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.
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
…………JAY
JAGANNATH ……………..
Tribikram Pradhan
