynamic Programming:
    • 4.2.4.1 Memoization, Tabulation
    • 4.2.4.2 Common DP Problems (Fibonacci, Knapsack)
  • 4.2.5 Recursion:
    • 4.2.5.1 Base Cases, Recursive Steps
    • 4.2.5.2. Tail Recursion
  • 4.2.6 Greedy Algorithms

5.0 Computer Architecture & Organization

• 5.1 CPU Components
  • 5.1.1 Arithmetic Logic Unit (ALU)
  • 5.1.2 Control Unit (CU)
  • 5.1.3 Registers
  • 5.1.4 Instruction Cycle (Fetch, Decode, Execute, Store)
• 5.2 Memory Hierarchy
  • 5.2.1 Cache Memory (L1, L2, L3)
  • 5.2.2 RAM (Random Access Memory)
  • 5.2.3 ROM (Read-Only Memory)
  • 5.2.4 Virtual Memory (Basic Concept)
• 5.3 Input/Output Systems
  • 5.3.1 I/O Devices and Controllers
  • 5.3.2 Polling, Interrupts, DMA
• 5.4 Instruction Sets
  • 5.4.1 RISC vs. CISC
• 5.5 Assembly Language (Basic Concepts)
• 5.6 How a Computer Works

6.0 Operating Systems (OS)

• 6.1 Role and Functions of an OS
  • 6.1.1 Resource Management, Process Management, Memory Management, File Management
• 6.2 Process Management
  • 6.2.1 Processes vs. Threads
  • 6.2.2 Process States
  • 6.2.3 CPU Scheduling Algorithms (FCFS, SJF, Priority, Round Robin)
  • 6.2.4 Inter-Process Communication (IPC)
• 6.2.5 Synchronization (Semaphores, Mutexes)
• 6.3 Memory Management
  • 6.3.1 Paging, Segmentation
  • 6.3.2 Virtual Memory
  • 6.3.3 Page Replacement Algorithms
• 6.4 File Systems
  • 6.4.1 File Organization, Directory Structures
  • 6.4.2 File Allocation Methods
• 6.5 Concurrency & Deadlock
  • 6.5.1 Conditions for Deadlock
  • 6.5.2 Deadlock Prevention, Avoidance, Detection, Recovery

7.0 Images & Graphics

• 7.1 What is a Digital Image?
  • 7.1.1 Pixels and Resolution
  • 7.1.2 How Computers See Color (RGB, basic idea)
• 7.2 Image File Formats
  • 7.2.1 JPG (for photos)
  • 7.2.2 PNG (for graphics, transparency)
  • 7.2.3 GIF (for simple animations, limited colors)
• 7.3 Computer Graphics: Making Digital Art
  • 7.3.1 Creating Images (drawing, 3D models - simple concepts)
  • 7.3.2 Animation Basics (moving images)
• 7.4 Image Processing: Changing Pictures
  • 7.4.1 Basic Edits (brightness, contrast, filters)
  • 7.4.2 Why We Process Images (fixing, enhancing, effects)
• 7.5 Computer Vision: Teaching Computers to See (Brief, exciting examples)
  • 7.5.1 Facial Recognition
  • 7.5.2 Object Detection (self-driving cars, sorting)

8.0 Video & Multimedia

• 8.1 What is Digital Video? (Explaining frames, frame rate, audio)
• 8.2 Video File Formats (MP4, MOV, etc.)
• 8.3 Video Compression (Basic idea of making files smaller)
• 8.4 Video Streaming (How YouTube/Netflix works)
• 8.5 Basic Audio Concepts (Sound waves to digital, basic formats)

9.0 Networking & Internet

• 9.1 Network Models
  • 9.1.1 OSI Model (7 Layers)
  • 9.1.2 TCP/IP Model (4/5 Layers)
• 9.2 Protocols
  • 9.2.1 HTTP/HTTPS, FTP, SMTP, POP3, IMAP
  • 9.2.2 TCP (Reliable, Connection-Oriented)
  • 9.2.3 UDP (Unreliable, Connectionless)
  • 9.2.4 IP (Internet Protocol)
  • 9.2.5 DNS (Domain Name System)
• 9.3 Network Topologies
  • 9.3.1 Bus, Star, Ring, Mesh
• 9.4 Web Technologies (Basic Overview)
  • 9.4.1 HTML, CSS, JavaScript (Client-side)
  • 9.4.2 Web Servers, APIs

10.0 Databases

• 10.1 Database Types
  • 10.1.1 Relational Databases (SQL)
  • 10.1.2 NoSQL Databases (Key-Value, Document, Column-Family, Graph)
• 10.2 Relational Database Concepts
  • 10.2.1 Tables, Rows, Columns
  • 10.2.2 Primary Keys, Foreign Keys
  • 10.2.3 Relationships (One-to-One, One-to-Many, Many-to-Many)
• 10.3 SQL (Structured Query Language)
  • 10.3.1 CRUD Operations (SELECT, INSERT, UPDATE, DELETE)
  • 10.3.2 JOINs, Subqueries
  • 10.3.3 Data Definition Language (DDL)
  • 10.3.4 Data Manipulation Language (DML)
• 10.4 Database Design
  • 10.4.1 Normalization (1NF, 2NF, 3NF, BCNF)
  • 10.4.2 Entity-Relationship (ER) Diagrams
• 10.5 Database Management Systems (DBMS)
  • 10.5.1 Examples (MySQL, PostgreSQL, Oracle, SQL Server) IX. Software Engineering

