Inferential Statistical Analysis with Python

In this course, we will learn the basic principles of using data to evaluate and analyze theories. We will analyze both categorical and quantitative data, starting with single population methods and extending them to comparing two populations. We will learn how to construct confidence intervals. We will also use sample data to evaluate whether a theory about the value of a parameter is consistent with the data. The focus will be on the correct interpretation of inference results. At the end of each week, students will apply what they have learned using Python in the course environment. During these labs, students will work through tutorials focused on specific examples to help reinforce the week’s statistical concepts, which will include further in-depth exploration of Python libraries including Statsmodels, Pandas, and Seaborn. This course uses the Jupyter Notebook environment within Coursera.