Data Science Statistics: Applied Inferential Statistics

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.