Course information

This course provides practical training in applying advanced statistical methods using R and Python, integrating the concepts learned in Design and Analysis of Experiments, Time Series Analysis, and Multivariate Analysis and Statistical Techniques for Data Mining. Students will develop computational skills for implementing statistical models, analysing real-world datasets, interpreting results, and communicating statistical findings effectively. The course emphasizes the practical application of experimental designs such as CRD, RBD, Latin square, incomplete block, factorial, and split-plot designs, along with analysis of variance and covariance. It also covers the implementation and interpretation of time series models and forecasting techniques, including smoothing methods and ARIMA models. Multivariate applications include principal component analysis, factor analysis, discriminant analysis, cluster analysis, canonical correlation, and MANOVA. Through hands-on exercises and data-driven projects, students will learn to select appropriate statistical techniques, perform analyses using R and Python, validate model assumptions, visualize results, and draw meaningful conclusions from complex datasets.