About the Programme
BSC Data Science & Analytics with Research
B.Sc. (Hons./by Research) Data Science & Analytics
Sharda School of Engineering & Science (SSES)
-
Programme Code
SBR0308
-
Level
Graduate
-
Duration
4
This course is for individuals who...
Students who are looking for...
Course Fee
For National Students
| 1st Year 120000 | 2nd Year 123600 | 3rd Year 127308 | 4th Year 131127 |
For International Students
| Fee Per Semester | Fee Per Year |
|---|---|
| NA | 3400* |
Programme Structure
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
MSM101 |
Foundation Course in Mathematics |
4 |
0 |
0 |
4 |
4 |
Basic Mathematics up to 10+2 |
CC |
|
2. |
CMS102 |
Descriptive Statistics |
3 |
0 |
0 |
3 |
3 |
Basic Mathematics up to 10+2 |
OPE |
|
3. |
CSE113 |
Programming for Problem Solving |
3 |
0 |
0 |
3 |
3 |
|
DSE (Multi/Inter-discpli) |
|
4. |
VOM103 |
Essential Excel Skills for Business |
0 |
0 |
6 |
6 |
3 |
|
SEC |
|
5. |
ARP101 |
Communicative English-1 |
1 |
0 |
2 |
3 |
2 |
|
AEC |
|
6. |
VAC103 |
Environmental Management |
3 |
0 |
0 |
3 |
3 |
|
VAC |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
7. |
CMS151 |
Foundation Course in Mathematics Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite MSM101 |
CC |
|
8. |
CSP113 |
Programming for Problem Solving Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite CSE113 |
DSE (Multi/Inter-discpli) |
|
TOTAL CREDITS |
|
|
|
|
20 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
CMS131 |
Matrix Analysis and Linear Algebra |
4 |
0 |
0 |
4 |
4 |
Pre-requisite MSM101 |
CC |
|
2. |
CMS132 |
Mathematical Expectations & Probability Distributions |
3 |
0 |
0 |
3 |
3 |
Pre-requisite CMS102 |
OPE |
|
3. |
CSE242 |
Data Structures |
3 |
0 |
0 |
3 |
3 |
Pre-requisite CSE113 |
CC |
|
4. |
VOM104 |
Advanced Excel Skills for Business |
0 |
0 |
6 |
6 |
3 |
Pre-requisite VOM103 |
SEC |
|
5. |
ARP102 |
Communicative English-2 |
1 |
0 |
2 |
3 |
2 |
Pre-requisite ARP101 |
AEC |
|
6. |
VAC110 |
Yoga for Holistic Health |
0 |
1 |
4 |
5 |
3 |
|
VAC |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
7. |
CMS171 |
Matrix Analysis and Linear Algebra Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite CMS131 |
CC |
|
8. |
CSP242 |
Data Structures Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite CSE113 |
CC |
|
TOTAL CREDITS |
|
|
|
|
20 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
MSM312 |
Discrete Mathematics |
3 |
1 |
0 |
4 |
4 |
Pre-requisite MSM101 |
DSE |
|
2. |
BDA215 |
Operations Research |
3 |
0 |
0 |
3 |
3 |
Pre-requisite MSM101 |
OPE |
|
3. |
BDA216 |
Statistical Inference |
4 |
0 |
0 |
4 |
4 |
Pre-requisite CMS132 |
CC |
|
4. |
BDA217 |
Data Preparation and Data Cleaning |
3 |
0 |
0 |
3 |
3 |
Pre-requisite CMS132 |
CC |
|
5. |
VOM203 |
Basic Excel Modelling |
0 |
0 |
6 |
6 |
3 |
Pre-requisite VOM104 |
SEC |
|
6. |
ARP207 |
Logical Skill Building & Soft Skills |
0 |
1 |
2 |
3 |
2 |
Pre-requisite ARP102 |
AEC |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
7. |
BDA261 |
Statistical Inference Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA216 |
CC |
|
8. |
BDA262 |
Data Preparation and Data Cleaning Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA217 |
CC |
|
9. |
RBL001 |
Research Report Writing-I (RBL-1) |
0 |
0 |
2 |
2 |
0 |
Pre-requisite ARP102 |
Project (Non-graded Qualifying) |
|
TOTAL CREDITS |
|
|
|
|
21 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
BDA218 |
Data Ware Housing & Data Mining |
3 |
0 |
0 |
3 |
3 |
Pre-requisite BDA217 |
CC |
|
2. |
BDA202 |
Database Management Systems |
4 |
0 |
0 |
4 |
4 |
Pre-requisite MSM312 |
CC |
|
3. |
BDA214 |
Sampling Theory |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA216 |
DSE |
|
4. |
ARP306 |
Campus to Corporate |
0 |
1 |
2 |
3 |
2 |
Pre-requisite ARP207 |
AEC |
|
5. |
OPE |
Open Elective-1 |
3 |
0 |
0 |
3 |
3 |
|
OPE |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
6. |
BDA270 |
Data Ware Housing & Data Mining Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA218 |
CC |
|
7. |
BDA271 |
Database Management Systems Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA202 |
