You will extensively study statistical data distribution, parametric and non-parametric tests, and hypothesis testing. The unit will equip students with the necessary skills to chalk out a research plan including, hypothesis formation, identifying applicable tests, writing codes in both languages for hypothesis testing, and eventually concluding. You will also gain an in-depth understanding of the statistical data distribution and hypothesis testing.

Students will learn about the modeling process in the context of real-life scenarios. So many challenges faced in business relate to the ability to forecast future developments, putting predictive modeling at the core of what it means to be a data scientist. For this reason, the unit will delve into several concepts in considerable detail.

Students will learn about category-dependent variable modeling in this unit. Marketing, clinical research, and several other domains often run into binary dependent variables for predictive modeling. Therefore, this unit will help students gain in-depth awareness of binary, ordinal, and multinomial modeling.

Students will learn about the time series predictive methodologies in this section. They will try their hands at time series regression, exponential smoothing, explore predictive modeling for macroeconomic parameters like inflation, and make attempts at intricate mathematical models.

This unit will teach students various data manipulation techniques such as data reduction and segmentation, besides learning analysis of massive data sets using clustering techniques. Using these techniques with the appropriate care tends to yield meaningful, valuable business insights.