Regression Analysis: Simplify Complex Data Relationships
- Offered byCoursera
Regression Analysis: Simplify Complex Data Relationships at Coursera Overview
Duration | 28 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Regression Analysis: Simplify Complex Data Relationships at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Coursera Labs Includes hands on learning projects. Learn more about Coursera Labs External Link
- Advanced Level
- Approx. 28 hours to complete
- English Subtitles: English
Regression Analysis: Simplify Complex Data Relationships at Coursera Course details
- This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.
- Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.
- Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.
- By the end of this course, you will:
- -Explore the use of predictive models to describe variable relationships, with an emphasis on correlation
- -Determine how multiple regression builds upon simple linear regression at every step of the modeling process
- -Run and interpret one-way and two-way ANOVA tests
- -Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models
Regression Analysis: Simplify Complex Data Relationships at Coursera Curriculum
Introduction to complex data relationships
Introduction to Course 5
Tiffany: Gain actionable insights with regression models
Welcome to week 1
PACE in regression analysis
Introduction to linear regression
Mathematical linear regression
Introduction to logistic regression
Wrap-up
Helpful resources and tips
Course 5 overview
Glossary terms from week 1
Test your knowledge: PACE in regression analysis
Test your knowledge: Linear regression
Test your knowledge: Logistic regression
Weekly challenge 1
Simple linear regression
Welcome to week 2
Jerrod: The incredible value of mentorship
Ordinary least squares estimation
Make linear regression assumptions
Explore linear regression with Python
Evaluate uncertainty in regression analysis
Model evaluation metrics
Interpret and present linear regression results
Wrap-up
Explore ordinary least squares
The four main assumptions of simple linear regression
Follow-along instructions: Explore linear regression with Python
Code functions and documentation
Interpret measures of uncertainty in regression
Evaluation metrics for simple linear regression
Correlation versus causation: Interpret regression results
Glossary terms from week 2
Test your knowledge: Foundations of linear regression
Test your knowledge: Assumptions and construction in Python
Test your knowledge: Evaluate a linear regression model
Test your knowledge: Interpret linear regression results
Weekly challenge 2
Multiple linear regression
Welcome to week 3
Introduction to multiple regression
Represent categorical variables
Make assumptions with multiple linear regressions
Interpret multiple regression coefficients
Interpret multiple regression results with Python
The problem with overfitting
Top variable selection methods
Regularization: Lasso, Ridge, and Elastic Net regression
Wrap-up
Multiple linear regression scenarios
Multiple linear regression assumptions and multicollinearity
Follow-along instructions: Interpret multiple regression results with Python
Underfitting and overfitting
Glossary terms from week 3
Test your knowledge: Understand multiple linear regression
Test your knowledge: Model assumptions revisited
Test your knowledge: Model interpretation
Test your knowledge: Variable selection and model evaluation
Weekly challenge 3
Advanced hypothesis testing
Welcome to week 4
Hypothesis testing with chi-squared
Introduction to the analysis of variance
Explore one-way vs. two-way ANOVA tests with Python
ANOVA post hoc tests with Python
Ignacio: Discovery at every stage of your career
ANCOVA: Analysis of covariance
More dependent variables: MANOVA and MANCOVA
Wrap-up
Chi-squared tests: Goodness of fit versus independence
Follow-along instructions: Explore one-way versus two-way ANOVA tests with Python
Glossary terms from week 4
Test your knowledge: The chi-squared test
Test your knowledge: Analysis of variance
Test your knowledge: ANCOVA, MANOVA, and MANCOVA
Weekly challenge 4
Logistic regression
Welcome to week 5
Find the best logistic regression model for your data
Construct a logistic regression model with Python
Evaluate a binomial logistic regression model
Key metrics to assess logistic regression results
Interpret the results of a logistic regression
Answer questions with regression models
Wrap-up
Follow-along instructions: Construct a logistic regression model with Python
Common logistic regression metrics in Python
Interpret logistic regression models
Prediction with different types of regression
Glossary terms from week 5
Test your knowledge: Foundations of logistic regression
Test your knowledge: Logistics regression with Python
Test your knowledge: Interpret logistic regression results
Test your knowledge: Compare regression models
Weekly challenge 5
Course 5 end-of-course project
Welcome to week 6
Leah: Strategies for sharing models and modeling techniques
Introduction to Course 5 end-of-course portfolio project
End-of-course project wrap-up and tips for ongoing career success
Course wrap-up
Course 5 end-of-course portfolio project overview: Automatidata
Activity Exemplar: Create your Course 5 Automatidata project
Course 5 glossary
Get started on the next course
Activity: Create your Course 5 Automatidata project
Assess your Course 5 end-of-course project
Regression Analysis: Simplify Complex Data Relationships at Coursera Admission Process
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