Data Science is an interdisciplinary field that utilises techniques from statistics, computer science, and mathematics, in conjunction with domain-specific knowledge, to extract knowledge and insights from structured and unstructured data, enabling informed decision-making. Data Science can be applied in a wide range of fields, including finance, business, computational sciences, social sciences, humanities, and political science. Data Science is relevant today because we live in a data-driven world where data is ubiquitous. More notably, in the past decade, this explosion of data, coupled with the availability of affordable general-purpose tools and algorithms, has opened up opportunities for the development of specialised data analytics software and services across several domains.
The undergraduate programme in Data Science is designed to be a rigorous, interdisciplinary programme. The programme builds on the mathematical foundations of computing, complemented by hands-on experience through project work mentored by faculty to address some of the key problems in the field of artificial intelligence (AI), computational and data sciences with applications in computational sciences, computer vision, digital agriculture, econometrics, e-commerce, environmental studies, finance, medical imaging, supply chain management, etc.
The Data Science programme at Krea is designed to be rigorous and interdisciplinary. The core courses have been designed to impart the necessary programming, computational, mathematical, and data analytical skills to students. The elective courses provide the necessary domain knowledge, enabling students to apply the theoretical and practical knowledge learned through the required courses in a diverse range of domains. Additionally, the undergraduate programme in Data Science incorporates cutting-edge tools and techniques employed by the industry. The courses are primarily project-based and provide students with opportunities to gain hands-on experience by analysing real-world data sets using various sophisticated tools for modeling, optimisation, data mining, and visualisation. Lastly, students are trained to conceive data through multiple branches of the sciences and humanities, which will play a vital role in approaching various interdisciplinary problems.
One of the distinguishing aspects of our interdisciplinary programme is the opportunity for our students to work with faculty and domain experts to build on the solid analytical and conceptual foundations of computer science, statistics, and mathematics, solving real-world problems relevant to society. This is primarily driven by a research-based Capstone project in their final year, where students apply various artificial intelligence tools and techniques to solve some of the vital problems in computational sciences, business, social sciences and humanities.
Graduates of the BSc Data Science programme at Krea University will develop a rigorous interdisciplinary foundation that integrates statistics, mathematics, computer science, and domain-specific knowledge to solve complex, real-world problems. Students will acquire the ability to collect, wrangle, and analyse structured and unstructured data using modern computational tools, while understanding the underlying mathematical and statistical principles that guide responsible data-driven decision-making.
Students will demonstrate proficiency in programming, algorithmic thinking, and computational modelling, enabling them to design and implement analytical workflows that span data acquisition, exploratory analysis, predictive modelling, optimisation, and visualisation. Through project-based learning, they will gain hands-on experience with industry-relevant tools and techniques used in artificial intelligence, machine learning, data mining, and large-scale data processing. They will learn to evaluate models rigorously, recognise uncertainty and bias, and apply techniques that ensure fairness, transparency, and ethical integrity in data-centric applications.
A distinguishing feature of the programme is its emphasis on approaching data problems through multiple disciplinary lenses, including the sciences, social sciences, humanities, business, and policy. Students will develop the ability to translate domain questions into formal data science problems and communicate insights effectively to diverse stakeholders through visual, written, and oral formats.
Graduates will be equipped to undertake research-driven inquiry, culminating in a capstone project where they engage deeply with a specialised problem in AI, finance, econometrics, healthcare, social science analytics, or other emerging fields. They will be prepared for careers in analytics, machine learning, data engineering, consulting, and research, or to pursue advanced studies in data science and allied disciplines.
This course is evaluated through a combination of the following components
Data Science graduation requirements for the three-year and four-year degree programmes:
|
Credits needed to earn a Single Major in Data Science |
Credits needed to earn a Double Major in Data Science |
Credits needed to earn a Minor in Data Science |
Credits needed to earn a Concentration in Data Science |
|
| 3-Year Programme | 60 | 48 | 24 | 16 |
| 4-Year Programme | 80 | 64 | 32 | 16 |
Data Science graduation requirements for the three-year and four-year degree programmes:
| Single Major | Double Major | Minor | Concentration | |||||
| Required | Elective | Required | Elective | Required | Elective | Required | Elective | |
| 3-Year Programme | 44 | 16 | 44 | 4 | 24 | 0 | 16 | 00 |
| 4-Year Programme | 44 | 36 | 44 | 20 | 28 | 4 | 16 | 00 |
To earn a Data Science Major, Minor, or Concentration, students must complete the required and elective credits in Data Science courses as indicated above.
The Data Science Lab provides GPU-enabled workstations for hands-on machine learning in required and elective courses. Students explore data, build models, and run experiments using JupyterLab/Anaconda Navigator with popular Python and R libraries (PyTorch, scikit-learn, etc.). For workloads that require more power, our on-campus HPC cluster offers high-performance computing and GPU acceleration, ideal for deep learning training and other computationally intensive tasks. The cluster is accessible both on campus and remotely via a secure VPN, allowing students to launch longer jobs, monitor progress, and retrieve results from anywhere. Together, the Lab and HPC let students turn classroom ideas into large-scale results with speed and confidence.
The Data Science graduate will be prepared by the programme to analyse and interpret data through the sciences and humanities, enabling them to address complex interdisciplinary problems across research, industry, and policy.
Partnerships
Krea University has partnerships with leading universities in India and abroad, offering students pathways for higher education and research. These collaborations create opportunities for postgraduate study, academic exchange, and continued learning across disciplines. Know more
Higher Education Pathways: MSc/PhD in Data Science, Artificial Intelligence, Machine Learning, Generative AI, Business Analytics, Big Data Analytics, Computational Statistics, Data Engineering, Cybersecurity and Data Privacy, MBA (with Data/Analytics or Tech specialisation), and more.
Job roles: Statistician, Data Analyst, Data Scientist, Data Engineer, Machine learning engineer and more.
Discover how Dheer Panjwani’s selected his Data Science major, what makes this discipline unique, the key lessons he learned, his future aspirations, and his words of wisdom for freshers.
“During the summer break of 24 – 25, I worked as a Data Science Intern at Coreflex Solutions. It was a great opportunity not only to apply my knowledge in Data Science, but also to work alongside industry experts to understand the current real-world needs in the job market. My tenure was 2 months where I worked on a few projects involving data manipulation and AI. The courses I had done previously at Krea gave me a very good base to use when dealing with more complex topics in Data Science and similar domains. The internship pushed me to manage my time well and communicate effectively to work smoothly with a team.”