COUniversity of Michigan · on Coursera
Fitting Statistical Models to Data with Python
Intermediate English Professional CertificateFREE
What you'll learn
Fit and interpret linear and logistic regression
Work with generalised linear models
Apply hierarchical and multilevel models
Use Bayesian inference techniques
Model data in Python with Statsmodels and pandas
Connect research questions to the right model
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
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Instructor
BG
Brenda Gunderson
Coursera instructor
— learners— courses — instructor rating
Taught by University of Michigan statistics faculty including Brenda Gunderson, Brady West, and Kerby Shedden. The course brings a rigorous, applied-statistics perspective with hands-on Python throughout.
Requirements
- High-school algebra
- Earlier courses in the specialization (or equivalent stats/Python)
Who this course is for
- Learners building applied-statistics skills
- Data analysts deepening their modelling
- Anyone in the Statistics with Python specialization
About this provider
CO
Coursera
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4.6 trust score
Frequently asked questions
High-school algebra and ideally the earlier courses in Michigan's Statistics with Python specialization (or equivalent stats and Python background).
Learners note it's solid but the difficulty ramps up partway through (around the middle weeks), and lectures can run long. It's worthwhile, just be ready to put in focus.
You can audit the full course free on Coursera. A certificate and graded work require a subscription.
Mainly Statsmodels for modelling, with pandas and Seaborn for handling and visualising data, in Jupyter notebooks.
Both — it's applied statistics done in Python, so you learn the modelling concepts and implement them in code through case studies.