Data Structures is a concept a means of
storing a collection of data. Computer Science is a concern with study of
methods for effectively using a computer to solve problems. These can be solve
by algorithms and data structures. Data Structures tells you what way the data
as to store in computer memory and how to access the data efficiently. Many
Applications are designed by data structures stack applications like page
visited history in a web-browser, chain of method calls in the Java virtual
machine or C++ Run-time environment etc Queue Application Like Waiting Lines,
Multi-programming etc For many applications the choice of proper data structure
is the only major decision involving the implementation. Majorly the database
designing and internal implementation is done only by using Data Structures
techniques.
Data
Structure Training Course Objective
This Course main objective for the
student to understand Analysis and Designing of the Algorithms and how the
different data structures are used for efficient accessing of the data and
Manipulation of the data at the end of the session we can able to Know
different Kinds of data structures and we can able to provide different
algorithms for time and space complexity.
Introduction
to Data Structure
Algorithms
Performance
Analysis
- Time
complexity
- Space
complexity
Asymptotic
Notations-
- Big
O
- Omega
- Theta
notations
Arrays
Structures
Pointers
Dynamic
Memory allocation
- Malloc()
- calloc()
- realloc()
- free()
Stacks
- Stack
Operations
·
push()
·
pop()
·
peex()
·
distzay()
·
isEmpty()
·
isFull()
Stack implementation using arrays
Applications
·
Decimal to Binary
·
String reverse
·
Number reverse
·
Recursion – Towers of Hanoi
·
Balanced Parentheses
·
Expressions
Stack
Implementation using pointer (dynamic)
Expression
- Introduction
to Notations
- Importance
of Notations in expression evaluation
- Conversion
Algorithms
·
Infix to prefix
·
Infix to postfix
·
Prefix to infix
·
Prefix to postfix
·
Postfix to infix
·
Postfix to prefix
Implementation of all the conversions
Queues
- Operations
on Queue – enqueue(), dequeue()
- Queue
implementation using static arrays
- Applications
- Queues
Implementations using pointer (dynamic)
Circular
queues
Double
Ended queue (Deques)
Single
linked list
- Introduction
- Construction
- Length
- Insertion
- Deletion
- Sort
- Reverse
list
- Swap
node data
- Swap
nodes
- Applications
Stack
implementation using linked list
Queue
implementation using linked list
Doubly
linked list
Circular
linked list
Circular
Doubly Linked List
Binary
Tree
- Terminology
- Differences
between Tree and Binary Tree
- Binary
Tree Representations
- Expression
Trees
- Traversals
·
In-order
·
pre-order
·
post-order
Binary
Search Tree
- Introduction
to BST
- Insertion
- Deletion
- Search
- Implementation
Graph
- Introduction
& Terminology
- Graph
Representations
- Traversal
·
BFS (Breadth First Search)
·
DFS (Depth First Search)
Searching
Algorithms
- Linear
search
- Binary
search
Sorting
Algorithms
- Bubble
sort
- Selection
sort
- Insertion
sort
- Heap
sort
- Merge
sort
- Quick
sort
AVL Trees
- Introduction
- BST
v/s AVL
- Rotations
·
L-L-Rotation
·
R-R-Rotation
·
L-R-Rotation
·
R-L-Rotation
Insertion
Deletion
Traversal
Red Black Trees
- Introduction
- BST
v/s AVL v/s RBT
- Rotations
·
L-L-Rotation
·
R-R-Rotation
·
L-R-Rotation
·
R-L-Rotation
Insertion
Deletion
B trees
- M-way
Search Tree
- Search
- Insertion
- Deletion
Hashing
- Hash
Table representation
- Hash
function-Division Method
- Collision
- Collision
Resolution Techniques
·
Separate Chaining
·
open addressing
·
linear probing
·
quadratic probing
·
double hashing
·
Rehashing
Priority Queue-Definition
- Operations-Insertion,
Deletion,
Heap
- Definition
- Max
Heap
- Min
Heap
- Insertion
and deletion
Pattern matching algorithms
- Brute
force
- Boyer
–Moore algorithm
- Knuth-Morris-Pratt
algorithm
Tries
- Standard
Tries
- Compressed
Tries
- Suffix
tries
Dynamic Programming
Greedy Method
Divide and conquer method
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