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Fitting Statistical Models to Data with Python

Intermediate English SpecializationFREE
SkillsStatistical modellingLinear regressionLogistic regressionGeneralised linear modelsPythonStatsmodels

Is this course right for you?

Our take
The third course of the University of Michigan's Statistics with Python specialisation, where the series turns from understanding data to modelling it.

Good for: Learning statistical modelling in Python, rigorously.

Skip if: You are new to statistics or Python, or want a quick overview.

It works through linear and logistic regression, generalised linear models and more, all in Python and with genuine statistical rigour rather than copy-paste recipes. That seriousness is the appeal for people who want modelling done properly, and the reason it assumes a foundation: if you're new to statistics or Python, or you want a quick overview, start earlier in the specialisation.

Audit it free on Coursera; a certificate needs a subscription (about $49/month) or Coursera Plus, with financial aid available, and evidences the work done rather than a formal qualification. Statistical modelling fundamentals hold their value (as of 2026).

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About this course

Fitting Statistical Models to Data with Python is Course 3 of Michigan's Statistics with Python specialization. It moves from understanding data to modelling it: linear and logistic regression, generalised linear models, hierarchical/multilevel models, and Bayesian techniques — all applied through case studies in Python using Statsmodels, pandas, and Seaborn.

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Instructor

BG
Brenda Gunderson
Coursera instructor

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.

Frequently asked questions

It helps. This is the third course in the University of Michigan's Statistics with Python specialization, so it assumes the grounding from the earlier courses — understanding and visualising data, and statistical inference — plus comfortable Python. You can take it fairly independently with equivalent knowledge, but arriving without that statistics-and-Python base makes the modelling material a steeper climb than intended.

Statistics, primarily. It focuses on fitting statistical models — like linear and logistic regression and multilevel models — to understand relationships and draw inferences from data, using Python. That overlaps with machine learning but the emphasis is on statistical modelling and interpretation rather than predictive-performance optimisation. If you want applied ML with scikit-learn, that is a different focus; this is the statistical-modelling side done in Python.

Comfortable fundamentals plus familiarity with data libraries. It uses Python (with pandas and statistical libraries) to fit and interpret models, so you should already write Python confidently and ideally have met pandas. It is not a Python primer. The earlier courses in the specialization build some of this, so taken in sequence the Python demands are manageable; taken alone, arrive prepared.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded assignments and the certificate require a subscription, with financial aid available if you apply. Since the value is in actually fitting and interpreting models in Python, the paid track is worth considering if you want the hands-on practice rather than only the lectures.

As a single course within the specialization, it typically runs about four weeks at a few hours a week, more if the statistics or Python is new. It is hands-on, so the time goes into working through the modelling exercises, with the full Statistics with Python specialization a longer commitment if you pursue it.
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