Fitting Statistical Models to Data with Python

Coursera Fitting Statistical Models to Data with Python

Platform
Coursera
Provider
University of Michigan
Effort
4-6 hours a week
Length
4 weeks
Language
English
Credentials
Paid Certificate Available
Part of
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Overview
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.

Syllabus
WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

WEEK 4: Special Topics

Taught by
Brenda Gunderson, Brady T. West and Kerby Shedden
Author
Coursera
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