It helps you gain knowledge on how to extract meaning from data, or in more simple terms – how to read statistics and make decisions based on data. This is undoubtedly a valuable skill set for everyday life and certainly for professionals aiming to grow as managers or entrepreneurs.

The course introduces techniques for visualising relationships on data and systematic techniques for understanding relationships using mathematics. This course can also equip prospective GMAT test takers with the valuable skills necessary for the Quantitative and the Integrated Reasoning sections.

This is a free course conducted in English and it is suitable for a wide international audience.

*“This course will cover visuali**sation, probability, regression and other topics that will help you learn the basic methods of understanding data with statistics.**”*

During the course, students will learn about:

- **Visualising relationships in data: **Seeing relationships in data and making predictions based on them; Simpson's paradox

- **Probability:** Probability; Bayes Rule; Correlation vs. Causation

- **Estimation:** Maximum Likelihood Estimation; Mean, Median, Mode; Standard Deviation, Variance

- **Outliers and Normal Distribution****:** Outliers, Quartiles; Binomial Distribution; Central Limit Theorem; Manipulating Normal Distribution

- **Inference:** Confidence intervals; Hypothesis Testing

- **Regression:** Linear regression; correlation

**About the course**

The course does not require any previous knowledge of statistics. However, basic familiarity with algebra such as knowing how to compute the mean, median and mode of a set of numbers will be helpful. Moreover, in order to complete the course, students will have a final exam.

**About the professor**

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

Start date: self-paced course

Length: Approx. 2 months (assumes 6 hours/week)

Professor: Sebastian Thrun

Platform: Udacity