Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of R and Python programming, as well as the fundamentals of statistics. This includes writing R and Python commands for data management, basic statistical analysis, performing descriptive statistics and presenting data using appropriate graphs and diagrams. This unit serves as a foundation for advanced analytics.

In the statistical inference unit you will gain an in-depth understanding of statistical distribution and hypothesis testing. This includes Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square distributions, as well as parametric and non-parametric tests used in research problems. The unit will help you to formulate research hypotheses, select appropriate tests for them, write R and Python programs to perform hypothesis testing and draw inferences using the outputs generated.

A good understanding of predictive modelling is an essential part of being an effective data scientist as many business problems are related to successfully predicting future outcomes. This fundamentals of predictive modelling unit provides a strong foundation for predictive modelling and covers the entire modelling process in the context of real life case studies. Many concepts in predictive modelling methods are commonly used in business and therefore these concepts will be discussed in detail.