University of Michigan
Fitting Statistical Models to Data with Python
University of Michigan

Fitting Statistical Models to Data with Python

This course is part of Statistics with Python Specialization

Taught in English

Some content may not be translated

Brenda Gunderson
Brady T. West
Kerby Shedden

Instructors: Brenda Gunderson

33,274 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.4

(673 reviews)

|

87%

Intermediate level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Deepen your understanding of statistical inference techniques by mastering the art of fitting statistical models to data.

  • Connect research questions with data analysis methods, emphasizing objectives, relationships between variables, and making predictions.

  • Explore various statistical modeling techniques like linear regression, logistic regression, and Bayesian inference using real data sets.

  • Work through hands-on case studies in Python with libraries like Statsmodels, Pandas, and Seaborn in the Jupyter Notebook environment.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(673 reviews)

|

87%

Intermediate level

Recommended experience

14 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Statistics with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 4 modules in this course

We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.

What's included

8 videos6 readings1 quiz2 ungraded labs

In this second week, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. You’ll also learn how to implement those models within Python.

What's included

5 videos4 readings3 quizzes3 ungraded labs

In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.

What's included

7 videos3 readings2 quizzes4 ungraded labs

In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. You’ll also have the opportunity to apply Bayesian techniques in Python.

What's included

6 videos4 readings1 quiz1 discussion prompt1 ungraded lab

Instructors

Instructor ratings
4.6 (96 ratings)
Brenda Gunderson
University of Michigan
3 Courses145,961 learners

Offered by

Recommended if you're interested in Probability and Statistics

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 673

4.4

673 reviews

  • 5 stars

    65.97%

  • 4 stars

    19.91%

  • 3 stars

    8.02%

  • 2 stars

    3.41%

  • 1 star

    2.67%

AA
4

Reviewed on Jun 19, 2020

ST
4

Reviewed on Jan 23, 2021

VO
5

Reviewed on Sep 17, 2019

New to Probability and Statistics? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions