B.Sc. programme in Artificial intelligence and and Data Science offers a wide range of opportunities. This programme is an undergraduate degree programme designed to provide students with the skills and knowledge required to analyse and interpret large data sets, create machine learning models, and design and implement artificial intelligence systems. The programme mixes statistics, mathematics, computer science, and engineering ideas to give students a thorough understanding of data science, artificial intelligence, and machine learning.
The Candidate must have passed 12th with Science and Mathematics from a recognized board
The admission is based on merit.
Graduates can work as data analysts, assisting organisations in collecting, analysing, and interpreting huge datasets to acquire insights into customer behaviour, business processes, and market trends. They can work as data scientists, analysing and interpreting data using statistical and mathematical models, as well as developing machine learning models to automate processes and make better decisions.
Graduates can work as machine learning engineers, creating, developing, and implementing machine learning models to automate processes and improve decision-making in a variety of industries such as healthcare, banking, and e-commerce. Graduates can work as data engineers, assisting organisations in designing, developing, and maintaining data management systems and warehouses. They can work as business intelligence analysts, assisting organisations in making data-driven decisions by developing reports and visualisations that provide insights into company performance.
The programme takes an interdisciplinary approach to data science, incorporating topics from computer science, mathematics, and statistics to provide students a comprehensive understanding of the field. The curriculum is meant to be industry-relevant, equipping students with the skills and information required to thrive in a variety of occupations across industries. Hands-on training in various tools and technologies used in data science, including programming languages, data visualization tools, and machine learning libraries. The program is equipped with cutting-edge technology, including computer labs and data centres, providing students with access to the latest tools and technologies in data science.
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1. |
AEC |
ENG101T |
English |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
2 |
Major |
BSAD101T |
Python Programming(IBM) |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
3 |
Major |
BSAD101P |
Python Programming Practical(IBM) |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
4. |
Major |
COM101T |
Introduction to computer and programming using ‘C’ |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
5. |
Major |
COM101P |
Introduction to computer and programming using ‘C’ – Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
6. |
Major |
MIT102T |
Database Management System |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
7. |
Major |
MIT102P |
Database Management System – Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
8. |
SEC |
BSAD102T |
Digital Computer Fundamental |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
9. |
Multidisciplinary |
BSAD103T |
Basics of Statistics |
4 |
1 |
0 |
5 |
20 |
20 |
– |
60 |
100 |
|
10. |
SEC |
SAT101P |
Software Applications and Tools – Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
11 |
Audit Course |
— |
Induction Program |
0 |
0 |
0 |
0 |
– |
– |
– |
– |
– |
|
Total |
26 |
22 |
120 |
120 |
160 |
600 |
1000 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1. |
AEC |
ENV101T |
Environmental Science |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
2. |
Major |
BSAD201T |
Devops(IBM) |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
3. |
Major |
BSAD201P |
Devops Practical(IBM) |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
4. |
Major |
MIT201T |
Data Structure and Algorithms |
4 |
0 |
0 |
4 |
20 |
20 |
– |
60 |
100 |
|
5. |
Major |
MIT201P |
Data Structure and Algorithms – Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
6. |
Major |
BSAD202T |
Principles of Data Science |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
7. |
Major |
BSAD202P |
Principles of Data Science Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
8. |
Multidisciplinary |
BSAD203T |
Basics of Mathematics |
4 |
1 |
0 |
5 |
20 |
20 |
– |
60 |
100 |
|
9. |
VAC |
VAC101T |
Yoga and Sports for holistic development |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
10 |
IKS |
IKS101T |
Foundational Literature of indian Civilization |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
Total |
25 |
22 |
140 |
140 |
120 |
600 |
1000 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1. |
Major |
BSAD301T |
Data Visualization using R and Watson Studio(IBM) |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
2 |
Major |
BSAD301P |
Data Visualization using R and Watson Studio Practical(IBM) |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
3 |
VAC |
IPR301T |
Business Ethics and Intellectual Property Rights |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
4. |
Major |
BTCE505T |
Artificial Intelligence |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
5. |
Major |
BTCE505P |
Artificial Intelligence Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
6. |
Major |
MIT204T |
Object Oriented Programming with JAVA |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
