Demonstrate an understanding of the philosophy behind qualitative research  

Examine the range of techniques and methods for qualitative research, such as the basics of thematic analysis, NVivo and more            

Recognize challenges in publishing qualitative work and thereby ensuring robustness in design   

Learning the basic syntax – commands and operators, variable and data-types, file formats in STATA,

Understanding the commands for descriptive and exploratory analysis (describe, list, summarize, tabulate)            

Create and modify variables, handle missing values, merge files, learning graphics – scatter, box-plot, histogram, basics of STATA programming - loops, local and global variables

Analysing data using regression and hypothesis testing  

Approach complex industrial and business research problems and address them through a rigorous, methodologically sound design of experiments.    

Use modern software to plan experiments effectively.     

Analyze the resulting data of an experiment, and communicate the results effectively to decision-makers.

Foundation of social network concepts 

Methods and models used in network science

Relevance of the models in management research and industry 

Addressing the pressing issues in the research frontiers​

Overview of machine learning and deep learning algorithms, model selection and validation             

ML/DL Ops workflow, overview of teachable machines and generative AI 

Applications of ML/AI techniques in business and public policy

Introduction to Bibliometric analysis – performance analysis and science mapping

Overview of different metrics to rank authors, papers and journals

Leveraging Network Analysis – difference between directed and undirected networks, modeling a citation and a collaboration network.

Hands on session – demo on a curated dataset for bibliometric analysis using appropriate software (VOSViewer, Bibliometrix, SCIMAT)

Epilogue: Beyond performance analysis – getting advanced insights from data