Multivariate Analysis (ST-C-301)

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest.

Design and Analysis of experiments (ST-C-302 )

Planning an experiment to obtain appropriate data and drawing inference out of the data with respect to any problem under investigation is known as design and analysis of experiments. This might range anywhere from the formulations of the objectives of the experiment in clear terms to the final stage of the drafting reports incorporating the important findings of the enquiry. 


Applied Stochastic Processes (ST-CE-303)

Applied Stochastic Processes is a collection of papers that cover stochastic processes, stochastic equations, and their applications in many fields of science. Stochastic processes are a mathematical model of systems and phenomena that appear to vary in a random manner.

Decision Theory & Bayesian Inference (ST-CE-303)

Bayesian decision theory provides a unified and intuitively appealing approach to drawing inferences from observations and making rational, informed decisions. Bayesians view statistical inference as a problem in belief dynamics, of using evidence about a phenomenon to revise and update knowledge about it.

Official Statistics (ST-AE-304)

Official statistics are the data collected by government departments in the course of their work (i.e. routine statistics) or collected specifically for statistical purposes. 'Unofficial' statistics are from independent organisations.

Demography (ST-AE-304)


Statistical Computing-III: Data Analysis using PYTHON (ST-C-305)

Data Analysis is the technique of collecting, transforming, and organizing data to make future predictions and informed data-driven decisions. It also helps to find possible solutions for a business problem.