Predictive Modeling, Model Fitting, and Regression Analysis
- 4.4
-
![]()
![]()
![]()
![]()
Course Summary
Learn how to build predictive models and fit regression analysis using R. This course covers key concepts such as data preprocessing, model selection, and interpretation of results.Key Learning Points
- Gain knowledge in data preprocessing and model selection
- Learn how to fit regression models
- Understand how to interpret and evaluate results
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $68,000
- India: ₹7,00,000
- Spain: €30,000
- Business Analyst
- USA: $70,000
- India: ₹8,00,000
- Spain: €35,000
- Data Scientist
- USA: $115,000
- India: ₹12,00,000
- Spain: €45,000
Related Topics for further study
Learning Outcomes
- Gain practical experience in building predictive models
- Learn how to fit regression models and interpret results
- Understand the importance of data preprocessing and model selection
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Familiarity with R programming language
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
- Applied Machine Learning
Related Education Paths
Notable People in This Field
- Hadley Wickham
- Andrew Gelman
- Nate Silver
Related Books
Description
Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.
Knowledge
- The application of predictive modeling to professional and academic work
- Applications of classification analysis: decision trees
- Applications of regression analysis (linear and logistic)
Outline
- Predictive Modeling
- Supervised vs. Unsupervised Modeling
- Predictive Modeling
- Supplemental Resources
- Data Dimensionality and Classification Analysis
- Data Dimensionality and Classification Analysis
- Supplemental Resources
- Modules 1 and 2
- Model Fitting
- Model Generalization
- Model Fitting
- Supplemental Resources
- Regression Analysis
- Regression Analysis
- Supplemental Resource
- Modules 3 and 4
Summary of User Reviews
Coursera's Predictive Modeling, Model Fitting, and Regression Analysis course is highly recommended by users for its comprehensive content and practical applications. Many users praise the interactive nature of the course, which encourages active learning and problem-solving.Key Aspect Users Liked About This Course
Interactive and practical nature of the coursePros from User Reviews
- Comprehensive content on predictive modeling and regression analysis
- Practical applications and hands-on exercises
- Engaging and interactive course format
- Expert instructors with real-world experience
- Flexible scheduling and self-paced learning options
Cons from User Reviews
- Heavy reliance on R programming language, which may be challenging for beginners
- Course may be too technical and math-heavy for some learners
- Course may not cover advanced topics in predictive modeling and regression analysis
- Some users report technical issues with the online platform
- Course may require a significant time commitment for completion
Dursun Delen
4.4 Raiting
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:...
Making Predictions with Data and Python
- 4
-
![]()
![]()
![]()
![]()
![]()
Build Awesome Predictive Models with Python Python has become one of any data scientist's favorite tools for doing Predictive Analytics. About the author...
Fundamentals of Quantitative Modeling
- 4.6
-
![]()
![]()
![]()
![]()
![]()
How can you put data to work for you? These building blocks will be put to use in the other courses in this Specialization....
Statistical Thinking for Industrial Problem Solving, presented by JMP
- 4.8
-
![]()
![]()
![]()
![]()
![]()
Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS....
