Statistics is a versatile discipline that has revolutionized the fields of business, engineering, medicine and pure sciences. This course is Part 1 of a 4-part series on Business Statistics, and is ideal for learners who wish to enroll in business programs. The first two courses cover topics in Descriptive Statistics, whereas the next two courses focus on Inferential Statistics.

Spreadsheets containing real data from diverse areas such as economics, finance and HR drive much of our discussions.

In Part 1, the course will explore multiple ways to describe these datasets, numerically as well as visually. Throughout, it will embrace a problem-based approach to understanding the material: the primary reason to pick up a tool or a technique will be to solve a problem. The course makes judicious use of tools.

In Part 2, the course takes up a few datasets that have over a million rows, which makes it impossible to analyze using a spreadsheet. This is a natural setting for R, an advanced statistical programming platform. The courses incorporate helpful tutorials to get learners acquainted with both the mechanisms. Parts 3 and 4 are dedicated to Inferential Statistics. In Part 3, the course begins by exploring the benefits of random sampling, and apply the Central Limit Theorem to arrive at confidence intervals for important population parameters. We also learn how to formulate hypotheses for business data, and resolve them with the testing framework that we establish. Along the way, we shall compare two or more populations and draw inferences with a set of statistical tests.

You will learn all these concepts with the help of various demonstrations, which show real-life application of the concepts related to business situations.

What you will learn

By the end of this course, you will be able:

• To download data from prominent Internet sources;
• To analyse a dataset using spreadsheet software;
• To pose pertinent business questions of datasets and to answer them;
• To clean up a dataset and summarize the data using single point measures of centrality and dispersion;
• To classify variables by scale and aggregate them with pivot tables;
• To build an understanding of probability, joint and marginal probability, conditional probability;
• To apply Bayes rule to invert probabilities on a decision tree.