Mr. Goldstein: Table of Contents: Computer Science

2025-07-05T02:53:58Z

Table of Contents: Computer Science

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Table of Contents
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I. Introduction to Computer Science
1.1 What is Computer Science?
Definition and Scope
Problem-Solving Focus
Interdisciplinary Nature
1.2 History of Computing
Early Calculating Devices (Abacus, Pascaline, Leibniz Wheel)
Analytical Engine (Babbage & Lovelace)
Early Electronic Computers (ENIAC, UNIVAC)
Transistors & Integrated Circuits
Personal Computers & the Internet
1.3 Key Figures in CS
Ada Lovelace, Alan Turing, Grace Hopper, Dennis Ritchie, Linus Torvalds, etc.
1.4 Branches of Computer Science
Theoretical CS, Algorithms, Data Structures, AI, ML, Cybersecurity, Networking, etc.
II. Foundational Concepts
2.1 Data Representation
Binary Numbers (Bits, Bytes)
Number Systems (Decimal, Binary, Octal, Hexadecimal)
Character Encoding (ASCII, Unicode, UTF-8)
Image, Audio, and Video Representation (Basic)
2.2 Boolean Algebra & Logic Gates
AND, OR, NOT, XOR, NAND, NOR gates
Truth Tables
Boolean Expressions & Simplification
2.3 Algorithms & Pseudocode
Definition of an Algorithm
Characteristics of Good Algorithms
Representing Algorithms (Flowcharts, Pseudocode)
2.4 Computational Thinking
Decomposition, Pattern Recognition, Abstraction, Algorithms
III. Programming Fundamentals
3.1 Programming Paradigms
Procedural Programming
Object-Oriented Programming (OOP) - Basic Concepts
Functional Programming (Basic Concepts)
3.2 Data Types & Variables
Primitive Data Types (Integers, Floats, Booleans, Characters)
Reference Data Types (Strings, Objects)
Variable Declaration and Initialization
3.3 Control Structures
Conditional Statements (if, else if, else, switch)
Looping Constructs (for, while, do-while)
3.4 Functions/Methods
Definition and Purpose
Parameters and Return Values
Scope
3.5 Basic Data Structures
Arrays (One-dimensional, Multi-dimensional)
Strings (Manipulation, Common Operations)
3.6 Error Handling & Debugging
Types of Errors (Syntax, Runtime, Logic)
Exception Handling (try-catch)
Debugging Techniques and Tools
IV. Data Structures & Algorithms (DSA)
4.1 Data Structures
Linear Data Structures:
Arrays (Fixed-size, Dynamic Arrays)
Linked Lists (Singly, Doubly, Circular)
Stacks (LIFO)
Queues (FIFO, Priority Queues)
Non-Linear Data Structures:
Trees (Binary Trees, Binary Search Trees, AVL Trees, Red-Black Trees)
Graphs (Directed, Undirected, Weighted)
Hash Tables (Hashing Functions, Collision Resolution)
Heaps (Min-Heap, Max-Heap)
4.2 Algorithms
Sorting Algorithms:
Bubble Sort, Selection Sort, Insertion Sort
Merge Sort, Quick Sort (Divide and Conquer)
Heap Sort, Radix Sort
Searching Algorithms:
Linear Search
Binary Search
Graph Algorithms:
Breadth-First Search (BFS)
Depth-First Search (DFS)
Dijkstra's Algorithm (Shortest Path)
Minimum Spanning Trees (Prim's, Kruskal's)
Dynamic Programming:
Memoization, Tabulation
Common DP Problems (Fibonacci, Knapsack)
Recursion:
Base Cases, Recursive Steps
Tail Recursion
Greedy Algorithms
V. Computer Architecture & Organization
5.1 CPU Components
Arithmetic Logic Unit (ALU)
Control Unit (CU)
Registers
Instruction Cycle (Fetch, Decode, Execute, Store)
5.2 Memory Hierarchy
Cache Memory (L1, L2, L3)
RAM (Random Access Memory)
ROM (Read-Only Memory)
Virtual Memory (Basic Concept)
5.3 Input/Output Systems
I/O Devices and Controllers
Polling, Interrupts, DMA
5.4 Instruction Sets
RISC vs. CISC
5.5 Assembly Language (Basic Concepts)
VI. Operating Systems (OS)
6.1 Role and Functions of an OS
Resource Management, Process Management, Memory Management, File Management
6.2 Process Management
Processes vs. Threads
Process States
CPU Scheduling Algorithms (FCFS, SJF, Priority, Round Robin)
Inter-Process Communication (IPC)
Synchronization (Semaphores, Mutexes)
6.3 Memory Management
Paging, Segmentation
Virtual Memory
Page Replacement Algorithms
6.4 File Systems
