Computer Science Algorithms and Operating Systems

Computer Science Study Notes

Algorithms

• Definition: A step-by-step procedure for solving a problem or performing a task.
• Types of Algorithms:
  • Sorting Algorithms: Organize data (e.g., Quick Sort, Merge Sort, Bubble Sort).
  • Search Algorithms: Find specific data within a dataset (e.g., Binary Search, Linear Search).
  • Graph Algorithms: Process graphs (e.g., Dijkstra’s Algorithm, A* Search).
  • Dynamic Programming: Solving complex problems by breaking them down into simpler subproblems.
• Algorithm Complexity:
  • Time Complexity: Measures the time an algorithm takes to complete based on input size (Big O Notation).
  • Space Complexity: Measures the amount of memory an algorithm uses in relation to input size.

Operating Systems

• Definition: Software that manages computer hardware and software resources and provides services for computer programs.
• Functions of an OS:
  • Process Management: Handling execution of processes, multitasking.
  • Memory Management: Managing primary memory (RAM) allocation, virtual memory.
  • File System Management: Controls how data is stored and retrieved.
  • Device Management: Managing device communication via drivers.
  • User Interface: Provides interfaces for users to interact with the computer (CLI, GUI).
• Common Operating Systems: Windows, macOS, Linux, Unix.

Data Structures

• Definition: A way of organizing and storing data for efficient access and modification.
• Types of Data Structures:
  • Primitive Data Structures: Basic types (e.g., integers, floats, characters).
  • Composite Data Structures: More complex types made from primitive types (e.g., Arrays, Structs).
  • Linear Data Structures: Data is arranged sequentially (e.g., Lists, Stacks, Queues).
  • Non-linear Data Structures: Data is not in a sequential order (e.g., Trees, Graphs).
• Common Operations:
  • Insertion: Adding a data element.
  • Deletion: Removing a data element.
  • Traversal: Accessing each element of the data structure.
  • Searching: Locating an element within the structure.

Algorithms

• Algorithms are a set of well-defined instructions for solving a problem or completing a task.
• Algorithms are classified by the problem they solve. Popular algorithm types include:
  • Sorting Algorithms: Order data in a specific sequence (e.g., Quick Sort, Merge Sort, Bubble Sort).
  • Search Algorithms: Locate specific data within a set (e.g., Binary Search, Linear Search).
  • Graph Algorithms: Process and analyze complex networks or relationships (e.g., Dijkstra’s Algorithm, A* Search).
  • Dynamic Programming: Break down complex problems into smaller, overlapping subproblems that can be solved independently and combined for the final solution.
• Algorithm efficiency is measured in terms of:
  • Time Complexity: Evaluates the total time an algorithm takes to complete based on input size. Big O notation is used to represent this complexity.
  • Space Complexity: Measures the maximum memory that an algorithm uses in relation to the size of the input.

Operating Systems

• Operating Systems (OS) are essential software that manage and control computer hardware and software resources.
• Key functions of an OS include:
  • Process Management: Handles the execution and scheduling of different processes (programs) running simultaneously, enabling multitasking.
  • Memory Management: Allocates and manages the primary memory (RAM) efficiently, including virtual memory, to ensure optimal resource utilization.
  • File System Management: Controls how data is stored, organized, and retrieved, making it accessible to users and applications.
  • Device Management: Manages the communication between the computer and peripheral devices.
  • User Interface: Provides a way for users to interact with the computer, offering options like graphical (GUI) or command-line (CLI) interfaces.
• Popular operating systems include: Windows, macOS, Linux, and Unix.

Data Structures

• Data Structures provide organized ways to store and manage data, making it easier to access and modify.
• Data Structure types can be categorized as:
  • Primitive Data Structures: Basic data types like integers, floating-point numbers, and characters.
  • Composite Data Structures: Complex data structures formed from combinations of primitive types (e.g., arrays, structs).
  • Linear Data Structures: Data is arranged in a linear sequence (e.g., lists, stacks, queues).
  • Non-linear Data Structures: Data is not organized in a sequential order (e.g., trees, graphs).
• Common operations performed on data structures include:
  • Insertion: Adding a new data element to the structure.
  • Deletion: Removing an existing data element from the structure.
  • Traversal: Accessing and processing each element within the data structure.
  • Searching: Locating a specific element within the structure.

This quiz covers essential concepts in algorithms and operating systems, including types of algorithms such as sorting and searching, and the functions of an operating system. Test your knowledge on algorithm complexity and the critical roles that operating systems play in managing computer resources.