7. |
Major |
MIT204P |
Object Oriented Programming with JAVA – Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
8. |
Minor |
BSAD302T |
Operations Research |
3 |
1 |
0 |
4 |
20 |
20 |
– |
60 |
100 |
|
9. |
Multidisciplinary |
BSAD303T |
Numerical Methods |
2 |
1 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
10 |
IKS |
IKS102T |
Indian Health Science |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
Total |
26 |
23 |
140 |
140 |
120 |
600 |
1000 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1 |
Major |
BSAD401T |
Predictive Modelling using SPSS Modeler(IBM) |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
2 |
Major |
BSAD401P |
Predictive Modelling using SPSS Modeler(IBM) |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
3 |
Major |
BTCE704T |
Machine Learning |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
4 |
Major |
BTCE704P |
Machine Learning Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
5 |
Major |
BTCE501T |
Design and Analysis of Algorithms |
4 |
0 |
0 |
4 |
20 |
20 |
– |
60 |
100 |
|
6 |
Major |
BTCE501P |
Design and Analysis of Algorithms- Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
7 |
Minor |
MDS104T |
Statistical Inference |
2 |
1 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
8 |
SEC |
BOM401T |
Management-I (Business and Organizational Management) |
4 |
0 |
0 |
4 |
20 |
20 |
– |
60 |
100 |
|
9 |
VAC |
INC401T |
Indian Constitution |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
Total |
24 |
21 |
120 |
120 |
120 |
540 |
900 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1. |
Major |
BSAD501T |
Deep Learning(IBM) |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
2 |
Major |
BSAD501P |
Deep Learning Practical(IBM) |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
3 |
Major |
BSAD503T |
Basics of Reinforcement |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
4 |
Major |
BSAD503P |
Basics of Reinforcement Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
5 |
Minor |
MIT301T |
Software Engineering |
4 |
0 |
0 |
4 |
20 |
20 |
– |
60 |
100 |
|
6 |
Major |
BTCE508CT |
Pattern Recognition |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
7 |
Major |
BTCE508CP |
Pattern Recognition Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
8 |
Minor |
MDS304T |
Time Series and Forecasting |
3 |
1 |
0 |
4 |
20 |
20 |
– |
60 |
100 |
|
9 |
Minor |
BSAD504T/ BTCE702T |
Elective – I |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
Total |
25 |
22 |
120 |
120 |
120 |
540 |
900 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1. |
Major |
BSAD601T |
Spark and Scala Fundamentals(IBM) |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
2 |
Major |
BSAD601P |
Spark and Scala Fundamentals Practical(IBM) |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
3 |
SEC |
BSAD602P |
Software Testing Practical |
0 |
0 |
4 |
2 |
– |
– |
40 |
60 |
100 |
|
4 |
AEC |
BSAD603T |
Entrepreneurship |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
5 |
Minor |
BSAD604AT/BSAD604BT |
Open Elective-I(Django/Fundamentals of Robotics |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
6 |
Minor |
BSAD605T/BTCE508AT |
Elective-II (Business Data Analysis using Tableau/Internet of Things |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
7 |
Major |
BSAD502T |
Ethics in AI and DS |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
8 |
Internship |
BSAD606P |
Internshp |
0 |
0 |
12 |
6 |
– |
– |
40 |
60 |
100 |
|
Total |
31 |
22 |
100 |
100 |
120 |
480 |
800 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1 |
Major |
BSAD701T |
IBM Watson Services |
2 |
0 |
0 |
2 |
20 |
20 |
|
60 |
100 |
|
2 |
Major |
BSAD701P |
IBM Watson Services Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
3 |
Major |
BTCE703T |
Big Data Analytics |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
4 |
Major |
BTCE703P |
Big Data Analytics Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
5 |
Major |
BTCE601T |
R Programming |
3 |
0 |
0 |
3 |
– |
– |
40 |
60 |
100 |
|
6 |
Major |
BTCE601P |
R Programming Practical |
0 |
0 |
2 |
1 |
– |
– |
40 |
60 |
100 |
|
7 |
Minor |
BSAD702AT/BSAD702BT |
Elective – III |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
8 |
AEC |
COS101T |
Communication Skills |
2 |
0 |
0 |
2 |
20 |
20 |
– |
60 |
100 |
|
9 |
OJT |
BSAD703P/ BSAD801P |
On Job Training /Research Project(IBM) |
0 |
0 |
12 |
6 |
– |
– |
40 |
60 |
100 |
|
Total |
31 |
22 |
80 |
80 |
200 |
540 |
900 |
|||||
|
S. No. |
Course Category |
Course Code |
Course Title |
Teaching Scheme |
Examination Scheme (Max. Marks) |
|||||||
|
Teaching Hours per week |
C |
IA |
SEE |
Total |
||||||||
|
L |
T |
P |
MSE |
Assi |
CA |
|||||||
|
1. |
Minor |
BSAD703P/ BSAD801P |
On Job Training /Research Project(IBM) |
0 |
0 |
12 |
6 |
– |
– |
40 |
60 |
100 |
|
2 |
Major |
BSAD802T |
MLOPS(Docker,Jenkins |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100
|
|
3 |
Major |
BTCE707BT |
Natural Language Processing Technique |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
4 |
Major |
BTCE707BP |
Natural Language Processing Technique Practical |
0 |
0 |
2 |
1 |
20 |
20 |
– |
60 |
100 |
|
5 |
Major |
DSS608 |
Business Intelligence using Power BI |
3 |
0 |
0 |
3 |
20 |
20 |
|
60 |
100 |
|
6 |
Minor |
BTCE605BT/BSAD803T |
Open Elective – II |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
7 |
Minor |
BSAD804AT/BSAD804BT |
Elective-IV(Data Security and Privacy/DImage Processing) |
3 |
0 |
0 |
3 |
20 |
20 |
– |
60 |
100 |
|
Total |
26 |
22 |
120 |
120 |
40 |
300 |
700 |
|||||