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#### 100% online

Start instantly and learn at your own schedule.

#### Intermediate Level

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

#### Approx. 14 hours to complete

Suggested: 4 weeks; 4-6 hours/week...

#### English

Subtitles: English, Korean

### Skills you will gain

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression

#### 100% online

Start instantly and learn at your own schedule.

#### Intermediate Level

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

#### Approx. 14 hours to complete

Suggested: 4 weeks; 4-6 hours/week...

#### English

Subtitles: English, Korean

Week
1

## Week 1

3 hours to complete

## WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

3 hours to complete
8 videos (Total 73 min), 6 readings, 1 quiz
8 videos
Fitting Statistical Models to Data with Python Guidelines5m
What Do We Mean by Fitting Models to Data?18m
Types of Variables in Statistical Modeling13m
Different Study Designs Generate Different Types of Data: Implications for Modeling9m
Objectives of Model Fitting: Inference vs. Prediction11m
Plotting Predictions and Prediction Uncertainty8m
Python Statistics Landscape2m
Course Syllabus5m
Meet the Course Team!10m
Mixed effects models: Is it time to go Bayesian by default?15m
Python Statistics Landscape1m
1 practice exercise
Week 1 Assessment15m
Week
2

## 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 Inference15m
Interview: Causation vs Correlation18m
Logistic Regression Introduction15m
Logistic Regression Inference7m
NHANES Case Study Tutorial (Linear and Logistic Regression)17m
Linear Regression Models: Notation, Parameters, Estimation Methods30m
Try It Out: Continuous Data Scatterplot App15m
Importance of Data Visualization: The Datasaurus Dozen10m
Logistic Regression Models: Notation, Parameters, Estimation Methods30m
3 practice exercises
Linear Regression Quiz20m
Logistic Regression Quiz15m
Week 2 Python Assessment20m
Week
3

## Week 3

4 hours to complete

## WEEK 3 - FITTING MODELS TO DEPENDENT DATA

4 hours to complete
8 videos (Total 121 min), 2 readings, 2 quizzes
8 videos
Multilevel Linear Regression Models21m
Multilevel Logistic Regression models14m
Practice with Multilevel Modeling: The Cal Poly App12m
What are Marginal Models and Why Do We Fit Them?13m
Marginal Linear Regression Models19m
Marginal Logistic Regression11m
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10m
Visualizing Multilevel Models10m
Likelihood Ratio Tests for Fixed Effects and Variance Components10m
2 practice exercises
Name That Model15m
Week 3 Python Assessment20m
Week
4

## Week 4

3 hours to complete

## WEEK 4: Special Topics

3 hours to complete
6 videos (Total 105 min), 3 readings, 1 quiz
6 videos
Bayesian Approaches to Statistics and Modeling15m
Bayesian Approaches Case Study: Part I13m
Bayesian Approaches Case Study: Part II19m
Bayesian Approaches Case Study - Part III23m
Bayesian in Python19m
Other Types of Dependent Variables20m
Optional: A Visual Introduction to Machine Learning20m
Course Feedback10m
1 practice exercise
Week 4 Python Assessment20m
4.4
26 Reviews

### Top reviews from Fitting Statistical Models to Data with Python

By BSJan 18th 2020

I am very thankful to you sir.. i have learned so much great things through this course.\n\nthis course is very helpful for my career. i would like to learn more courses from you. thank you so much.

By AFMar 12th 2019

The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.

### Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics

Research Associate Professor
Institute for Social Research

### Kerby Shedden

Professor
Department of Statistics

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future....

## 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....