The curriculum follows a modern, flexible learning approach that focuses on:

• Case-based learning to connect theory with real industry scenarios.

• Research-oriented study that sharpens analytical and investigative skills.

• Practical implementation through data tools, models, and simulations.

• Mentorship-driven learning with guidance from subject experts and industry professionals.

• Independent research projects to help learners build confidence in advanced data-driven decision-making.

The course material is structured to support both academic depth and industry relevance. Key modules typically include:

• Foundations of Data Science & Analytics

• Applied Statistics for Business Research

• AI, Machine Learning & Predictive Modeling

• Big Data Technologies & Digital Transformation

• Data Visualization & Business Intelligence

• Strategic Data-Driven Leadership

• Advanced Research Methodology

• Doctoral Research Proposal Development

• Final Dissertation / Industry-Driven Research Project

All learning resources include research papers, case studies, industry reports, datasets, hands-on tools, and interactive learning materials.

The DBA in Data Science follows a structured and research-focused evaluation system:

• Module-wise assignments to assess conceptual clarity and application.

• Case study analysis for practical understanding of real business data issues.

• Quizzes & data lab exercises to test analytical proficiency.

• Research proposal submission evaluated by faculty experts.

• Viva-voce and presentations for demonstrating practical and theoretical knowledge.

• Final doctoral dissertation based on original research or industry-based problem solving.

Assessment ensures that learners develop real-world expertise and the ability to apply data science in strategic business contexts.