Overview of Computer Science

Definition: Study of computation, information processing, and associated systems.
Branches:
- Theoretical Computer Science
- Software Engineering
- Systems Architecture
- Artificial Intelligence
- Human-Computer Interaction
- Networking and Communications
- Data Science

Algorithms: Step-by-step procedures for calculations and data processing.
Data Structures: Ways to organize and store data (e.g., arrays, linked lists, trees, graphs).
Programming Languages: Tools for writing software (e.g., Python, Java, C++).
Computational Complexity: Study of resource requirements of algorithms (time complexity, space complexity).

Lifecycle: Stages include requirements, design, implementation, testing, deployment, maintenance.
Methodologies:
- Agile
- Waterfall
- DevOps
Version Control: Systems like Git for managing changes to code.

Functions: Manage hardware, software resources; provide common services for programs.
Types:
- Batch Operating Systems
- Time-Sharing Operating Systems
- Distributed Operating Systems
- Real-Time Operating Systems

Types: Relational (SQL) and Non-relational (NoSQL).
Key Concepts:
- Normalization
- Transactions
- ACID properties (Atomicity, Consistency, Isolation, Durability)

Definition: Simulation of human intelligence in machines.
Subfields:
- Machine Learning
- Natural Language Processing
- Computer Vision
Techniques: Neural networks, decision trees, reinforcement learning.

Purpose: Protect systems, networks, and data from digital attacks.
Key Concepts:
- Encryption
- Firewalls
- Intrusion Detection Systems
- Security Protocols

Quantum Computing: Explores computation using quantum-mechanical phenomena.
Big Data: Management and analysis of large datasets.
Cloud Computing: Delivery of computing services over the internet.
Internet of Things (IoT): Interconnection of everyday objects to the internet.

Algorithms: Structured methods for solving problems and processing data.
Data Structures: Organized formats for data storage, including:
- Arrays
- Linked Lists
- Trees
- Graphs
Programming Languages: Tools used for software development, such as:
- Python
- Java
- C++
Computational Complexity: Evaluates the efficiency of algorithms based on time and space usage.

Lifecycle stages encompass:
- Requirements gathering
- Design specifications
- Implementation of code
- Testing for quality assurance
- Deployment to production
- Maintenance post-launch
Methodologies for managing software projects:
- Agile: Iterative development and flexibility.
- Waterfall: Sequential phase-based approach.
- DevOps: Collaboration between development and operations teams.
Version Control: Systems like Git help track changes and manage code revisions.

Primary functions include managing hardware and software resources and facilitating program services.
Types of operating systems:
- Batch Operating Systems: Execute tasks in batches without user interaction.
- Time-Sharing Operating Systems: Allow multiple users to share system resources simultaneously.
- Distributed Operating Systems: Manage a group of independent computers as a single system.
- Real-Time Operating Systems: Provide immediate processing and response for critical tasks.

Networking connects computers to enable resource and information sharing.
Communication Protocols govern data exchange, including:
- TCP/IP: Foundation of internet communication.
- HTTP: Hypertext transfer protocol for web transactions.
- FTP: File transfer protocol for file sharing.
Architectural models include:
- Client-Server Model: Hierarchical structure where clients request resources from servers.
- Peer-to-Peer Networks: Direct sharing between users without a central server.

Two main database types:
- Relational Databases (SQL): Use structured query language for data management.
- Non-Relational Databases (NoSQL): Handle unstructured and semi-structured data.
Essential concepts in databases:
- Normalization: Process of organizing data to reduce redundancy.
- Transactions: Series of operations treated as a single logical unit.
- ACID properties: Key principles ensuring reliable transaction processing:
  - Atomicity: All operations in a transaction succeed or fail together.
  - Consistency: Data remains valid and consistent after a transaction.
  - Isolation: Concurrent transactions do not interfere with each other.
  - Durability: Completed transactions persist despite system failures.

Simulation of human cognitive functions in machines.
Subfields include:
- Machine Learning: Algorithms that allow computers to learn from data.
- Natural Language Processing: Enabling machines to understand and interact using human language.
- Computer Vision: Teaching machines to interpret and process visual data.
Techniques utilized:
- Neural Networks: Models inspired by the human brain for pattern recognition.
- Decision Trees: Flowchart-like structures for decision-making analysis.
- Reinforcement Learning: Algorithms that learn optimal behaviors through trial and error.

Vital for protecting systems, networks, and sensitive data from threats.
Key concepts in cybersecurity encompass:
- Encryption: Securing data by transforming it into a coded format.
- Firewalls: Systems that monitor and control incoming and outgoing network traffic.
- Intrusion Detection Systems: Tools that detect unauthorized access attempts.
- Security Protocols: Standards governing secure communication and data integrity.

Quantum Computing: Explores new computing paradigms leveraging quantum mechanics.
Big Data: Focuses on methods for managing and analyzing vast datasets.
Cloud Computing: Offers scalable computing services and resources delivered via the internet.
Internet of Things (IoT): Connects everyday physical objects to the internet for enhanced data exchange and control.