Fitting Statistical Models to Data with Python
- Offered byCoursera
Fitting Statistical Models to Data with Python at Coursera Overview
Duration | 15 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Fitting Statistical Models to Data with Python at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Fitting Statistical Models to Data with Python at Coursera Course details
- 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.
Fitting Statistical Models to Data with Python at Coursera Curriculum
WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
Welcome to the Course!
Fitting Statistical Models to Data with Python Guidelines
What Do We Mean by Fitting Models to Data?
Types of Variables in Statistical Modeling
Different Study Designs Generate Different Types of Data: Implications for Modeling
Objectives of Model Fitting: Inference vs. Prediction
Plotting Predictions and Prediction Uncertainty
Python Statistics Landscape
Course Syllabus
Meet the Course Team!
Help Us Learn More About You!
About Our Datasets
Mixed effects models: Is it time to go Bayesian by default?
Python Statistics Landscape
Week 1 Assessment
WEEK 2 - FITTING MODELS TO INDEPENDENT DATA
Linear Regression Introduction
Linear Regression Inference
Interview: Causation vs Correlation
Logistic Regression Introduction
Logistic Regression Inference
NHANES Case Study Tutorial (Linear and Logistic Regression)
Linear Regression Models: Notation, Parameters, Estimation Methods
Try It Out: Continuous Data Scatterplot App
Importance of Data Visualization: The Datasaurus Dozen
Logistic Regression Models: Notation, Parameters, Estimation Methods
Linear Regression Quiz
Logistic Regression Quiz
Week 2 Python Assessment
WEEK 3 - FITTING MODELS TO DEPENDENT DATA
What are Multilevel Models and Why Do We Fit Them?
Multilevel Linear Regression Models
Multilevel Logistic Regression models
Practice with Multilevel Modeling: The Cal Poly App
What are Marginal Models and Why Do We Fit Them?
Marginal Linear Regression Models
Marginal Logistic Regression
NHANES Case Study Tutorial (Marginal and Multilevel Regression)
Visualizing Multilevel Models
Likelihood Ratio Tests for Fixed Effects and Variance Components
Name That Model
Week 3 Python Assessment
WEEK 4: Special Topics
Should We Use Survey Weights When Fitting Models?
Bayesian Approaches to Statistics and Modeling
Bayesian Approaches Case Study: Part I
Bayesian Approaches Case Study: Part II
Bayesian Approaches Case Study - Part III
Bayesian in Python
Other Types of Dependent Variables
Optional: A Visual Introduction to Machine Learning
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Week 4 Python Assessment
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