Data Science

Vision

To achieve academic excellence in domains of Data Science, Machine Learning, and Artificial Intelligence to produce professionally competent and socially sensitive engineers capable of working in global environment.

Mission

  • M1: To equip students with practical competency through emerging technologies and open source platforms related to area of Data Science, Machine Learning, and Artificial Intelligence.
  • M2: To equip students with interdisciplinary skill sets this provides dynamic and promising careers in global market place.
  • M3: To identify, analyse, and utilize professional and academic literature in fields of Data Science, Machine Learning, and Artificial Intelligence which helps solve problems and stay up to date with rapidly changing context of global technological concerns.

Program Educational Objectives (PEOs)

  • PEO1: Graduates will have successful careers in Computer Science & Engineering (Data Science) and related fields through strong technical fundamentals.
  • PEO2: Graduates will pursue higher education and research in advanced areas of Data Science, Machine Learning, and Artificial Intelligence.
  • PEO3: Graduates will demonstrate professional competence, ethical values, and leadership skills in their workplace.
  • PEO4: Graduates will engage in lifelong learning and adapt to evolving technological advancements.

Program Outcomes (POs)

  • PO1: Engineering Knowledge: Apply knowledge of mathematics, science, engineering fundamentals, and engineering specialization to solve complex engineering problems.
  • PO2: Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions.
  • PO3: Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet specified needs.
  • PO4: Conduct Investigations of Complex Problems: Use research-based knowledge and research methods to provide valid conclusions.
  • PO5: Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering tools.
  • PO6: The Engineer and Society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal, and cultural issues.
  • PO7: Environment and Sustainability: Understand impact of engineering solutions in societal and environmental contexts.
  • PO8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities.
  • PO9: Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams.
  • PO10: Communication: Communicate effectively on complex engineering activities with engineering community and society.
  • PO11: Project Management and Finance: Demonstrate knowledge and understanding of engineering and management principles.
  • PO12: Life-long Learning: Recognize the need for and have preparation and ability to engage in independent and life-long learning.

Program Specific Outcomes (PSOs)

  • PSO1: Ability to apply knowledge of data science protocols, machine learning algorithms, and statistical methods for developing data-driven solutions.
  • PSO2: Proficiency in implementing machine learning models and artificial intelligence techniques for data analysis and prediction systems.
  • PSO3: Capability to design and develop end-to-end data science solutions with integrated machine learning and artificial intelligence features.

MoUs

  • Industry Collaboration: MoUs with leading IT companies for internships and placements.
  • Research Partnerships: Collaboration with research institutions for cutting-edge projects.
  • Technology Transfer: Partnerships for technology transfer and innovation.
  • Academic Exchange: MoUs with international universities for student exchange programs.

Projects

  • Data Analytics Platform: Real-time data analysis and visualization systems for business intelligence.
  • Machine Learning Solutions: Predictive modeling and classification systems for various applications.
  • AI-Powered Applications: Intelligent systems using artificial intelligence and deep learning.
  • Data Science Tools: Statistical analysis and data processing tools for research.

Working Prototypes

  • Smart Data Analytics System: Automated data processing and insight generation system.
  • ML Model Deployment: Production-ready machine learning models for real-world applications.
  • Data Visualization Dashboard: Interactive dashboard for data exploration and reporting.
  • Predictive Analytics Tool: Forecasting system for business and research applications.

Faculty Publications

  • International Journals: Publications in IEEE, Springer, and Scopus indexed journals.
  • Conference Papers: Research presented at national and international conferences.
  • Patents Filed: Intellectual property contributions in data science and machine learning.
  • Book Chapters: Contributions to edited books in emerging technologies.

Expert Lectures

  • Industry Experts: Regular lectures by industry professionals from leading tech companies.
  • Research Scholars: Talks by renowned researchers in data science and machine learning.
  • Startup Founders: Sessions by successful entrepreneurs in AI and data science.
  • Government Officials: Insights from policy makers on data governance and technology regulations.