This course is part of the Statistics with Python Specialization

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Statistics with Python SpecializationUniversity of Michigan

About this Course

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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.

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Course 3 of 3 in the

Statistics with Python Specialization

Intermediate Level

Completion of the first two courses in this specialization; high school-level algebra

Approx. 15 hours to complete

English

Skills you will gain

Bayesian Statistics
Python Programming
Statistical Model
statistical regression

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 3 in the

Statistics with Python Specialization

Intermediate Level

Completion of the first two courses in this specialization; high school-level algebra

Approx. 15 hours to complete

English

Instructors

Instructor rating

4.6/5 (89 Ratings)

Offered by

University of Michigan

Syllabus - What you will learn from this course

Content Rating87%(2,356 ratings)

Week1

Week 1

3 hours to complete

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

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.

3 hours to complete

8 videos (Total 73 min), 6 readings, 1 quiz

8 videos

Welcome to the Course!3mFitting Statistical Models to Data with Python Guidelines5mWhat Do We Mean by Fitting Models to Data?18mTypes of Variables in Statistical Modeling13mDifferent Study Designs Generate Different Types of Data: Implications for Modeling9mObjectives of Model Fitting: Inference vs. Prediction11mPlotting Predictions and Prediction Uncertainty8mPython Statistics Landscape2m

6 readings

Course Syllabus10mMeet the Course Team!10mHelp Us Learn More About You!10mAbout Our Datasets2mMixed effects models: Is it time to go Bayesian by default?15mPython Statistics Landscape1m

1 practice exercise

Week 1 Assessment15m

Week2

Week 2

5 hours to complete

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

5 hours to complete

6 videos (Total 85 min), 4 readings, 3 quizzes

6 videos

Linear Regression Introduction11mLinear Regression Inference15mInterview: Causation vs Correlation18mLogistic Regression Introduction15mLogistic Regression Inference7mNHANES Case Study Tutorial (Linear and Logistic Regression)17m

4 readings

Linear Regression Models: Notation, Parameters, Estimation Methods30mTry It Out: Continuous Data Scatterplot App15mImportance of Data Visualization: The Datasaurus Dozen10mLogistic Regression Models: Notation, Parameters, Estimation Methods30m

3 practice exercises

Linear Regression Quiz20mLogistic Regression Quiz15mWeek 2 Python Assessment20m

Week3

Week 3

4 hours to complete

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

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.

4 hours to complete

8 videos (Total 121 min), 2 readings, 2 quizzes

8 videos

What are Multilevel Models and Why Do We Fit Them?17mMultilevel Linear Regression Models21mMultilevel Logistic Regression models14mPractice with Multilevel Modeling: The Cal Poly App12mWhat are Marginal Models and Why Do We Fit Them?13mMarginal Linear Regression Models19mMarginal Logistic Regression11mNHANES Case Study Tutorial (Marginal and Multilevel Regression)10m

2 readings

Visualizing Multilevel Models10mLikelihood Ratio Tests for Fixed Effects and Variance Components10m

2 practice exercises

Name That Model15mWeek 3 Python Assessment20m

Week4

Week 4

3 hours to complete

WEEK 4: Special Topics

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.

3 hours to complete

6 videos (Total 105 min), 4 readings, 1 quiz

6 videos

Should We Use Survey Weights When Fitting Models?13mBayesian Approaches to Statistics and Modeling15mBayesian Approaches Case Study: Part I13mBayesian Approaches Case Study: Part II19mBayesian Approaches Case Study - Part III23mBayesian in Python19m

4 readings

Other Types of Dependent Variables20mOptional: A Visual Introduction to Machine Learning20mCourse Feedback10mKeep Learning with Michigan Online10m

1 practice exercise

Week 4 Python Assessment20m

Reviews

4.4

121 reviews

5 stars
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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM FITTING STATISTICAL MODELS TO DATA WITH PYTHON

by SKMay 27, 2020

Great course. It really improved my understanding of statistical modeling methodologies.

by VOSep 17, 2019

Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science

by SMJun 15, 2020

A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.

by KRJan 12, 2021

These whole three certifications lays the foundation for learning Machine Learning a more in-depth way.

View all reviews

About the Statistics with Python Specialization

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Fitting Statistical Models to Data with Python

About this Course

Skills you will gain

Instructors

Brenda Gunderson

Brady T. West

Kerby Shedden

Offered by

University of Michigan

Syllabus - What you will learn from this course

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

Reviews

TOP REVIEWS FROM FITTING STATISTICAL MODELS TO DATA WITH PYTHON

About the Statistics with Python Specialization

Frequently Asked Questions

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Fitting Statistical Models to Data with Python

About this Course

Skills you will gain

Instructors

Brenda Gunderson

Brady T. West

Kerby Shedden

Offered by

University of Michigan

Syllabus - What you will learn from this course

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

Reviews

TOP REVIEWS FROM FITTING STATISTICAL MODELS TO DATA WITH PYTHON

About the Statistics with Python Specialization

Frequently Asked Questions

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