CC |
|
8. |
BDA272 |
Sampling Theory Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA214 |
DSE |
|
9. |
RBL002 |
Research Based Learning-II (RBL-2) |
0 |
0 |
2 |
2 |
0 |
Pre-requisite RBL001 |
Project (Non-graded Qualifying) |
|
TOTAL CREDITS |
|
|
|
|
19 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
BDA346 |
Artificial Intelligence |
5 |
0 |
0 |
5 |
5 |
Pre-requisite BDA218 |
CC |
|
2. |
BDA303 |
Machine Learning |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA218 |
CC |
|
3. |
BDA319 |
Regression Analysis |
3 |
0 |
0 |
3 |
3 |
Pre-requisite BDA214 |
CC |
|
4. |
BDA320/ BDA321 |
Advanced Statistical Analysis/ Experimental Design |
2 |
0 |
0 |
2 |
2 |
|
DSE (Multi/Inter-discpli) |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
5. |
BDA355 |
Machine learning Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA303 |
CC |
|
6. |
BDA356 |
Regression Analysis Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA319 |
CC |
|
7. |
INC001 |
Industry Connect |
0 |
0 |
4 |
4 |
2 |
|
Project |
|
8. |
RBL003 |
Research Based Learning-III (RBL-3) |
0 |
0 |
2 |
2 |
1 |
Pre-requisite RBL002 |
Project |
|
9. |
BDA359/ BDA363 |
Advanced Statistical Analysis Lab/ Experimental Design Lab |
0 |
0 |
2 |
2 |
1 |
|
DSE (Multi/Inter-discpli) |
|
TOTAL CREDITS |
|
|
|
|
20 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
CMS331 |
Numerical Methods |
4 |
0 |
0 |
4 |
4 |
Pre-requisite CMS131 |
CC |
|
2. |
BDA322 |
Statistical Simulation |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA319 |
CC |
|
3. |
BDA323 |
Multivariate Data Analysis |
3 |
0 |
0 |
3 |
3 |
Pre-requisite BDA319 |
CC |
|
4. |
BDA325 |
Deep Learning |
3 |
0 |
0 |
3 |
3 |
Pre-requisite BDA303 |
OPE |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
5. |
CMS371 |
Numerical Methods Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite CMS331 |
CC |
|
6. |
BDA360 |
Statistical Simulation Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA322 |
CC |
|
7. |
BDA361 |
Multivariate Data Analysis Lab |
0 |
0 |
2 |
2 |
1 |
Co-requisite BDA323 |
CC |
|
8. |
CCU108 |
Community Connect |
0 |
0 |
4 |
4 |
2 |
|
Project (Multi/Inter-discpli) |
|
9. |
RBL004 |
Research Based Learning-IV (RBL-4) |
0 |
0 |
2 |
2 |
1 |
Pre-requisite RBL003 |
Project |
|
TOTAL CREDITS |
|
|
|
|
20 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
MDA104 |
Next Generation Databases |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA346, 303, 323 |
CC |
|
2. |
MDA109 |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA323 |
CC |
|
|
3. |
MDA110/ MDA112 |
Time Series, Forecasting and Index Number/ Econometrics |
3 |
0 |
0 |
3 |
3 |
|
DSE/CC* |
|
4. |
MDA111/ MDA113 |
Non-Parametric Statistical Inference/Survival Analysis |
4 |
0 |
0 |
4 |
4 |
|
DSE/CC* |
|
5. |
OPE |
Open Elective-1 |
4 |
0 |
0 |
4 |
4 |
|
OPE |
|
|
PRACTICALS |
|
|
|
|
|
|
|
|
|
6. |
MDA155/ MDA156 |
Time Series, Forecasting and Index Number Lab/ Econometrics Lab |
0 |
0 |
2 |
2 |
1 |
|
DSE/CC* |
|
TOTAL CREDITS |
|
|
|
|
20 |
|
|
||
|
S. No. |
Course Code |
Course Name |
Teaching Load |
Credits |
Pre-Requisite/ Co-Requisite |
Type of Course: 1. CC 2. DSE 3. OPE 4. SEC 5. AEC 6. VAC 7.Project |
|||
|
|
THEORY |
|
L |
T |
P |
TOTAL (hrs) |
|
|
|
|
1. |
MDA107 |
4 |
0 |
0 |
4 |
4 |
Pre-requisite MDA109 |
CC |
|
|
2. |
MDA114 |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA322, 323 |
CC |
|
|
3. |
MDA117 |
4 |
0 |
0 |
4 |
4 |
Pre-requisite BDA303,322,346, |
CC |
|
|
4. |
MDA115/ MDA116 |
Demography/ Statistical Quality Control |
4 |
0 |
0 |
4 |
4 |
|
DSE/CC* |
|
5. |
OPE |
Open Elective-2 |
4 |
0 |
0 |
4 |
4 |
|
OPE |
|
TOTAL CREDITS |
|
|
|
|
20 |
|
|
||
Programme Curricula
| S.No. | Academic year | Curricula Link |
|
1 |
2023-24 | View Details |
Eligibility Criteria
| For National Students |
|
| For International Students | The eligibility criterion for all programs for international applicants is minimum 50% in the qualifying examination and having studied the pre-requisite subjects for admission in to the desired program. |
How to apply for the course?
Career path you can choose after the course