About the Programme
Comprehensive Curriculum
The programme offers a strong interdisciplinary curriculum integrating Data Science, Statistics, Mathematics. Students develop a solid foundation in statistical methods, data analytics, machine learning, data visualization, database management, predictive modelling. The curriculum is designed to equip students with both analytical and computational skills required for data-driven decision-making. The programme provides an excellent foundation for higher education
Hands-on and Application-Oriented Learning
The programme emphasizes experiential and project-based learning through practical laboratory sessions, coding exercises, statistical computing, industry-oriented projects, and case studies. Students gain proficiency in programming languages, statistical software, data visualization tools, and machine learning frameworks while working with real-world datasets from diverse domains such as healthcare, finance, marketing, and social sciences.
Research-Focused Education
The programme fosters a strong research culture by introducing students to research methodology, statistical investigation, data-driven problem solving, and advanced analytical techniques. Students are encouraged to participate in research projects, paper presentations, workshops, seminars, and innovation-driven activities that enhance critical thinking, analytical reasoning, and scientific inquiry.
Industry Exposure and Career Opportunities
Students gain exposure to contemporary industry practices through expert lectures, internships, industry projects, workshops, hackathons, and interactions with professionals from academia and industry. The programme prepares graduates for diverse career opportunities in analytics, artificial intelligence, research, and business domains. Graduates can pursue roles such as Data Scientist, Data Analyst, Statistical Analyst, Business Intelligence Analyst, Machine Learning Engineer, Data Engineer, Quantitative Analyst, Research Associate, Market Research Analyst, and AI Analyst across industries including technology, healthcare, finance, consulting, retail, and government sectors.
Why Choose This Programme?
Interdisciplinary Learning
The programme combines statistics, data science, mathematics, and analytics to provide a strong multidisciplinary foundation.
Advanced Analytics & AI Skills
Students gain practical exposure to machine learning, Neural networks, artificial intelligence, predictive analytics, and data-driven technologies.
Strong Statistical Foundation
Develop expertise in statistical modelling, probability, and data interpretation for research and business applications.
Industry Exposure & Internships
Internships, industry projects, workshops, and expert sessions help students understand real-world applications of data science.
Research-oriented Curriculum
The programme promotes innovation, research projects, and analytical thinking through experiential learning opportunities.
Diverse Career Opportunities
Graduates can pursue careers in data analytics, business intelligence, finance, healthcare analytics, research, and technology sectors.
Choose Your Track
Data Science
Data Analysis Data Analyst Junior Data Analyst Business Intelligence (BI) Analyst Data Visualization Analyst Statistical Analyst Market Research Analyst Financial Data Analyst
Data Scientist Machine Learning Engineer Artificial Intelligence (AI) Engineer AI Solutions Developer MLOps Engineer
postgraduate studies in Data Science Postgraduate in Statistics Postgraduate in Artificial Intelligence
What You Will Learn
Programme Structure
The programme structure outlines the academic curriculum designed to provide a systematic progression of learning through core subjects, electives, and practical components across semesters, ensuring both theoretical understanding and skill development.
Semester 1 — Foundations of Data Science, Mathematics and Programming
Semester 2 — Computational Thinking and Mathematical Foundations
Semester 3 — Data Management, Artificial Intelligence and Statistical Inference
Semester 4 — Machine Learning and Computational Mathematics
Semester 5 — Advanced AI, Analytics and Mathematical Modelling
Semester 6 — Applied Artificial Intelligence, Research and Professional Practice
Semester 7 — Intelligent Systems, Language Models and Advanced AI
Semester 8 — Next-Generation AI, MLOps and Emerging Technologies
Eligibility & Fee Structure
Career Paths
Your Career Roadmap
Select a specialisation track to explore the learning journey and career outcomes tailored to your chosen domain.
