Course descriptionStatistics is the driving force in any quantitative career. It is the fundamental skill data scientists need to be able to understand and design statistical tests and analyses performed by modern software packages and programming languages. In this course, we start from the very basics of statistics and gradually build up your statistical thinking, enabling you to understand the more complex analyses carried out later in the program.
Descriptive Statistics Fundamentals
Here, you will learn how to distinguish between the different types of data and levels of measurement. This will help you when calculating the measures of central tendency (mean, median, and mode) and dispersion indicators such as variance and standard deviation, as well as measures of the relationship between variables like covariance and correlation. To reinforce what you have learned, we will wrap up this section with a hands-on practical example.
Here, you will learn how to calculate confidence intervals with known population and variance. We will introduce the Student T distribution, and you will learn how to work with smaller samples, as well as differences between two means (with dependent and independent samples). These tools are fundamental later on when we start applying each of these concepts to large datasets and use coding languages like Python and R. To reinforce what you have learned, we will wrap up this section with an easy-to-understand practical example.
In this section, you will learn how to perform hypothesis testing, as well as the difference between a null and alternative hypothesis. We will discuss rejection and significance levels, and type I and type II errors. The lessons will teach you how to test for the mean when the population variance is known and unknown, as well as how to test for the mean when you are dealing with dependent and independent samples. You will also become familiar with the p-value. To consolidate the new knowledge, we will conclude with a practical example.