Lecture Objective:
By the end of this lecture, students will:
• Understand the definition and scope of Computer Science
• Trace the historical evolution of computing
• Differentiate between CS, IT, and Software Engineering
• Understand how computer science solves real-world problems
• Recognize the structure and approach of scientific inquiry within CS
Section 1: What is Computer Science?
Computer Science is the study of computation. It focuses on both theoretical foundations and
practical techniques for implementing and applying those foundations to solve problems. It's
about understanding what computers can do, what they cannot do, and how we can make them
do what we want efficiently.
Core Aspects:
• Algorithms: Step-by-step instructions to solve problems
• Data structures: Ways of organizing and storing data
• Programming: Writing instructions for a computer to follow
• Hardware systems: Understanding the physical machinery
• Software systems: Operating systems, databases, compilers
• Artificial Intelligence: Mimicking cognitive functions
• Theory of Computation: Mathematical aspects like automata, languages
CS is not just programming. Programming is a tool in CS, but the core goal is problem-solving
using computational thinking.
Section 2: A Brief History of Computer Science
Pre-Computer Era:
• Abacus (~2400 BCE): First known calculation tool
• Algorithm by Al-Khwarizmi (9th century): Origins of the word "algorithm"
• Charles Babbage (1837): Designed the first mechanical computer - Analytical Engine
• Ada Lovelace: The first computer programmer
Theoretical Foundations:
• Alan Turing (1936): Concept of a theoretical machine (Turing Machine), foundation of
modern computation
• Claude Shannon (1937): Introduced binary logic in electrical circuits
Electronic Computers:
• ENIAC (1945): First general-purpose digital computer
• Transistors (1947): Replaced vacuum tubes, smaller and more reliable
• Microprocessors (1971): Complete CPU on a chip
• Internet Era (1990s): Global connectivity revolution
• AI & Quantum Computing (2010s-present): Redefining computational limits
Section 3: Scope and Branches of Computer Science
1. Theoretical CS
o Automata theory
o Computability
o Complexity theory
2. Algorithms & Data Structures
o Graphs, trees, hashing, sorting algorithms
3. Programming Languages
o Syntax, semantics, compilers, interpreters
4. Software Engineering
o Software lifecycle, testing, project management
5. Artificial Intelligence & Machine Learning
o Neural networks, deep learning, NLP, robotics
6. Computer Graphics & Vision
o Rendering, 3D modeling, computer vision
7. Human-Computer Interaction
o UI/UX design, accessibility
8. Operating Systems
o Process management, memory, file systems
9. Networks and Communications
o Internet protocols, wireless communication
10. Cybersecurity
o Encryption, firewalls, ethical hacking
11. Databases
o SQL, NoSQL, data warehousing
12. Cloud Computing & DevOps
o Virtualization, scalability, CI/CD
Section 4: CS vs IT vs Software Engineering
Field Computer Science Information Technology (IT) Software Engineering
Algorithms, theory, Application of tech in business Building maintainable
Focus
computation & management software systems
Programming, logic, Network management, tech Design patterns,
Skills
problem solving support architecture, SDLC
Academic and Software for real-world
Outcome Systems and tools deployment
innovation-focused use
Section 5: The Scientific Method in CS
Just like in other sciences, CS uses the scientific method:
1. Observe a phenomenon or need
2. Hypothesize a computational solution (e.g., an algorithm)
3. Experiment through code implementation
4. Evaluate results and efficiency
5. Repeat to refine
Example:
• Problem: Sorting names alphabetically
• Hypothesis: Bubble sort will work
• Experiment: Implement bubble sort in Python
• Evaluate: Measure time complexity O(n^2)
• Improve: Switch to merge sort O(n log n)
Section 6: Real-World Applications of CS
Field Application
Medicine AI for diagnostics, surgical robots
Finance Fraud detection, algorithmic trading
Education Adaptive learning platforms
Gaming Real-time physics, AI opponents
Space NASA simulations and robotics
Environment Climate modeling, sustainability monitoring
