Overview of Computer Science

Definition: Study of computer systems, algorithms, and data processing.
Branches:
- Theoretical Computer Science
- Software Engineering
- Computer Hardware
- Networking
- Artificial Intelligence
- Data Science
- Human-Computer Interaction

Algorithms:
- Step-by-step procedures for calculations.
- Complexity: Time and space complexity (Big O notation).
Data Structures:
- Ways to store and organize data.
- Common types: Arrays, Linked Lists, Stacks, Queues, Trees, Graphs.
Programming Languages:
- Types: High-level (Python, Java) vs. Low-level (C, Assembly).
- Paradigms: Object-oriented, Functional, Procedural.
Operating Systems:
- Manages hardware and software resources.
- Examples: Windows, macOS, Linux.
Networks:
- Communication systems connecting computers.
- Protocols: TCP/IP, HTTP, FTP.

Life Cycle:
- Phases: Requirement analysis, Design, Implementation, Testing, Maintenance.
Methodologies:
- Agile, Waterfall, DevOps.

Fundamentals:
- Cybersecurity practices to protect data.
- Concepts: Authentication, Encryption, Threat Analysis.

Algorithms: Step-by-step procedures for calculations.
- Algorithms can be analyzed for their time and space complexity using Big O notation.
Data Structures: Ways to store and organize data.
- Common data structures include Arrays, Linked Lists, Stacks, Queues, Trees, and Graphs
Programming Languages: Used to communicate with computers.
- High-level languages (Python, Java) are easier to understand, while low-level languages (C, Assembly) provide more direct control over hardware.
- Programming paradigms include Object-oriented, Functional, and Procedural.
Operating Systems: Manage hardware and software resources.
- Popular operating systems include Windows, macOS, and Linux.
Networks: Systems that connect computers.
- Networks use protocols such as TCP/IP, HTTP, and FTP for communication.

Life Cycle: Phases include requirement analysis, design, implementation, testing, and maintenance.
Methodologies: Agile, Waterfall, and DevOps are common approaches to software development.

Types:
- Relational databases use SQL (Structured Query Language) for data management.
- Non-relational (NoSQL) databases offer alternative data models for specific needs.
Key Operations: CRUD (Create, Read, Update, Delete) are fundamental database operations.

Subfields:
- Machine Learning: Enables computers to learn from data without explicit programming.
- Natural Language Processing: Allows computers to understand and process human language.
- Robotics: Design and development of robots.
Techniques:
- Neural Networks: Inspired by biological neural networks, used for complex tasks like image recognition.
- Decision Trees: Used to classify data based on a series of rules.
- Genetic Algorithms: Inspired by biological evolution, used to solve optimization problems.

Fundamentals: Cybersecurity practices protect data from unauthorized access, use, disclosure, disruption, modification, or destruction.
Concepts:
- Authentication: Verifies user identity for access control.
- Encryption: Scrambles data to protect its confidentiality.
- Threat Analysis: Identifying and assessing potential security risks.

Big Data: Handling and analyzing massive datasets.
Cloud Computing: Provides on-demand computing services over a network.
Internet of Things (IoT): Interconnected smart devices that collect and share data.