The data structure is a particular way of storing and organizing data so that it can be used efficiently. Arrays, trees, linked lists, stacks, graphs, etc. are all data structures and each of them allows us to perform different operations on data. No matter which programming language we use, it is important to learn algorithm design techniques in data structures in order to be able to build scalable systems. Selecting a proper design technique for algorithms is a complex but important task.

Algorithm Design Techniques – Data Structure and Algorithms

In this “Algorithm Design Techniques – Data Structure and Algorithms” you will learn about the following topics:

  1. Introduction to Algorithm Design Techniques
  2. Brute-Force Or Exhaustive Search
  3. What Is The Use Of A Brute Force Algorithm?, Performance of Brute Force Algorithms, How to Make a Brute Force Algorithm?
  4. Divide and Conquer
  5. Fundamental of Divide & Conquer Strategy, Applications of Divide and Conquer Approach, Advantages of Divide and Conquer, Disadvantages of Divide and Conquer
  6. Greedy Algorithms
  7. When to use Greedy Algorithms? Standard Greedy Algorithm, Advantages of Greedy Approach/Technique, Disadvantages of Greedy Approach/Technique
  8. Dynamic Programming
  9. Characteristics of Dynamic Programming, Elements of Dynamic Programming, Components of Dynamic Programming, Development of Dynamic Programming Algorithm, Applications of Dynamic Programming
  10. Branch and Bounce Algorithms
  11. When Branch and Bound Is a Good Choice? Branch and Bound Algorithm Pseudocode, Advantages of Branch and Bound Algorithm, Disadvantages of Branch and Bound Algorithm
  12. Randomized Algorithms
  13. When Is Randomness Useful? How To Analyze Randomized Algorithms?
  14. Backtracking Algorithm
  15. Example of Backtracking Algorithm, Algorithm of Backtracking




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BCA 3rd Semester Data Structure and Algorithms (DSA) Notes Pdf: