Data Structures And Algorithms (DSA)

Data Structures and Algorithms (DSA) are fundamental to computer science, providing a framework for organizing and manipulating data and devising strategies for computational tasks. Proficiency in DSA enables the design of efficient algorithms, analysis of their time and space complexity, and implementation of data structures to enhance performance.

Mastering concepts like arrays, linked lists, trees, graphs, and sorting algorithms is crucial for developing effective software applications and systems. DSA skills are especially valued by FAANG companies (Facebook, Amazon, Apple, Netflix, Google) for their technical interviews, where problem-solving and algorithmic thinking are key evaluation criteria.

Learning DSA not only enhances problem-solving abilities but also opens career opportunities in software development, data engineering, and computer science research.

Syllabus:

• 1) Introduction to Data Structures
• Arrays and Strings
• Linked Lists
• Stacks and Queues
• Trees and Binary Search Trees (BST)
• Graphs and Graph Algorithms
• 2) Analysis of Algorithms
• Time and Space Complexity
• Asymptotic Notations: Big O, Big Omega, Big Theta
• Algorithm Design Techniques: Divide and Conquer, Greedy, Dynamic Programming
• Recursion and Backtracking
• 3) Sorting and Searching Algorithms
• Bubble Sort, Selection Sort, Insertion Sort
• Merge Sort, Quick Sort, Heap Sort
• Binary Search
• Linear Search
• 4) Advanced Data Structures
• Hash Tables and Hashing Techniques
• Priority Queues and Heaps
• Trie Data Structure
• Segment Trees and Fenwick Trees
• 5) Dynamic Programming
• Optimal Substructure and Overlapping Subproblems
• Memoization and Tabulation
• Examples of Dynamic Programming Problems
• Advanced Dynamic Programming Techniques