TOPICS TO BE COVERED

Motivation and case studies; 

Discrete and Continuous Probability Distributions;

R programming and Python programming with relevant hands-on sessions

Testing of Hypotheses: Parametric and Non-parametric; ANOVA; 

Linear, Multiple and Logistic Regression

Time Series Analysis: ARIMA and ARCH Models

Multivariate Statistical Methods;

Data Foundations;

Tree-Based Models for Credit Scoring and CatBoost; Model Evaluation in diverse applications;

 Class-Imbalanced Learning for Fraud Detection.

Clustering and Customer Analytics; 

Time Series Modeling; Advanced Techniques in Fraud Detection and Risk Forecasting;

Deep Learning in diverse fields;

NLP for Machine Intelligence for diverse industries with LLMs;

Explainability, Ethics, and Deployment.