11.0 Software Engineering

• 11.1 Software Development Life Cycle (SDLC)
  • 11.1.1. Phases (Requirements, Design, Implementation, Testing, Deployment, Maintenance)
  • 11.1.2 Models (Waterfall, Iterative, Spiral)
• 11.2 Agile Methodologies
  • 11.2.1 Scrum, Kanban
  • 11.2.2 User Stories, Sprints
• 11.3 Version Control
  • 11.3.1 Git (Basic Commands: clone, add, commit, push, pull, branch, merge)
  • 11.3.2 GitHub/GitLab/Bitbucket (Remote Repositories)
• 11.4 Software Testing
  • 11.4.1 Unit Testing, Integration Testing, System Testing, Acceptance Testing
  • 11.4.2 Test-Driven Development (TDD)
• 11.5 Design Patterns
  • 11.5.1 Creational (Singleton, Factory)
  • 11.5.2 Structural (Adapter, Decorator)
  • 11.5.3 Behavioral (Observer, Strategy) X. Artificial Intelligence (AI) & Machine Learning (ML)

12.0 Artificial Intelligence (AI) & Machine Learning

• 12.1 Introduction to AI
  • 12.1.1 Strong AI vs. Weak AI
  • 12.1.2 Applications of AI
• 12.2 Types of Machine Learning
  • 12.2.1 Supervised Learning (Regression, Classification)
  • 12.2.2 Unsupervised Learning (Clustering, Dimensionality Reduction)
    • 12.2.3 Reinforcement Learning
• 12.3 Common ML Algorithms
  • 12.3.1 Linear Regression, Logistic Regression
  • 12.3.2 Decision Trees, Random Forests
  • 12.3.3 Support Vector Machines (SVM)
  • 12.3.4 K-Means Clustering
• 10.4 Neural Networks & Deep Learning (Basic Concepts)
  • 12.4.1 Perceptrons, Layers
  • 12.4.2 Activation Functions
  • 12.4.3 Backpropagation (High-level)
  • 12.4.4 Convolutional Neural Networks (CNNs)
  • 12.4.5 Recurrent Neural Networks (RNNs)
• 12.5 Natural Language Processing (NLP) / Computer Vision (CV)
  • 12.5.1 Basic tasks and applications
  • 12.5.2 Large Language Models (LLMs)
    • 12.5.2.1 Generative Pre-trained Transformers (GPT)

13.0 Cybersecurity

• 13.1 Fundamentals of Security
  • 13.1.1 Confidentiality, Integrity, Availability (CIA Triad)
  • 13.1.2 Authentication, Authorization, Accounting (AAA)
• 13.2 Common Threats & Attacks
  • 13.2.1 Malware (Viruses, Worms, Ransomware)
  • 13.2.2 Phishing, Social Engineering
  • 13.2.3 Denial of Service (DoS) / Distributed Denial of Service (DDoS)
  • 13.2.4 Man-in-the-Middle Attacks
• 13.3 Cryptography
  • 13.3.1 Symmetric vs. Asymmetric Encryption
  • 13.3.2 Hashing
  • 13.3.3 Digital Signatures
• 13.4 Network Security
  • 13.4.1 Firewalls, Intrusion Detection/Prevention Systems (IDS/IPS)
  • 13.4.2 Virtual Private Networks (VPNs)
• 13.5 Web Security
  • 13.5.1 Cross-Site Scripting (XSS)
  • 13.5.2 SQL Injection
  • 13.5.3 Cross-Site Request Forgery (CSRF) XII. Theoretical Computer Science

14.0 Theoretical Computer Science

• 14.1 Automata Theory
  • 14.1.1 Finite Automata (DFAs, NFAs)
  • 14.1.2 Regular Expressions
  • 14.1.3 Context-Free Grammars & Pushdown Automata
• 14.2 Computability Theory
  • 14.2.1 Turing Machines
  • 14.2.2 Church-Turing Thesis
  • 14.2.3 Halting Problem (Undecidability)
• 14.3 Complexity Theory
  • 14.3.1 Time and Space Complexity (Big O Notation revisited)
  • 14.3.2 P, NP, NP-Complete, NP-Hard XIII. Emerging Topics & Future Trends

15.0 Emerging Topics and Future Trends

• 15.1 Quantum Computing
  • 15.1.1 Basic Principles (Superposition, Entanglement)
  • 15.1.2 Qubits, Quantum Gates
• 15.2 Blockchain & Distributed Ledger Technologies
  • 15.2.1 Decentralization, Cryptocurrencies
  • 15.2.2 Smart Contracts
• 15.3 Cloud Computing
  • 15.3.1 IaaS, PaaS, SaaS
  • 15.3.2 Public, Private, Hybrid Clouds
• 15.4 Big Data
  • 15.41 Volume, Velocity, Variety
  • 15.42 Hadoop, Spark (Basic Concepts)
• 15.5 Ethical AI & Societal Impact
  • 15.51 Bias in AI, Privacy Concerns
  • 15.52 AI Safety and Governance

16.0 Resources

• 16.1 Code.org
• 16.2 Hour of Code
• 16.3 Khan Academy
• 16.4 Scratch
• 16.5 Skillstruck.com
• 16.6 Harvard CS50
  • 16.61 Harvard CS50 (2023) Full Computer Science University Course

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