Data Science
Coordinator Professor (Dr.) Anirban Mukhopadhyay
Head of the Department-
Departmental E-mail Address
About the Course
During the last decade, it has become a universal truth that modern business will be driven by data. Most of the organizations are therefore opening up their doors to big data. Data science professionals are those who can unlock the power of data to extract actionable insights out of such large amount of data. As per a report published in Times of India in July 2018, India has observed more than 400% rise in requirement for data science professional in different industry sectors, while the supply of such talent has a very slow growth in the country. Good data scientists having both theoretical knowledge and programming skill are scarce and on high demand. As a data scientist, one has to understand the business problems, collect and format data, applying algorithms, and make recommendations backed by data. To perform these tasks professionally, one has to be trained well with many facets of data science, artificial intelligence, machine learning, deep learning, statistical analysis and data mining along with good programming skills. The objective of this course is to produce such trained data scientists who will have an edge for being hired in the positions like data scientists, data analyst and data science manager in the industry as well as academia.
Objective
This course will help the candidates in mastering data science tools and techniques like statistical analysis of data, machine learning, clustering, neural networks, deep learning classification, regression, association rule mining, decision trees, soft computing, big data analytics with Hadoop and Apache Spark, cloud infrastructure, IoT and other data science applications. The candidates will also be able to code data science programs using Python/R and gain from the implementation of some data science-related projects. This will help the candidates to develop the required skill set for building a career as a data science professional in the industry, as an entrepreneur to establish a start-up, or as a researcher in some R&D organization.
Programmes offered
| Sl. No. | Programme | Duration | Intake capacity | Admission criteria |
|---|---|---|---|---|
| No programmes available. | ||||
Faculty Profile(Alphabetical Order)
Department Details
Coordinator
Prof. Anirban Mukhopadhyay, M.E., Ph.D. (Engg.) – [Professor, Dept. of Computer Science & Engineering]
Deputy Coordinator
Prof. Samares Pal, M.Sc., Ph.D. – [Professor and Head, Dept. of Mathematics]
Full-time Faculty Member
Dr. Samir Maity, M.Sc., MCA, Ph.D. Click here for details
Renowned professors and scientists from in-house as well as various academic institutes and industries for teaching and mentoring
“Motivational talks” every month by eminent scientists with special attention on industrial personalities
University of Kalyani has good research and study setup in various classical science and engineering disciplines like Mathematics, Statistics, Computer Science & Engineering, Business Administration, Physics, Chemistry, and several branches of life sciences. Due to the growing demand of the data science professionals and scarcity of organized data science programmes in the state, University of Kalyani feels the need of introducing a complete two-year M.Sc. programme in Data Science exploiting the expertise of the faculties and resources from various existing disciplines mentioned above. Moreover, various other premiere academic and research institutes in close vicinity of the university, like IIIT Kalyani, NIBMG, IISER Kolkata, Kalyani Govt. Engineering College, Kalyani Medical College, ISI Kolkata, Bose Institute, Saha Institute of Nuclear Physics, University of Calcutta, JadavpurUniversity etc. has good collaboration with University of Kalyani in various academic aspects. The university will therefore get good support of faculties from these institutes.Moreover, faculties from industries will also teach various industry-related papers.
There is an increasing demand for data science professionals in different industries. The students having a data science degree will get opportunity to work as data scientist, data analyst or data engineer in different industry like banking and finance, healthcare, pharmaceutical, travel and tourism, biomedical and biotechnology, e-commerce and many other where lot of data are being generated every day. Besides this, students can also opt for higher studies and research in the areas of data mining, machine learning, big data analytics etc. both in India and abroad. Furthermore, students can also go for entrepreneurship in the above areas by setting up start-up companies to serve different real-life problems
| Semester | Paper Code | Paper Name | Theory/Practical | Credit | Hrs/Wk
[L-T-P] |
| I | DST 101 | Mathematics & Statistics – I | Theory | 4 | 3-1-0 |
| DST 102 | Algorithms & Data Structure | 4 | 3-1-0 | ||
| DST 103 | Database Management Systems | 4 | 3-1-0 | ||
| DST 104 | Introduction to Data Science &Artificial Intelligence | 4 | 3-1-0 | ||
| DSP 111 | Algorithms & Data Structure Laboratory with C | Practical | 3 | 0-0-9 | |
| DSP 112 | Database Management Systems Laboratory | 2 | 0-0-6 | ||
| DSP 113 | Statistics and Machine Learning with Excel/R/SPSS Laboratory | 2 | 0-0-6 | ||
| LSS 121 | Communicative English &HR Management– I | Sessional | 1 | 0-0-3 | |
| Total | 24 | 12-4-24 | |||
| II | DSTO 201 | CBCS Open Choice Course | Theory | 4 | 3-1-0 |
| DST 202 | Mathematics & Statistics – II | 4 | 3-1-0 | ||
| DST 203 | Machine Learning | 4 | 3-1-0 | ||
| DST 204 | Big Data Analytics & Cloud Computing | 4 | 3-1-0 | ||
| DSP 211 | Machine Learning Laboratory with Python | Practical | 3 | 0-0-9 | |
| DSP 212 | Information Visualization Laboratory | 2 | 0-0-6 | ||
| DSP 213 | Big Data Analytics and Cloud Computing Laboratory | 2 | 0-0-6 | ||
| LSS 221 | Communicative English & HR Management – II | Sessional | 1 | 0-0-3 | |
| Total | 24 | 12-4-24 | |||
| III | DST 301 | Entrepreneurship & IPR | Theory/Tutorial | 3 | 2-1-0 |
| DST 302 | Research Methodology | 3 | 2-1-0 | ||
| DSE 303 | Elective – I | 2 | 0-2-0 | ||
| DSE 304 | Elective – II | 2 | 0-2-0 | ||
| DSE 305 | Elective-III (Student’s choice) | 2 | 0-2-0 | ||
| DSS 321 | Review on Frontiers in Data Science | Sessional | 2 | – | |
| DSS 322 | Project/Training/Seminar | 2 | 0-0-6 | ||
| Total | 16 | 4-8-6 | |||
| IV | DSS 421 | Dissertation (Final) | Sessional | 10 | 0-0-40 |
| DSS 422 | Seminar | 2 | 0-0-6 | ||
| DSS 423 | Grand Viva | 4 | — | ||
| Total | 16 | 0-0-46 | |||
DST Data Science Theory
DSP Data Science Practical
LSS Life Skill course Sessional
DSTO Data Science Theory (Open Choice)
DSE Data Science Elective
DSS Data Science Sessional
1st Digit in the Suffix No. of Semester [1= Semester I, 2= Semester II, 3= Semester III, 4= Semester IV]
2nd Digit in the Suffix Type of paper [0 = Theory paper; 1 = Practical paper; 2 = Sessional Paper]
3rd Digit in the Suffix No. of paper in each category (Theory, Practical and Sessional)
1. Time Series Analysis
2. Natural Language Processing
3. Computational Biology
4. Complex Network Analysis
5. Image Processing & Computer Vision
6. Information Security
7. Multicriteria Decision Making