From Basics to Advanced: Deep Dive into Data Structures

From Basics to Advanced: Deep Dive into Data Structures

 

Before Going into the subject lets learn what is data structures,what are its applications,why we will use it and some basics of it

Data Structures:

used to organize, manage, and store data efficiently, enabling easy access and modification. They provide a systematic way to handle data and are essential for designing robust algorithms. data structures include arrays,  linked lists, stacks and queues, and trees, which represent hierarchical relationships and graphs, which model pairwise relationships between objects. Additionally, hash tables allow fast data retrieval using key-value pairs. Understanding and implementing appropriate data structures is crucial for optimizing performance and resource utilization in software applications.

Applications:

1. Database Management Systems
2. Operating Systems
3. Data Processing
4. Machine Learning
5. Artificial Intelligence

 

Why Data Structures?

it help us organize and manage data efficiently. They allow us to store information in a structured way that makes it easy to access, modify, and retrieve when needed.

Basics Of Data Structures

Topics Covered

1. Array
2. Stack
3. Queue
4. LinkedList
5. Tree
6. Graph

 

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Types Of Data Structures

In these pdf we will discuss some little more about Basics

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Overview Of Data Structures

This Pdf gives about overview it can serve as a foundational guide for anyone beginning their journey in learning Data Structures and Algorithms, providing both theoretical knowledge and practical problem-solving techniques.

Topics Covered

1. What is Data Structure?

• Definition and importance of data structures in efficient data management.
• Focus on minimizing time and space complexities.

2. What is Algorithm?

• Definition and explanation of algorithms.
• Steps to solve problems or perform calculations systematically.

3.How to Start Learning DSA?

• Breakdown of the learning process into four parts:
• Time and Space complexities.
• Basics of individual Data Structures.
• Basics of Algorithms.
• Practice Problems on DSA.

4.Learn Algorithms?

Searching Algorithms: Linear Search, Binary Search, Jump Search, Interpolation Search, Exponential Search.

• Sorting Algorithms: Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Merge Sort.
  Divide and Conquer Algorithms: Breaking problems into subproblems, solving them recursively, and combining solutions.
• Greedy Algorithms: Building solutions piece-by-piece for immediate benefits. Examples include Fractional Knapsack Problem.
  Recursion: Concept of repeated usage of code. Examples include Tree traversals, Graph traversals, Backtracking algorithms.
  Backtracking Algorithms: Trying all possible solutions and finding the correct one. Examples include Knight’s tour problem, N-Queen problem, Sudoku.
  Dynamic Programming: Optimization of recursive solutions by avoiding repeated calculations. Concepts include Tabulation vs. Memoization, Optimal Substructure, Overlapping Subproblems.
  Pattern Searching: Searching strings within another string.
  Mathematical Algorithms: Solving Mathematical and Number Theory problems. Topics include GCD, LCM, Prime Factorization, Fibonacci Numbers, Modular Arithmetic, Euler Totient Function.
  Geometric Algorithms: Solving geometric problems. Topics include lines, triangles, rectangles, circles, 3D objects, polygons, convex hulls.

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HandWrittenNotes

Complete Handwritten Notes is given below

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100_DSA_INTERVIEW_QUESTIONS

Top 100 Questions asked in Top MNC Companies

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DSA_SHORT_NOTES

This Pdf will provide a short revision on Data Structures

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