ANNOUNCEMENTS
The Four Year Under-Graduate Programme (FYUP) in Data Science (BSc Honours/BSc Honours with Research) have been designed in accordance with the National Education Policy (NEP) 2020 and the recent guidelines issued by the University Grants Commission with in-built options of Multiple Entry and Multiple Exit mapped with employability opportunities.
Eligibility
A Senior Secondary School Examination (10+2) certificate in any discipline or equivalent, from a recognized Board of Education with at least 50% marks in aggregate. There is no upper age bar.
Degree in Data Science: B.Sc./ B.Sc. (Honours)/ B.Sc.(Honours with Research)
Data science is an interdisciplinary, rapidly emerging branch of learning that facilitates extracting information from large datasets using scientific methods that combine mathematics, statistics, computer science, machine learning, artificial intelligence, deep learning, and domain-specific knowledge.
The proposed interdisciplinary FYUP in Data Science will provide formal training in various quantitative techniques, along with a unique blend of theory and practice interwoven in a qualitative matrix. The programme will facilitate a systematic amalgamation of widespread knowledge under a common platform.
The programme has been innovatively designed that will provide students to have in-depth study of interdisciplinary major in Data Science with an option to choose from interdisciplinary minors and skill-based courses relating to a chosen thematic area – environmental studies, climate science, geoinformatics, economics and management studies.
Admission Criteria
Admission to FYUP and FYIPP in Data Science shall be on the basis of merit of marks secured in 10+2 or equivalent examination in aggregate of best of three subjects and one language.
Formal classroom instruction, workshops, hands-on practise, field excursions, case studies, field visits, quizzes, term papers, assignments, and tutorials will be among the pedagogical strategies used. Individual and group projects will be utilised to show specific environmental and social science challenges using a variety of spatial-temporal and time-series information. Interactive workshops and industrial training will be organised with data science and plan execution players and stakeholders from the government, business sector, entrepreneurs, and NGOs. Data science intersects with the environment, climate science, and related business sectors, as well as geoinformatics.
