Computer Science 101 Lecture Notes

Introduction

• Overview of common coding errors and misconceptions
• Importance of understanding underlying concepts in computer science

Basic Concepts

• What is a Computer?

  • A device that processes data using binary (ones and zeros)
  • Conceptually similar to a Turing machine

• Central Processing Unit (CPU)

  • Core component of modern computers
  • Contains billions of tiny transistors (on/off switches)
  • A bit is the smallest piece of information; a byte (8 bits) represents 256 values

Data Representation

• Binary System

  • A counting system using only 1 and 0
  • Often simplified for humans using hexadecimal (base 16)

• Character Encoding

  • Maps keyboard characters to binary values (e.g., ASCII, UTF-8)

• Machine Code

  • Code that the CPU can decode and execute
  • Applications use Random Access Memory (RAM) to store data temporarily

Input/Output Devices

• Input: Keyboard, Mouse
• Output: Monitor
• Operating System (OS) manages hardware through device drivers
  • Examples: Linux, Mac, Windows

Shell and Programming Languages

• Shell

  • Program that interacts with the OS via a command line interface
  • Can connect to remote computers using secure shell protocol

• Programming Languages

  • Tools for writing code with different abstraction levels
  • Interpreted (e.g., Python) vs. Compiled (e.g., C++)

Data Types and Variables

• Common built-in data types:

  • Integers (int): Whole numbers (signed/unsigned)
  • Floating point numbers (float/double): Numbers with decimals
  • Characters (char) and Strings: Text data

• Variables

  • Names attached to data points for reuse
  • Dynamic typing (e.g., Python) vs. Static typing (e.g., C)

Memory Management

• Pointers: Variables that hold memory addresses of other variables
• Garbage Collection: Automatic memory management system in some languages

Data Structures

• Arrays/Lists: Ordered collections of items
• Linked Lists: Items connected by pointers
• Stacks and Queues: LIFO and FIFO data structures respectively
• Hash Tables: Key-value pair collections
• Trees and Graphs: Hierarchical and networked data structures

Algorithms

• Defined as blocks of code that solve problems
• Functions: Named blocks of code that take inputs and return outputs
• Conditional logic (if statements) and loops (while/for)
• Recursion: Functions calling themselves with base conditions to avoid infinite loops

Algorithm Efficiency

• Big-O Notation: Measures time and space complexity of algorithms
• Various types of algorithms:
  • Brute Force: Examining all possible solutions
  • Divide and Conquer: Breaking problems into smaller parts
  • Dynamic Programming: Storing results of solved sub-problems
  • Greedy Algorithms: Making the best immediate choice
  • Backtracking: Incremental approach to find solutions

Programming Paradigms

• Declarative vs. Imperative: Describes what the program does vs. explicit instructions
• Object-Oriented Programming (OOP): Classes encapsulating data and behavior
  • Inheritance: Subclass extending behaviors of a parent class

Memory Management in Depth

• Heap vs. Stack: Dynamic vs. static memory allocation
• Object references to avoid memory duplication

Multithreading and Concurrency

• Threads: Allow concurrent execution of code
• Concurrency Models: Event loops, co-routines for handling multiple jobs

Cloud Computing

• Virtual Machines: Simulating hardware to run multiple smaller computers
• Networking: IP addresses, TCP handshake, and APIs for client-server communication

Conclusion

• The importance of understanding how all components work together in computer science
• Practical implications, such as troubleshooting printers at home.