DSA (Data Structures and Algorithms) Tutorial

Introduction to DSA

• DSA: Study of organizing data efficiently with data structures and algorithms for effective problem solving.
• Data Structures: Manage data storage and access (e.g., arrays, stacks, trees).
• Algorithms: Focus on processing the data.

Why Learn DSA?

• Enhances problem-solving abilities and programming skills.
• Foundation for various software applications (e.g., GPS, search engines).
• Essential for interviews at top tech companies (Google, Microsoft, Amazon, etc.).

How to Learn DSA?

1. Learn a programming language (C++, Java, Python, or JavaScript).
2. Understand Time and Space Complexities.
3. Study Data Structures and Algorithms.
4. Practice coding problems regularly.
5. Participate in daily challenges and contests (e.g., GfG POTD).

Core Concepts in DSA

1. Logic Building

• Develop a strong logic foundation within a programming language.
• Logic Building Guide
• Quiz

2. Time and Space Complexities

• Measure algorithm efficiency via growth order.
• Complexity Analysis Guide
• Quiz

3. Array

• Linear data structure with contiguous memory allocation.
• Array Guide
• Quiz

4. Searching Algorithms

• Locate specific data within large sets.
• Searching Guide
• Quiz

5. Sorting Algorithms

• Arrange list elements in a specific order.
• Sorting Guide
• Quiz

6. Hashing

• Generate fixed-size output from variable input sizes.
• Hashing Guide
• Quiz

7. Two Pointer Technique

• Use two indices to find required points in an array.
• Two Pointer Guide
• Quiz

8. Window Sliding Technique

• Use previous subarray results for quick computation.
• Window Sliding Guide
• Quiz

9. Prefix Sum Technique

• Use prefix sums to quickly find subarray results.
• Prefix Sum Guide
• Quiz

10. String

• Sequence of characters, often immutable.
• String Guide
• Quiz

11. Recursion

• Functions calling themselves within their definition.
• Recursion Guide
• Quiz

12. Matrix/Grid

• 2D array arranged in rows and columns.
• Matrix Guide
• Quiz

13. Stack

• Follows LIFO (Last In, First Out) principle.
• Stack Guide
• Quiz

14. Queue

• Follows FIFO (First In, First Out) principle.
• Queue Guide
• Quiz

15. Deque

• Double-ended queue allowing element operations at both ends.
• Deque Guide
• Quiz

16. Linked List

• Linear structure storing data in nodes connected by pointers.
• Linked List Guide
• Quiz

17. Tree

• Non-linear, hierarchical structure with a root and child nodes.
• Tree Guide
• Quiz

18. Heap

• Complete binary tree satisfying heap property.
• Heap Guide
• Quiz

19. Graph

• Non-linear structure with vertices and edges.
• Graph Guide
• Quiz

20. Greedy Algorithm

• Builds solutions incrementally by choosing the currently optimal solution.
• Greedy Guide
• Quiz

21. Dynamic Programming

• Solves complex problems by breaking them into subproblems and storing results.
• Dynamic Programming Guide
• Quiz

22. Other Algorithms

• Bitwise Algorithms: Operate on individual bits.
  • Guide
  • Quiz
• Backtracking Algorithm: Uses recursion to find feasible solutions.
  • Guide
  • Quiz
• Divide and Conquer: Divides problems into subproblems.
  • Guide
  • Quiz
• Branch and Bound: Systematic search for optimal solutions.
  • Guide
• Geometric Algorithms: Solve problems related to shapes and points.
  • Guide
• Randomized Algorithms: Use randomness to solve problems.
  • Guide

23. Advanced Data Structures

• Trie, Segment Tree, Red-Black Tree, Binary Indexed Tree: Advanced structures for specific problems.
• Advanced Data Structures Guide

Cheat Sheets

• Blind 75
• SDE Sheet
• GfG Master Sheet

Related Article

• Introduction to DSA