Understanding and Visualizing Data with Python

In this course, students will be introduced to the field of statistics, including where data comes from, study design, data management, and data exploration and visualization. Students will identify different types of data and learn to visualize, analyze, and interpret summary data for both univariate and multivariate data. Students will also learn the differences between probability and non-probability sampling of large populations, how sample estimates vary, and how to make inferences about large populations based on probability sampling. At the end of each week, students will apply the statistical concepts they have learned using Python in the course environment. During these labs, students will be introduced to the various uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos will guide students through the creation of visualizations and data management in Python. This course uses the Jupyter Notebook environment within Coursera.