19, అక్టోబర్ 2022, బుధవారం
Data Structure and Types
data structure and its types.
What
are Data Structures?
Data structure is a
storage that is used to store and organize data. It is a way of arranging data
on a computer so that it can be accessed and updated efficiently.
Depending on your
requirement and project, it is important to choose the right data structure for
your project. For example, if you want to store data sequentially in the
memory, then you can go for the Array data structure.
Array data Structure Representation
Note: Data structure and
data types are slightly different. Data structure is the collection of data
types arranged in a specific order.
Types
of Data Structure
Basically, data
structures are divided into two categories:
·
Linear data structure
·
Non-linear data
structure
Let's learn about each
type in detail.
Linear
data structures
In linear data
structures, the elements are arranged in sequence one after the other. Since
elements are arranged in particular order, they are easy to implement.
However, when the
complexity of the program increases, the linear data structures might not be
the best choice because of operational complexities.
Popular linear data
structures are:
1.
Array Data Structure
In an array, elements
in memory are arranged in continuous memory. All the elements of an array are
of the same type. And, the type of elements that can be stored in the form of
arrays is determined by the programming language.
To learn more,
visit Java Array.
An array with each element represented by an index
2.
Stack Data Structure
In stack data
structure, elements are stored in the LIFO principle. That is, the last element
stored in a stack will be removed first.
It works just like a
pile of plates where the last plate kept on the pile will be removed first. To
learn more, visit Stack Data Structure.
In
a stack, operations can be perform only from one end (top here).
3.
Queue Data Structure
Unlike stack, the
queue data structure works in the FIFO principle where first element stored in
the queue will be removed first.
It works just like a
queue of people in the ticket counter where first person on the queue will get
the ticket first. To learn more, visit Queue Data Structure.
In
a queue, addition and removal are performed from separate ends.
4.
Linked List Data Structure
In linked list data
structure, data elements are connected through a series of nodes. And, each
node contains the data items and address to the next node.
To learn more,
visit Linked List Data
Structure.
A linked list
Non
linear data structures
Unlike linear data
structures, elements in non-linear data structures are not in any sequence.
Instead they are arranged in a hierarchical manner where one element will be
connected to one or more elements.
Non-linear data
structures are further divided into graph and tree based data structures.
1.
Graph Data Structure
In graph data
structure, each node is called vertex and each vertex is connected to other
vertices through edges.
To learn more,
visit Graph Data Structure.
Graph data structure example
Popular Graph Based
Data Structures:
·
Spanning Tree and
Minimum Spanning Tree
·
Strongly Connected
Components
2.
Trees Data Structure
Similar to a graph, a
tree is also a collection of vertices and edges. However, in tree data
structure, there can only be one edge between two vertices.
To learn more,
visit Tree Data Structure.
Tree data structure example
Popular Tree based Data
Structure
·
AVL Tree
·
B-Tree
·
B+ Tree
Linear
Vs Non-linear Data Structures
Now that we know about
linear and non-linear data structures, let's see the major differences between
them.
|
Linear Data Structures |
Non Linear Data Structures |
|
The data items are arranged in sequential order, one after
the other. |
The data items are arranged in non-sequential order
(hierarchical manner). |
|
All the items are present on the single layer. |
The data items are present at different layers. |
|
It can be traversed on a single run. That is, if we start
from the first element, we can traverse all the elements sequentially in a single
pass. |
It requires multiple runs. That is, if we start from the
first element it might not be possible to traverse all the elements in a
single pass. |
|
The memory utilization is not efficient. |
Different structures utilize memory in different efficient
ways depending on the need. |
|
The time complexity increase with the data size. |
Time complexity remains the same. |
|
Example: Arrays, Stack, Queue |
Example: Tree, Graph, Map |
Why
Data Structure?
Knowledge about data
structures help you understand the working of each data structure. And, based
on that you can select the right data structures for your project.
This helps you write
memory and time efficient code.
4.3.0 ASSOCIATE PROFESSOR A Master’s Degree with at least 55% marks with 5 Yrs Of Experience (or an equivalent grade in a point scale wherever grading system is followe
concern all engineering courses with minimum (M.Tech) aggregation is required for a lecturer job in almost all top colleges
M.TECH is the entry point , no base degree , regarding underlying degree no hard and fast rule
Assistant Professors (16 hours) and Associate Professors/Professors (14 hours) by MHRD to UGC. WORKLOADS i.e one subject to 2 sections, or 2 different subjects to different semisters
Press Information Bureau
Ministry of Human Resource Development
26-May-2016 20:03 IST
Consequent on the review, the Ministry has issued a direction to the UGC, under Section 20(1) of the UGC Act, 1956, to undertake amendments in the Regulation. After these amendments are carried out, the position regarding workload will be as follows:-
(i) In the UGC (Minimum Qualifications for appointment of teachers and other academic staff in universities and colleges and measures for the maintenance of standards in higher education) Regulations, 2010, the overall workload of Assistant Professors and Associate Professors/Professors in full employment was prescribed to be not less than 40 hours a week for 180 teaching days. This workload remains unchanged, even with the amended Regulation.
(ii) The direct teaching-learning hours to be devoted by Assistant Professors (16 hours) and Associate Professors/Professors (14 hours) too will remain unchanged, as a consequence of the direction from the MHRD and subsequent notification by the UGC.
In consonance with established academic and teaching traditions, and with a view to reinforcing a student-centric and caring approach, teachers are encouraged to work with students, beyond the structure of classroom teaching. Indicatively, this could entail mentoring, guiding and counselling students. In particular teachers would be the best placed to identify and address the needs of students who may be differently-abled, or require assistance to improve their academic performance, or to overcome a disadvantage. There are no prescribed hours for such efforts, measured either in weeks or months. While they will not be included in the calculation of the API scores, these are nevertheless important and significant activities that could be carried out by teachers.
Teachers were required to allocate 6 additional hours per week, beyond the direct teaching-learning hours, on research. These hours can now be also utilized for tutorials/remedial classes/seminars/administrative responsibilities/ innovation and updating of course contents.
There will be no increase in the workload of teachers, after the amendments, in comparison with the workload prescribed earlier.
