We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. This course utilizes the Jupyter Notebook environment within Coursera. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries.

Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.Īt the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment.

Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data.

Bayesian statistics khan academy