Sem 1&2: Junior Data Analyst, Data Associate, Reporting Analyst, Data Processing Executive, Business Data Assistant
Sem 3&4: Data Engineer, QA Tester, Data Visualization Analyst, ETL Developer, Business Intelligence Associate
Sem 5&6: Full Stack Data Analyst, Applied Analyst, Predictive Analytics Associate, Machine Learning Analyst, Decision Support Analyst
Sem 7&8: Explainable AI Analyst, NLP Analyst, AI Governance Analyst, Security Analytics Associate, AI Solutions Consultant, LLM Application Developer
Sem 1&2: Junior Data Scientist, AI Associate, Data Science Intern, Junior Analytics Associate, AI Support Engineer
Sem 3&4: Machine Learning Engineer, Full Stack Data Scientist, AI Developer, Computer Vision Associate, Data Science Engineer
Sem 5&6: Applied Scientist, Research Scientist, Deep Learning Engineer, AI Solutions Engineer, Predictive Modeling Specialist
Sem 7&8: NLP Engineer, Explainable AI Specialist, AI Security Engineer, Generative AI Engineer, LLM Engineer, AI Product Engineer
Research Foundations, Scientific Inquiry, Literature Survey, Academic Project Assistant, Research Methodology Trainee
Undergraduate Researcher, Statistical Analyst Intern, Data Science Research Intern, Research Assistant, Academic Research Associate
M.Sc./M.Tech. Entrance Preparation, Junior Research Fellow (JRF), Research Associate, Industry R&D Trainee, Ph.D. Aspirant
AI Research Assistant, NLP Research Associate, Explainable AI Researcher, Generative AI Research Associate, Responsible AI Researcher, Doctoral Research Trainee
Message from the HOD
Bangalore Central Campus
Welcome to the Department of Statistics and Data Science at CHRIST (Deemed to be University), Bangalore
The Department of Statistics and Data Science is a vibrant centre for learning, innovation, and research, reflecting the university’s vision of “Excellence and Service.” The department fosters an intellectually stimulating environment that nurtures analytical minds, encourages inquiry, and inspires innovation through data-driven thinking.
The disciplines of Statistics and Data Science converge here to create a powerful synergy between analytical reasoning and technological advancement. Statistics provides the foundation for understanding uncertainty, drawing inferences, and ensuring precision, while Data Science ext
Message from the HOD
Welcome to the Department of Statistics and Data Science at CHRIST (Deemed to be University), Bangalore
The Department of Statistics and Data Science is a vibrant centre for learning, innovation, and research, reflecting the university’s vision of “Excellence and Service.” The department fosters an intellectually stimulating environment that nurtures analytical minds, encourages inquiry, and inspires innovation through data-driven thinking.
The disciplines of Statistics and Data Science converge here to create a powerful synergy between analytical reasoning and technological advancement. Statistics provides the foundation for understanding uncertainty, drawing inferences, and ensuring precision, while Data Science extends this base through computational techniques, machine learning, and modern analytics. Together, they empower learners to transform data into knowledge and knowledge into impactful decisions.
Driven by a spirit of research and innovation, the department offers a range of programmes designed to meet emerging global and industry needs. These include an industry-integrated Postgraduate Programme, as well as a Dual Degree Postgraduate Programme offered in partnership with Steinberg University, which provides students with valuable international exposure and a global perspective in data-centric education. The curriculum integrates statistical reasoning with advanced analytical tools, enabling learners to interpret complex data, design intelligent models, and communicate insights effectively.
Through rigorous academics, hands-on projects, and a strong research culture, the department ensures that students develop both conceptual depth and practical expertise. The learning environment encourages creativity, collaboration, and critical thinking — essential for addressing contemporary challenges in an increasingly data-driven world. The department takes pride in preparing students to become innovative problem-solvers and responsible professionals who contribute meaningfully to global progress. Each learner who joins this journey becomes part of a community that values knowledge, ethics, and purposeful engagement.
Admission Process
- Register with your Email ID
- Login to the Admission Portal
- Fill the Application Form
- Pay Application Fee
- Entrance Test (If Applicable)
- Assessment
- Interview
- Check login page for result
- If selected, Offer Letter attached
- Pay Course Fee Online
- Complete Admission Process
Ready to Apply?
Applications for the 2026 batch are open. Deadline: 04-May-2026.