M.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

Academic

Contact

SASTRA DEEMED UNIVERSITY
Tirumalaisamudram
Thanjavur - 613401
Tamilnadu, India

+91 4362 264101 - 108
        304000 - 010
+91 4362 264120

admissions@sastra.edu