Introduction to Computer Science

Table of Contents

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