chem.sastra.eduM.Tech. Big Data Biology
Programme Educational Objectives
The M.Tech. (Big Data Biology) programme at SASTRA envisions creating a knowledge hub of graduates who can address key challenges in the emerging frontiers of data-driven biotechnology including the exploration, analysis, interpretation, modelling and simulation of biological data for obtaining actionable insights in industrial and research settings. The specific objectives of the programme are:
- To impart fundamental concepts and principles of big data biology and techniques required for effective exploration, analysis, and interpretation of biological data.
- To provide a broad base of basic computational biology concepts and skills and an in-depth knowledge of at least one area of biology including understanding of biological big data analytics
- To inculcate a sense of scientific curiosity and self-learning abilities to make them life-long learners.
- To create individuals who possess ability to effectively communicate with a range of audience possessing varying degrees of scientific knowledge
- To develop interpersonal skills to work as a team in a multidisciplinary front to accomplish a common goal
- To equip the graduates with time and project management capabilities
- To create ethical professionals with social responsibility and legal awareness
Programme Specific Learning Outcomes
Upon completion of the programme, the graduates will be able to
- Explain the role of big data in biology.
- Apply algorithmic methods to analyze and interpret biological big data.
- Develop efficient repositories and tools for biological data sharing.
- Employ existing and create new big data-based computational tools to provide insights and solutions into biological problems.
- Work in a multidisciplinary team with effective communication and interpersonal skills.
- Contribute to the welfare of the society with ethical and environmental consciousness.
M. Tech. - Big Data Biology
(For students to be admitted from 2020–21)
Scheme of Study
I Semester (22 Credits)
|
Course Code |
Course Name |
No of Contact Hours / Week |
Credits |
||
|
L |
T |
P |
|||
|
MAT445 |
Probability and Statistics using R |
3 |
0 |
2 |
4 |
|
BIN501 |
Computational Methods in Bioinformatics |
3 |
1 |
0 |
4 |
|
BIN522 |
Python for Data Science |
2 |
1 |
2 |
4 |
|
INT531 |
Data Mining Techniques |
3 |
1 |
0 |
4 |
|
INT413 / BIT524 |
RDBMS, SQL and Visualization/ Cell and Molecular Biology |
4 |
0 |
0 |
4 |
|
BIN505 |
Sequence and Structure Analysis Lab |
0 |
0 |
2 |
1 |
|
TNP101 |
Soft Skills I |
0 |
0 |
2 |
1 |
|
TOTAL |
15 |
3 |
8 |
22 |
|
II Semester (26 Credits)
|
Course Code |
Course Name |
No of Contact Hours / Week |
Credits |
||
|
L |
T |
P |
|||
|
INT534 |
Machine Learning |
4 |
0 |
2 |
5 |
|
CSE614 |
Big Data Mining and Analytics |
3 |
0 |
2 |
4 |
|
BIN523 |
Algorithms in Computational Genomics |
3 |
0 |
0 |
3 |
|
BIN524 |
Biological Data Analysis – Tools and Techniques |
3 |
1 |
0 |
4 |
|
|
Department Elective – I |
2 |
1 |
0 |
3 |
|
MAN106 |
Research Methodology & IPR |
2 |
0 |
0 |
2 |
|
OEXYZ |
Open Elective I |
3 |
0 |
0 |
3 |
|
BIN399 |
Seminar |
0 |
0 |
2 |
1 |
|
TNP102 |
Soft Skills II |
0 |
0 |
2 |
1 |
|
TOTAL |
20 |
3 |
6 |
26 |
|
Summer (3 Credits)
|
Course Code |
Course Name |
No of Contact Hours / Week |
Credits |
||
|
L |
T |
P |
|||
|
BIN598 |
Summer Project |
0 |
0 |
6 |
3 |
|
TOTAL |
0 |
0 |
6 |
3 |
|
III Semester (24Credits)
|
Course Code |
Course Name |
No of Contact Hours / Week |
Credits |
||
|
L |
T |
P |
|||
|
BIN525 |
Molecular Modelling and Drug Design |
3 |
0 |
2 |
4 |
|
BIN526 |
Systems Modelling |
3 |
0 |
0 |
3 |
|
|
Department Elective – II |
3 |
0 |
0 |
3 |
|
|
Department Elective – III |
3 |
0 |
0 |
3 |
|
OEXYZ |
Open Elective II |
3 |
0 |
0 |
3 |
|
MAN107 |
Digital Pedagogy and Collaborative Learning |
2 |
0 |
0 |
2 |
|
BIN541 |
Systems Modelling Lab |
0 |
0 |
2 |
1 |
|
BIN527 |
Genome sequencing: data analysis Lab |
0 |
0 |
2 |
1 |
|
BIN528 |
Network science Laboratory |
0 |
0 |
2 |
1 |
|
BIN599 |
Project (Phase I) |
0 |
0 |
6 |
3 |
|
TOTAL |
17 |
0 |
14 |
24 |
|
IV Semester(15 Credits)
|
Course Code |
Course Name |
No of Contact Hours / Week |
Credits |
||
|
L |
T |
P |
|||
|
BIN600 |
Project and Viva Voce |
0 |
0 |
30 |
15 |
|
TOTAL |
0 |
0 |
30 |
15 |
|
DEPARTMENTAL ELECTIVES
BIN533: Healthcare data analytics (3-0-0-3)
BIN529: Data Science for Structural Biology (3-0-0-3)
BIN534: Drug informatics (3-0-0-3)
CSE608: Image processing and analysis (4-0-0-4)
CSE615: Deep Learning & Applications (3-0-2-4)
List of Open Electives - Proposed - for II and III Semesters (Any two to be selected)
|
Course Code |
Course Name |
|
Indian Constitution |
|
|
Business Analytics |
|
|
Industrial Safety |
|
|
Operations Research |
|
|
Cost Management of Engineering Projects |
|
|
Composite Materials |
|
|
Waste to Energy |
|
|
Indian Heritage |
|
|
Engineering Economics and Management |
|
|
Data Science Ethics |
