### Data Science Statistics: Applied Inferential Statistics

**Overview/Description**

**Expected Duration**

**Lesson Objectives**

**Course Number**

**Expertise Level**

**Overview/Description**

Explore how different t-tests can be performed by using the SciPy library for hypothesis testing in this 10-video course, which continues your explorations of data science. This beginner-level course assumes prior experience with Python programming, along with an understanding of such terms as skewness and kurtosis and concepts from inferential statistics, such as t-tests and regression. Begin by learning how to perform three different t-tests—the one-sample t-test, the independent or two-sample t-test, and the paired t-test—on various samples of data using the SciPy library. Next, learners explore how to interpret results to accept or reject a hypothesis. The course covers, as an example, how to fit a regression model on the returns on an individual stock, and on the S&P 500 Index, by using the scikit-learn library. Finally, watch demonstrations of measuring skewness and kurtosis in a data set. The closing exercise asks you to list three different types of t-tests, identify values which are returned by t-tests, and write code to calculate the percentage returns from time series data using Pandas.

**Expected Duration (hours)**

1.3

**Lesson Objectives**

**Data Science Statistics: Applied Inferential Statistics**

**Course Number:**

it_dssds2dj_02_enus

**Expertise Level**

Intermediate