About this Course

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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 StatisticsPython ProgrammingStatistical Modelstatistical regression
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

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

Approx. 15 hours to complete
English

Offered by

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University of Michigan

Syllabus - What you will learn from this course

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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
6 readings
Course Syllabus5m
Meet the Course Team!10m
Help Us Learn More About You!10m
About Our Datasets2m
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
4 readings
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
2 readings
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
3 readings
Other Types of Dependent Variables20m
Optional: A Visual Introduction to Machine Learning20m
Course Feedback10m
1 practice exercise
Week 4 Python Assessment20m

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About the Statistics with Python Specialization

Statistics with Python

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