Fitting Statistical Models to Data with Python
- 4.4
-
![]()
![]()
![]()
![]()
Course Summary
Learn how to fit statistical models to data using the Python language in this course. Gain hands-on experience with linear regression, generalized linear models, and more.Key Learning Points
- Use Python to fit statistical models to data
- Understand the principles of linear regression
- Learn how to apply generalized linear models
- Gain hands-on experience with real-world data sets
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453 - $118,741
- India: ₹276,000 - ₹1,576,000
- Spain: €21,000 - €39,000
- Data Scientist
- USA: $84,000 - $154,000
- India: ₹360,000 - ₹2,500,000
- Spain: €27,000 - €51,000
- Statistical Analyst
- USA: $62,000 - $107,000
- India: ₹418,000 - ₹1,700,000
- Spain: €25,000 - €43,000
Related Topics for further study
Learning Outcomes
- Understand the principles of linear regression
- Learn how to apply generalized linear models to data
- Gain hands-on experience with real-world data sets
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Understanding of statistical concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Data Analysis with Python
- Applied Machine Learning
Related Education Paths
Notable People in This Field
- Wes McKinney
- Hadley Wickham
Related Books
Description
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.
Outline
- 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
- Course Feedback
- Keep Learning with Michigan Online
- Week 4 Python Assessment
Summary of User Reviews
Learn to fit statistical models to data using Python with this highly rated Coursera course. Many users found the course engaging and informative.Key Aspect Users Liked About This Course
Engaging and informative course contentPros from User Reviews
- Well-structured course content
- Clear explanations and examples
- Good balance of theory and practical exercises
- Helpful course materials and resources
- Great instructor
Cons from User Reviews
- Some users found the course too basic
- Lack of advanced topics or applications
- Occasional technical difficulties with the platform
- Limited interaction with instructors or peers
- Course may be too focused on Python for some users
Brenda Gunderson
4.4 Raiting
Data Science 2021 : Complete Data Science & Machine Learning
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science Well, you have come to the right place....
Python Data Analysis & Visualization Bootcamp
- 4.3
-
![]()
![]()
![]()
![]()
![]()
Financial Data Analysis and Visualization with Python: The HighQuality Study kit Welcome to Data Analysis Analytics Bootcamp content powered by TakenMind....
Logistic Regression in R Studio
- 4.4
-
![]()
![]()
![]()
![]()
![]()
Logistic regression in R Studio tutorial for beginners. You can do Predictive modeling using R Studio after this course. After completing this course, you will be able to:...
AI Workflow: Machine Learning, Visual Recognition and NLP
- 4.5
-
![]()
![]()
![]()
![]()
![]()
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. Out-of-the-box Watson models for natural language understanding and visual recognition will be used....
