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Duration
This is a three year degree program thoughtfully designed for curious minds who enjoy solving real-world problemsthrough numbers, logic, and technology. It offers students a strong grounding in statistical theory while also opening doors to the exciting world of modern data science. From understanding the fundamentals of probability and inference to diving into machine learning, big data, and artificial intelligence, this program ensures a well-rounded academic journey. What sets it apart is its hands-on approach—students learn to code in Python and R, explore data using industry-relevant tools, and apply their skills to meaningful projects and internships. The program is flexible, with a wide variety of electives ranging from Natural Language Processing and Cloud Computing to Healthcare Analytics and Quantum Computing, allowing students to tailor their learning to their interests and career goals. Whether you see yourself building AI systems, analyzing trends in finance, solving healthcare challenges, or pursuing cutting-edge research, this program gives you the tools, the freedom, and the confidence to get there.
10+2 with Science, including Mathematics/Statistics as one of the subjects, with a minimum aggregate of 50% marks.
Students with a CUET / SAT (DI Code - 71057) score will be given preference in the merit list.
01
Critical thinking and analytical reasoning: Acquire ability of critical thinking and logical reasoning and capability of recognizing and distinguishing the various aspects of real life problems.
02
Conduct investigations of complex research problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
03
Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern scientific and IT tools including prediction and modelling to complex scientific activities with an understanding of the limitations.
04
Communication: Communicate effectively on scientific activities with the community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
05
Environment and sustainability: Understand the impact of the professional scientific solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
06
Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the scientific practice.
07
Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
08
Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Statistical and Mathematical Knowledge: Demonstrate in-depth knowledge of statistical theories, data analysis techniques, and computational tools essential for extracting meaningful insights from data, and apply this knowledge effectively across diverse real-world domains.
Data Science and Computational Skills: Apply the knowledge of data science tools and programming languages (e.g., R, Python, SQL) to manage, analyse, and visualize large datasets using techniques such as machine learning, data mining, and statistical modeling.
Real-World Applications: Formulate real-world problems into statistical and data-driven models, and design appropriate analytical approaches, algorithms, or computational solutions to interpret data and support informed decision-making.