File Organization, Directory Structures
File Allocation Methods
6.5 Concurrency & Deadlock
Conditions for Deadlock
Deadlock Prevention, Avoidance, Detection, Recovery
VII. Networking & Internet
7.1 Network Models
OSI Model (7 Layers)
TCP/IP Model (4/5 Layers)
7.2 Protocols
HTTP/HTTPS, FTP, SMTP, POP3, IMAP
TCP (Reliable, Connection-Oriented)
UDP (Unreliable, Connectionless)
IP (Internet Protocol)
DNS (Domain Name System)
7.3 Network Topologies
Bus, Star, Ring, Mesh
7.4 Web Technologies (Basic Overview)
HTML, CSS, JavaScript (Client-side)
Web Servers, APIs
VIII. Databases
8.1 Database Types
Relational Databases (SQL)
NoSQL Databases (Key-Value, Document, Column-Family, Graph)
8.2 Relational Database Concepts
Tables, Rows, Columns
Primary Keys, Foreign Keys
Relationships (One-to-One, One-to-Many, Many-to-Many)
8.3 SQL (Structured Query Language)
CRUD Operations (SELECT, INSERT, UPDATE, DELETE)
JOINs, Subqueries
Data Definition Language (DDL)
Data Manipulation Language (DML)
8.4 Database Design
Normalization (1NF, 2NF, 3NF, BCNF)
Entity-Relationship (ER) Diagrams
8.5 Database Management Systems (DBMS)
Examples (MySQL, PostgreSQL, Oracle, SQL Server)
IX. Software Engineering
9.1 Software Development Life Cycle (SDLC)
Phases (Requirements, Design, Implementation, Testing, Deployment, Maintenance)
Models (Waterfall, Iterative, Spiral)
9.2 Agile Methodologies
Scrum, Kanban
User Stories, Sprints
9.3 Version Control
Git (Basic Commands: clone, add, commit, push, pull, branch, merge)
GitHub/GitLab/Bitbucket (Remote Repositories)
9.4 Software Testing
Unit Testing, Integration Testing, System Testing, Acceptance Testing
Test-Driven Development (TDD)
9.5 Design Patterns
Creational (Singleton, Factory)
Structural (Adapter, Decorator)
Behavioral (Observer, Strategy)
X. Artificial Intelligence (AI) & Machine Learning (ML)
10.1 Introduction to AI
Strong AI vs. Weak AI
Applications of AI
10.2 Types of Machine Learning
Supervised Learning (Regression, Classification)
Unsupervised Learning (Clustering, Dimensionality Reduction)
Reinforcement Learning
10.3 Common ML Algorithms
Linear Regression, Logistic Regression
Decision Trees, Random Forests
Support Vector Machines (SVM)
K-Means Clustering
10.4 Neural Networks & Deep Learning (Basic Concepts)
Perceptrons, Layers
Activation Functions
Backpropagation (High-level)
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
10.5 Natural Language Processing (NLP) / Computer Vision (CV)
Basic tasks and applications
XI. Cybersecurity
11.1 Fundamentals of Security
Confidentiality, Integrity, Availability (CIA Triad)
Authentication, Authorization, Accounting (AAA)
11.2 Common Threats & Attacks
Malware (Viruses, Worms, Ransomware)
Phishing, Social Engineering
Denial of Service (DoS) / Distributed Denial of Service (DDoS)
Man-in-the-Middle Attacks
11.3 Cryptography
Symmetric vs. Asymmetric Encryption
Hashing
Digital Signatures
11.4 Network Security
Firewalls, Intrusion Detection/Prevention Systems (IDS/IPS)
Virtual Private Networks (VPNs)
11.5 Web Security
Cross-Site Scripting (XSS)
SQL Injection
Cross-Site Request Forgery (CSRF)
XII. Theoretical Computer Science
12.1 Automata Theory
Finite Automata (DFAs, NFAs)
Regular Expressions
Context-Free Grammars & Pushdown Automata
12.2 Computability Theory
Turing Machines
Church-Turing Thesis
Halting Problem (Undecidability)
12.3 Complexity Theory
Time and Space Complexity (Big O Notation revisited)
P, NP, NP-Complete, NP-Hard
XIII. Emerging Topics & Future Trends
13.1 Quantum Computing
Basic Principles (Superposition, Entanglement)
Qubits, Quantum Gates
13.2 Blockchain & Distributed Ledger Technologies
Decentralization, Cryptocurrencies
Smart Contracts
13.3 Cloud Computing
IaaS, PaaS, SaaS
Public, Private, Hybrid Clouds
13.4 Big Data
Volume, Velocity, Variety
Hadoop, Spark (Basic Concepts)
13.5 Ethical AI & Societal Impact
Bias in AI, Privacy Concerns
AI Safety and Governance

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Mr. Goldstein: Table of Contents: Computer Science