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.
Target Audience:
Duration: 01:19
Description:
Learners continue their exploration of data science in this 10-video course, which deals with using NumPy, Pandas, and SciPy libraries to perform various statistical summary operations on real data sets. This beginner-level course assumes some prior experience with Python programming and an understanding of basic statistical concepts such as mean, standard deviation, and correlation. The course opens by exploring different ways to visualize data by using the Matplotlib library, including univariate and bivariate distributions. Next, you will move to computing descriptor statistics for distributions, such as variance and standard error, by using the NumPy, Pandas, and SciPy libraries. Learn about the concept of the z-score, in which every value in a distribution is expressed in terms of the number of standard deviations from the mean value. Then cover the computation of the z-score for a series using SciPy. In the closing exercise, you will make use of the matplotlib data visualization library through three points represented by given coordinates, then enumerate all of the details which are conveyed in a Boxplot.
Target Audience:
Duration: 01:16