Workshop on Research in Data Science

The Department of Statistics and Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, organised a workshop on “Research in Data Science” on 28 August 2026 at the City Campus. The workshop was designed for MSc Data Science students to help them develop a systematic and practical understanding of research methodology in the context of Data Science. The session was conducted by Dr. Vahida Z. Attar, Associate Professor and Dr. Pramod Chaudhari Endowed Chair Associate Professor in AI/ML in the Department of Computer Science and Engineering at COEP Technological University, Pune. With more than 26 years of academic experience, Dr. Attar has served in significant leadership roles, including Dean of the School of Computational Sciences and Head of the Department of Computer Science and Engineering. She is a recognised PhD guide and has extensive experience in research, teaching and academic mentoring. The workshop began by highlighting that meaningful Data Science research does not start with selecting an algorithm or downloading a dataset. Instead, it begins with identifying a relevant problem and clearly understanding why it needs to be studied. Students were introduced to the process of moving from a broad area of interest to a specific and researchable question. The importance of conducting a careful literature review was also discussed, particularly in understanding existing contributions, identifying limitations and recognising genuine

research gaps. The session covered the formulation of research objectives, selection of suitable datasets and identification of appropriate research methodologies. Dr. Attar explained the importance of carefully designed experiments and meaningful baselines in evaluating Data Science and machine-learning models. Students were encouraged to look beyond model accuracy and consider whether the chosen methods, evaluation measures and conclusions were appropriate for the research problem. Important methodological concerns such as data leakage, bias, inappropriate evaluation metrics and overinterpretation of results were discussed. The session also emphasised reproducibility, research integrity and ethical academic practice. These aspects are especially important in Data Science, where the quality of the data, transparency of the
process and interpretation of results can significantly influence the reliability of a study. A particularly engaging aspect of the workshop was the use of case studies involving organisations such as Netflix and Uber. These examples demonstrated how Data Science is used to understand user behaviour, improve services, support decisions and address complex operational challenges. The case studies helped students connect the theoretical stages of research with applications they encounter in everyday life. They also illustrated how real-world Data Science projects require a clear problem definition, appropriate data, careful modelling and meaningful interpretation. The workshop further addressed academic writing, presentation of research findings and the responsible use of artificial intelligence tools. Students were encouraged to use AI tools as aids for learning and research while maintaining originality, critical thinking, transparency and academic integrity. Emerging developments and potential research areas in Data Science were also discussed, enabling students to consider relevant directions for their academic projects and future research. Overall, the workshop provided students with a clearer understanding of how Data Science research progresses from an initial idea to a well-designed study and a meaningful outcome. The combination of methodological guidance, ethical considerations and industry-oriented case studies made the programme relevant to both their academic development and future professional careers.