This is a recorded Replay of the Data Science for Managers Live session that ran on April 12th at 11 AM ET.

This is a recorded Replay of the Data Science for Managers Live session that ran on April 13th at 11 AM ET.

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.

Explore a variety of new data science tools available today; the different uses for these tools; and the benefits and challenges in deploying them in this 12-video course. First, examine a data science platform, the nucleus of technologies used to perform data science tasks. You will then explore the analysis process to inspect, clean, transform, and model data. Next, the course surveys integrating and exploring data, coding, and building models using that data, deploying the models to production, and delivering results through applications or by generating reports. You will see how a great data science platform should be flexible and scalable, and it should combine multiple features and capabilities that effectively centralize data science efforts. You will learn the six sequential steps of a typical data science workflow, from defining the objective for the project to reporting the results. Finally, explore DevOps, resources that allow developers and IT to work together in harmony which includes people, processes, and infrastructure; and its typical functionalities including integration, testing, packaging, as well as deployment.