Empowering Future Innovators Through Data-Driven Intelligence
The Department of CSE (Data Science) at VVIT was established in 2024 with an intake of 60 students. The program combines Computer Science fundamentals with Data Science, AI, Machine Learning, Big Data, and Cloud Computing to prepare students for modern industry demands.
Download BrochureEstablished
Program in CSE (Data Science)
Approved Student Intake
Oriented Curriculum
Modern Labs & Tools
Associate Professor & HOD
"The department focuses on developing future-ready professionals with expertise in Data Science, Artificial Intelligence, and emerging technologies through academic excellence, practical learning, research, and industry collaboration."— Dr. Sharada P N, Associate Professor & HOD
To emerge as a center of excellence in Computer Science and Engineering (Data Science) through quality education, research, and data-driven innovation.
Provide comprehensive learning in Data Science with ethical values
Enhance industry readiness through collaborations and practical exposure
Promote research, innovation, and entrepreneurship
Develop solutions for real-world data-centric challenges
Apply knowledge of mathematics, natural science, computing, engineering fundamentals and an engineering specialization to develop solutions to complex engineering problems.
Identify, formulate, review research literature and analyze complex engineering problems reaching substantiated conclusions with consideration for sustainable development.
Design creative solutions for complex engineering problems and design/develop systems to meet identified needs with consideration for public health, safety, and environment.
Conduct investigations of complex engineering problems using research-based knowledge including design of experiments, modelling, analysis & interpretation of data.
Create, select and apply appropriate techniques, resources and modern engineering & IT tools, recognizing their limitations to solve complex engineering problems.
Analyze and evaluate societal and environmental aspects while solving complex engineering problems for its impact on sustainability.
Apply ethical principles and commit to professional ethics, human values, diversity and inclusion; adhere to national & international laws.
Function effectively as an individual, and as a member or leader in diverse/multi-disciplinary teams.
Communicate effectively within the engineering community and society at large, including effective reports, design documentation, and presentations.
Apply knowledge of engineering management principles and economic decision-making to manage projects in multidisciplinary environments.
Recognize the need for independent and life-long learning, adaptability to new technologies, and critical thinking in the context of technological change.
A systematic, theory-based understanding of the natural sciences applicable to the discipline and awareness of relevant social sciences.
Conceptually-based mathematics, numerical analysis, data analysis, statistics and formal aspects of computer and information science to support detailed analysis and modelling.
A systematic, theory-based formulation of engineering fundamentals required in the engineering discipline.
Engineering specialist knowledge that provides theoretical frameworks and bodies of knowledge for the accepted practice areas in the engineering discipline.
Knowledge supporting engineering design and operations in a practice area, including efficient resource use, environmental impacts, and net zero carbon.
Knowledge of engineering practice (technology) in the practice areas in the engineering discipline.
Knowledge of the role of engineering in society and identified issues, such as professional responsibility to public safety and sustainable development.
Engagement with current research literature of the discipline, awareness of critical thinking and creative approaches to evaluate emerging issues.
Ethics, inclusive behavior and conduct. Knowledge of professional ethics, responsibilities, and norms of engineering practice with diversity awareness.
Graduates will excel in careers related to Artificial Intelligence and Machine Learning, demonstrating strong technical skills, innovation, and adaptability to evolving technologies.
Graduates will engage in continuous learning and professional development to stay at the forefront of advancements in AI and ML, contributing to organizational growth.
Graduates will apply knowledge in AI and ML to develop solutions addressing real-world problems, ensuring ethical practices and societal well-being.
Graduates will exhibit leadership qualities and work effectively in multidisciplinary teams, contributing to the successful execution of innovative projects.
Graduates will design, develop, and implement data-driven applications using data engineering, statistical modeling, machine learning, and visualization techniques to support decision-making.
Graduates will analyze and interpret complex datasets across various domains using statistical, ML, and data mining techniques to generate actionable insights.
Graduates will contribute to innovation and applied research in data science, promoting responsible AI, data ethics, and impactful societal applications.
Take the first step towards a successful career in engineering
Admission Support
Scholarship Available
Admission Process
