1. Name and Academic Designation

Dr. B. N. Padmaja Priyadarshini Academician, Statistician, Researcher, Corporate Trainer and Entrepreneur

2. VIIM Resource Category

• Statistics, Data Analysis and Quantitative Modelling

• Mathematical Modelling

• Research Methodology and Research Mentoring

• Applied Mathematics and Interdisciplinary Applications

3. Profile

Dr. B. N. Padmaja Priyadarshini is an academician, statistician, entrepreneur, author, researcher, corporate trainer, and independent director with over 25 years of multidisciplinary professional experience spanning higher education, banking, entrepreneurship, research consulting, and corporate governance. She holds a Ph.D. in Women Entrepreneurship, an M.Phil. in International Business, an MBA in Operations, and an M.Sc. in Statistics, in which she secured university second rank. Her expertise includes statistical analysis, business analytics, statistical modelling, research methodology, and decision sciences. She has served as Associate Professor in Management Studies, works as a part-time biostatistician for medical colleges, and provides statistical consulting to international universities. Her experience includes research design, data analysis, statistical software training, research publication support, and mentoring students and researchers.

4. Areas of Contribution to VIIM

• Statistics and Quantitative Analysis: Workshops on statistical concepts, data interpretation, and quantitative reasoning.

• Statistical Modelling: Applying statistical methods to real-world problems in education, business, healthcare, and social sciences.

• Research Methodology: Training in research design, problem formulation, sampling, data collection, and analysis.

• Statistical Software: Hands-on training using SPSS, Jamovi, SmartPLS, AMOS, and other relevant analytical tools.

• Biostatistics and Healthcare Data: Introducing statistical methods for medical research, health-related datasets, and evidence-based interpretation.

• Business Analytics and Decision Sciences: Applying quantitative methods to business problems, operational decisions, and performance analysis.

• Mathematical Modelling: Helping learners translate practical questions into quantitative models, analyse results, and interpret their implications.

• Research Mentoring: Supporting UG/PG projects and research scholars in statistical analysis, interpretation, and research writing.

• AI for Research: Training participants to use AI tools responsibly for research support while independently checking statistical methods, assumptions, and conclusions.

• Faculty Development: Conducting workshops on research methodology, statistical analysis, and data-driven approaches to teaching and research.