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Fitting Statistical Models to Data with Python   


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. 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. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.

This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).

During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

Difficulty Level: INTERMEDIATE

Estimated Learning Time:  15 hours


Probability & Statistics

General Statistics


Computer Programming

Machine Learning

Machine Learning Algorithms

Bayesian Statistics

Business Analysis

Data Analysis


Course Instructor PhotoBrenda Gunderson
Lecturer IV and Research Fellow
Department of Statistics

Course Instructor PhotoBrady T. West
Research Associate Professor
Institute for Social Research

Course Instructor PhotoKerby Shedden
Department of Statistics


  • Fitting Statistical Models to Data with Python
  • Fee: $59.00

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