■ COMPUTER SCIENCE
MASTER STUDY GUIDE
Complete Notes for Data Structures, Algorithms, OOP, OS & AI/ML
✦ DATA STRUCTURES & ALGORITHMS ✦ OBJECT-ORIENTED PROGRAMMING
✦ OPERATING SYSTEMS ✦ ARTIFICIAL INTELLIGENCE & ML
✦ SYSTEMS DESIGN ✦ EXAM PREP & CHEAT SHEETS
2026 Edition | University-Level Comprehensive Notes | Exam-Ready Format
FEATURES INCLUDED
✓ Visual diagrams & flowcharts for every concept
✓ Time & space complexity analysis (Big-O)
✓ Python & pseudocode examples
✓ Common exam questions with model answers
✓ Cheat sheets & quick reference tables
✓ Real-world application examples
,■ TABLE OF CONTENTS
1. Data Structures & Algorithms 3
1.1 Arrays & Linked Lists 3
1.2 Stacks & Queues 4
1.3 Trees & Binary Search Trees 5
1.4 Heaps & Priority Queues 6
1.5 Hash Tables 7
1.6 Graphs & Graph Algorithms 8
1.7 Sorting Algorithms 9
1.8 Searching Algorithms 10
2. Object-Oriented Programming 11
2.1 OOP Principles (SOLID) 11
2.2 Design Patterns 12
2.3 UML Diagrams 13
3. Operating Systems 14
3.1 Process Management 14
3.2 Memory Management 15
3.3 File Systems & I/O 16
4. Artificial Intelligence & Machine Learning 17
4.1 AI Fundamentals 17
4.2 Machine Learning Algorithms 18
4.3 Deep Learning & Neural Networks 19
5. Systems Design & Architecture 20
6. Exam Cheat Sheets & Quick Reference 21
, 1. DATA STRUCTURES & ALGORITHMS
The foundation of computer science. Master these, and you master 80% of technical
interviews.
1.1 Arrays & Linked Lists
Arrays are contiguous memory blocks with O(1) random access. Linked Lists use nodes with pointers, enabling
O(1) insertion/deletion at known positions.
KEY CONCEPT — Array vs Linked List:
Feature Array Linked List
Access O(1) — random access O(n) — sequential access
Insertion (front) O(n) — shift elements O(1) — change head pointer
Deletion (front) O(n) O(1)
Search O(n) unsorted, O(log n) sorted O(n)
Memory Contiguous, fixed size Dynamic, scattered
Cache Cache-friendly Cache-unfriendly
Python Implementation — Linked List:
class Node: def __init__(self, data): self.data = data self.next = None class LinkedList:
def __init__(self): self.head = None def append(self, data): new_node = Node(data) if not
self.head: self.head = new_node return current = self.head while current.next: current =
current.next current.next = new_node def prepend(self, data): # O(1) — key advantage!
new_node = Node(data) new_node.next = self.head self.head = new_node
■ EXAM TIP: Always mention cache locality when comparing arrays vs linked lists. Arrays are
cache-friendly because elements are contiguous; linked lists cause cache misses due to pointer chasing.
1.2 Stacks & Queues
Stack = LIFO (Last In, First Out). Think: browser back button, undo operations, function call stack. Queue =
FIFO (First In, First Out). Think: print queue, BFS, task scheduling.
KEY CONCEPT — Stack Operations:
• push(x): Add element to top — O(1) • pop(): Remove top element — O(1) • peek(): View top without removing
— O(1) • isEmpty(): Check if empty — O(1) All operations are O(1) because we only access the top.
MASTER STUDY GUIDE
Complete Notes for Data Structures, Algorithms, OOP, OS & AI/ML
✦ DATA STRUCTURES & ALGORITHMS ✦ OBJECT-ORIENTED PROGRAMMING
✦ OPERATING SYSTEMS ✦ ARTIFICIAL INTELLIGENCE & ML
✦ SYSTEMS DESIGN ✦ EXAM PREP & CHEAT SHEETS
2026 Edition | University-Level Comprehensive Notes | Exam-Ready Format
FEATURES INCLUDED
✓ Visual diagrams & flowcharts for every concept
✓ Time & space complexity analysis (Big-O)
✓ Python & pseudocode examples
✓ Common exam questions with model answers
✓ Cheat sheets & quick reference tables
✓ Real-world application examples
,■ TABLE OF CONTENTS
1. Data Structures & Algorithms 3
1.1 Arrays & Linked Lists 3
1.2 Stacks & Queues 4
1.3 Trees & Binary Search Trees 5
1.4 Heaps & Priority Queues 6
1.5 Hash Tables 7
1.6 Graphs & Graph Algorithms 8
1.7 Sorting Algorithms 9
1.8 Searching Algorithms 10
2. Object-Oriented Programming 11
2.1 OOP Principles (SOLID) 11
2.2 Design Patterns 12
2.3 UML Diagrams 13
3. Operating Systems 14
3.1 Process Management 14
3.2 Memory Management 15
3.3 File Systems & I/O 16
4. Artificial Intelligence & Machine Learning 17
4.1 AI Fundamentals 17
4.2 Machine Learning Algorithms 18
4.3 Deep Learning & Neural Networks 19
5. Systems Design & Architecture 20
6. Exam Cheat Sheets & Quick Reference 21
, 1. DATA STRUCTURES & ALGORITHMS
The foundation of computer science. Master these, and you master 80% of technical
interviews.
1.1 Arrays & Linked Lists
Arrays are contiguous memory blocks with O(1) random access. Linked Lists use nodes with pointers, enabling
O(1) insertion/deletion at known positions.
KEY CONCEPT — Array vs Linked List:
Feature Array Linked List
Access O(1) — random access O(n) — sequential access
Insertion (front) O(n) — shift elements O(1) — change head pointer
Deletion (front) O(n) O(1)
Search O(n) unsorted, O(log n) sorted O(n)
Memory Contiguous, fixed size Dynamic, scattered
Cache Cache-friendly Cache-unfriendly
Python Implementation — Linked List:
class Node: def __init__(self, data): self.data = data self.next = None class LinkedList:
def __init__(self): self.head = None def append(self, data): new_node = Node(data) if not
self.head: self.head = new_node return current = self.head while current.next: current =
current.next current.next = new_node def prepend(self, data): # O(1) — key advantage!
new_node = Node(data) new_node.next = self.head self.head = new_node
■ EXAM TIP: Always mention cache locality when comparing arrays vs linked lists. Arrays are
cache-friendly because elements are contiguous; linked lists cause cache misses due to pointer chasing.
1.2 Stacks & Queues
Stack = LIFO (Last In, First Out). Think: browser back button, undo operations, function call stack. Queue =
FIFO (First In, First Out). Think: print queue, BFS, task scheduling.
KEY CONCEPT — Stack Operations:
• push(x): Add element to top — O(1) • pop(): Remove top element — O(1) • peek(): View top without removing
— O(1) • isEmpty(): Check if empty — O(1) All operations are O(1) because we only access the top.
