Why Data Structures and Algorithms Are Important to Learn?

If you’re preparing for a tech interview of any big tech company like Adobe, Amazon, Microsoft, Google, etc. – most probably, you would have known about the importance of Data Structures and Algorithms to crack these interviews. Yes, most of the interviews for technical roles in these companies are focused on measuring the Data Structures and Algorithms knowledge of the candidates.  

 In addition to that, there are multiple reasons why Product Based Companies place so much emphasis on Data Structures and Algorithms as stated below: 

 1. Data Structures and Algorithms are used to test the analytical skills of the candidates as they are a useful tool to pick out the underlying algorithms in real-world problems and solve them efficiently. 

2.  Data Structures and Algorithms are the fundamentals of Software Development.

3.  Data Structures and Algorithms demonstrate the problem-solving ability of a candidate. There is no room to craft elaborate stories and this means that either the candidate can solve the problem or they can’t.

4. Questions based on Data Structures and Algorithms can be scaled up or down according to the knowledge level of the candidate. This means that a variety of candidates can be tested using roughly the same problems.

DATA STRUCTURES SYLLABUS

1)                  Introduction to data structures, classification of data structure, description of     various                      types of data structure, Data structure operation, Abstraction Data type(ADT)

2)                  Trees: Introduction, Tree, Tree Terminology

3)                  Binary Tree: strictly Binary tree, Level of a node in Binary tree, Complete Binary Tree,                         Almost  Complete Binary Tree

4)                  Memory Representation of a Binary Tree : Arrays and Linked Representation

5)                  Traversing a Binary Tree: Pre Order, In order, Postorder Traversal, Characteristics of                         Binary  Tree

6)                  Binary Search Tree- Insertion, Deletion, Efficiency of Binary Search Tree operations

7)                  Heap Trees [Priority Queues]: Insertion, Deletion

8)                  Height Balance: AVL Tree, Insertion, Deletion

9)                  Multiway Search Tree, B-tree Insertion and Deletion

10)              B+- Tree Insertion and Deletion

11)              Splay Tree, Red-Black Tree, Expression Tree, Threaded Binary Tree

12)              Graphs: Introduction, Graph Terminology, Representation of Graph 

                    [Matrix, Linked, Array]

13)               Shortest Path Algorithm: Warshal, Dijkstra's algorithm

14)              Minimum Spanning Tree: kruskals , prims and sollins

15)              Graph Traversal: BFS, DFS, Topological Sorting

16)              Searching and Sorting: Introduction, Types of search (Linear, Binary)

17)              Sorting: Bubble, Insertion, Selection, Merge, Quick, Radix, Heap, Bucket, Shell, 2-way,                         Merge and Polyphase merge sort

18)              Hashing: Introduction, Hash Functions, Collision Resolution Techniques: Separate Chain

19)              Open Addressing-Linear  Probing, Quadratic Probing, Double Hashing

20)              Arrays and Strings

21)              Linked List: Double Linked List, Circular  Linked List , Arrays Linked List

22)              Sparse Matrix and Polynomials

23)              Stack and Queues

 

 Conclusion – If you are willing to do a job in a product-based company, then data structure and algorithms are mandatory for you. As we know product-based companies develop software for their customers. And day by day they used to change and add features to their products. For implementing those features, they required software engineers with good data structure and algorithm skills. Moreover, they offer a good package to software engineers.

 

 

 

 

 

 

…………JAY JAGANNATH ……………..

 

Tribikram Pradhan