Quantitative analysis is essential for researchers as it provides a systematic and rigorous approach to studying phenomena, generating empirical evidence, and advancing knowledge across various disciplines. By employing quantitative methods, researchers can make meaningful contributions to their fields, address pressing societal issues, and drive innovation and progress. Quantitative analysis allows researchers to measure phenomena objectively using numerical data. This reduces bias and subjectivity in the research process, leading to more reliable and valid results. Also, quantitative analysis enables researchers to draw statistically valid inferences about populations based on sample data. By employing statistical techniques such as hypothesis testing and confidence intervals, researchers can make generalizations and conclusions with a known level of confidence. The aim of this Faculty Development Program (FDP) is to equip participants with advanced skills and knowledge in data analysis and modeling techniques, specifically focusing on Partial Least Squares Structural Equation Modeling (PLS-SEM), Artificial Neural Networks (ANN), Fuzzy-set Qualitative Comparative Analysis (FsQCA), and Sentiment Analysis. This will empower the participants with the confidence to leverage advanced data analysis and modeling techniques in their academic and professional pursuits, ultimately contributing to advancements in their respective fields.