The MSc Data Science and Analytics programme at JAIN (Deemed-to-be University) provides advanced training in data-driven thinking, statistical modelling, and computational analytics. The curriculum integrates mathematics, statistics, and computer science to develop expertise in analysing complex datasets and generating actionable insights. Key learning areas include machine learning, big data analytics, deep learning, data visualisation, and advanced statistical methods that support evidence-based decision making across industries.
The programme emphasises experiential learning through laboratory sessions, data analysis projects, and industry-aligned coursework. Students gain hands-on experience in predictive modelling, data pipeline development, and the application of artificial intelligence to real-world datasets. Internships, collaborative projects, and applied research activities further strengthen practical skills and innovation. Specialised training integrated with the GUVI–HCL certification programme enhances technical competencies in emerging data science technologies and strengthens industry readiness.
By combining theoretical depth with applied learning, the programme prepares graduates for careers in data analytics, machine learning, business intelligence, and AI-driven decision systems across sectors such as technology, finance, healthcare, and consulting. The research-oriented structure also supports progression to PhD programmes and advanced research in artificial intelligence, data engineering, and computational analytics.
Certifications Offered: Certification courses from GUVI-HCL
A student who has passed a 3-year undergraduate degree (level 5.5, a total of 120 credits) shall be eligible for admission to 2-year MSc programmes.
Preference will be given to candidates who have secured at least 50% marks (45% marks in case of candidates belonging to reserved category) in the qualifying examination.
However, students who have not studied requisite courses from Science discipline related to the respective programmes will have to undertake the prescribed bridge course(s).
Duration: 2 Years (4 Semesters)
No. of Credits: 88 Credits (as per UGC CCFPP and NCrF framework)
School of Sciences
#34, 1st Cross,
J C Road Bangalore - 560 027
P: +91 80 4343 0100
JAIN Knowledge Campus
#44/4, District Fund Road
Jayanagar 9th Block Campus
Bangalore - 560 069
P : +91 73376 13222
Comprehensive Two-Year Structure: Structured progression from foundational concepts to advanced data science and analytics techniques.
Industry-Integrated Curriculum: Embedded GUVI HCL certification aligned with current industry tools, platforms, and professional workflows.
Strong Theoretical Foundations: Emphasis on statistics, probability, linear algebra, and algorithmic thinking to build robust analytical capability.
Advanced Data Science Technologies: Coverage of machine learning, deep learning, big data analytics, cloud-based data pipelines, and AI-driven systems.
Hands-on and Experiential Learning: Extensive laboratory sessions, real-world datasets, case studies, internships, and capstone projects.
Co-Branded Professional Certification: GUVI HCL co-branded certification strengthens professional credibility and employability.
Research and Innovation Orientation: Exposure to research methodologies, advanced analytics, and problem formulation for research and development roles.
Industry-Relevant Skill Development: Training aligned with roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and AI Specialist.
Interdisciplinary Learning Environment: Integration of statistics, computing, artificial intelligence, and domain applications for holistic data science expertise.
Future Academic and Professional Pathways: Strong preparation for doctoral studies, advanced research, and leadership roles in the data science ecosystem.
JAIN (Deemed-to-be University) enhances the learning experience through a series of career-focused and research-oriented initiatives that complement the academic curriculum. These programmes provide students with opportunities to interact with industry professionals, participate in research projects, and gain practical exposure through internships, workshops, and conferences. The approach ensures that graduates are well prepared for professional careers, research roles, and higher studies.
Research Accelerator Programme (RAP) Structured mentoring that encourages students to undertake guided research projects and develop strong research skills.
Scientific Writing and Publication Support Training in academic writing, research documentation, and preparation of manuscripts for scholarly journals.
Patent and Research Publication Assistance Guidance and support for filing patents and publishing research in recognised national and international journals.
Industrial and Laboratory Visits Exposure to industry facilities and advanced laboratories to understand real-world applications of scientific concepts and technologies.
Guest Lectures by Scientists and Industry Experts Regular interactions with leading researchers and professionals to provide insights into emerging trends and industry practices.
Competitive Examination Preparation Support for national-level examinations such as GATE, JEST, and CSIR NET to help students pursue research and academic careers.
Conference Presentation Opportunities Encouragement and support for students to present their research at national and international conferences.
Internships and Research Collaborations Opportunities to work with industry partners, research institutions, and laboratories to gain practical and research experience.
Graduates of the MSc Data Science and Analytics acquire practical and analytical skills that prepare them for diverse professional pathways, including:
Data Modelling and Predictive Analytics
Apply statistical, machine learning, and deep learning techniques to analyse complex datasets. Roles include Data Scientist, Machine Learning Engineer, and Predictive Analytics Specialist.
Data Pipeline Development and Big Data Management
Build, maintain, and optimise scalable data pipelines and storage solutions. Roles include Data Engineer, Big Data Developer, and Cloud Analytics Specialist.
Data Interpretation and Strategic Decision-Making
Translate data insights into actionable strategies for business and industry. Roles include Business/Data Analyst, Product Analyst, and Decision Scientist.
Artificial Intelligence and Natural Language Processing
Develop AI-driven systems, chatbots, and language models for practical applications. Roles include AI Specialist, NLP Engineer, and Intelligent Systems Developer.
Data-Driven Problem Solving and Innovation
Design, test, and implement solutions for real-world challenges using data science methodologies. Roles include Data Science Consultant, Innovation Analyst, and Research Associate.
Research, Policy, and Academic Analysis
Conduct research, perform evidence-based analysis, and support policy or academic initiatives. Roles include Research Scientist, Policy Analyst, and PhD Researcher.
Entrepreneurship and Technology-Driven Ventures
Leverage analytical and computational expertise to launch startups or develop innovative products. Roles include Entrepreneur, Startup Founder, and Technology Product Innovator.
Global Collaboration and Interdisciplinary Application
Work on international projects or cross-disciplinary teams applying data science in sectors like finance, healthcare, and sustainability. Roles include Global Data Analyst, AI Project Specialist, and Cross-Disciplinary Researcher.
Applicants must hold a Bachelor’s degree at NHEQF Level 6 in a relevant discipline such as Computer Science, Statistics, Mathematics, or related fields.
The programme does not offer formal specialisations. However, elective courses and project work allow students to focus on areas such as Machine Learning, Artificial Intelligence, Big Data, and Natural Language Processing.
Students gain hands-on experience with industry-relevant tools and technologies including Python, R, SAS, SQL, Tableau, Power BI, and Big Data platforms.
Yes, internships are mandatory and carry a minimum of two credits, providing students with practical industry experience.
Students receive career guidance that includes résumé development, interview preparation, skill enhancement sessions, and placement support.
Yes, the programme includes research-oriented coursework, academic writing guidance, and project work that help prepare students for doctoral studies and research careers.
Graduates can pursue careers in sectors such as technology, finance, healthcare, e-commerce, education, consulting, and government organisations.
Yes, the programme includes a dissertation worth 16 credits, distributed across the four semesters, allowing students to conduct independent research.
The curriculum includes courses in Deep Learning, Artificial Intelligence, and Big Data, along with workshops, seminars, and guest lectures by industry experts.
The capstone project is a final semester project where students apply their knowledge to solve real-world problems, strengthening both research capabilities and industry readiness.