M.Sc DS – Master of Science in Data Science
Transforming Data into Knowledge, Insight, and Impact
Why M.Sc DS?
The Department of Data Science (PG) is dedicated to creating a strong academic and research-oriented environment in the field of data science. The department promotes interdisciplinary learning, innovation, and ethical data practices. Through academic activities, industry interaction, and research initiatives, the department aims to develop competent professionals and researchers who can contribute effectively to academia, industry, and society.
About M.Sc DS
The PG Department of Data Science fosters academic excellence, research, innovation, and ethical data practices to develop skilled professionals and researchers for industry, academia, and society.
Program Overview
The M.Sc. Data Science program is designed to equip students with comprehensive knowledge of data analysis, statistics, machine learning, artificial intelligence, and computational techniques. The curriculum emphasizes practical learning through laboratories,......
Key Focus Areas
Data-Centric Curriculum
Emphasis on end-to-end data handling, from data acquisition and preprocessing to insight generation and interpretation.
Computational Proficiency
Hands-on training in programming, algorithms, and data-driven computational techniques using modern platforms.
Research & Innovation
Encouragement of exploratory research, project-based learning, and innovation in emerging data science domains.
Decision-Oriented Analytics
Training students to transform analytical results into meaningful insights for strategic and informed decision-making.
Our Faculty
More Faculty »Frequently Asked Questions
Find answers to common questions about our M.Sc DS program
To be eligible for the M.Sc. Data Science program, candidates must have completed Bachelor”™s degree in Computer Science stream / Mathematics / Statistics / Electronics and secured a minimum of 60% marks in aggregate.
The M.Sc. Data Science is a two-year postgraduate program structured into four semesters. Each semester is organized with 4–5 months of teaching and learning activities, followed by examinations. The fourth semester includes a major project, enabling students to apply their knowledge to real-world data science problems.
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Engineer
- Big Data Analyst
- Business Intelligence (BI) Developer
- Opportunities in IT services, finance, healthcare, e-commerce, education, and research organizations
- Careers in startups and government agencies
- Higher studies, research, and doctoral programs in Data Science and related fields
Program Features
Strong focus on Machine Learning, AI, Big Data, and Analytics
Hands-on training through projects, case studies, and internships
Value Added Courses (VAC) for enhanced employability
Active research culture encouraging publications and project work
Expert talks, workshops, and industry interaction sessions
Placement-oriented training with aptitude, coding, and soft skills support
University Curriculum
The M.Sc. Data Science program is structured in accordance with the academic regulations and syllabus prescribed by Bharathiar University, ensuring systematic and outcome-oriented learning.
Industry Integration
To meet evolving industry requirements and enhance practical competence, the department integrates Value-Added Courses (VACs) with expert lectures, hands-on workshops, industry-oriented projects, internships, and exposure to real-world datasets.
These initiatives bridge the gap between theory and practice, enabling students to apply classroom knowledge to real-world problems and improve career readiness.
Specialized Electives
Students can choose electives across different semesters based on their interests and career goals.
Elective I
- Design and Analysis of Algorithms
- Business Intelligence
- IoT Analytics
Elective II
- Web Analytics
- Natural Language Processing
- Sentiment Analysis
Elective III
- Social Media Analytics
- Cloud Analytics
- Digital Marketing Analytics
Project Structure
Mini Project (Semester III): Students work on real-world data problems involving data preprocessing, visualization, and model building, strengthening analytical thinking and hands-on implementation skills.
Major Project & Viva-Voce (Semester IV): The final semester is dedicated to an extensive project where students design, develop, and evaluate a complete data science solution in domains such as healthcare analytics, finance, smart cities, social media analysis, or business intelligence.
Value-Added Courses (VACs)
To enhance industry readiness beyond the core curriculum, the department offers Value Added Courses (VACs):
- Data Analytics using Advanced Excel
- Data Visualization using Power BI and Tableau
- Generative AI and Prompt Engineering
Technical Skills Development
Students gain proficiency in the following areas:
Programming Skills: Python, R, SQL
Data Handling & Analysis: Data cleaning, preprocessing, and exploratory data analysis
Machine Learning & AI: Supervised and unsupervised learning and deep learning techniques
Big Data Technologies: Hadoop, Spark, Hive, HBase
Data Visualization: Tableau, Matplotlib, Seaborn, Power BI
Cloud & Web Analytics: Cloud analytics, web, and social media data analysis
These skills are developed through core courses, laboratory sessions, mini projects, and major project work.
The Institution provides structured placement assistance to M.Sc. Data Science students through a dedicated placement cell. The support includes pre-placement training, skill development programmes, résumé preparation, mock interviews, and campus recruitment activities.
Department Events
Academic Collaborations
