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.
Regression Analysis: Simplify Complex Data Relationships
This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
Top Instructor
Sponsored by UNHCR - The UN Refugee Agency
42,409 already enrolled
(426 reviews)
What you'll learn
Investigate relationships in datasets
Identify regression model assumptions
Perform linear and logistic regression using Python
Practice model evaluation and interpretation
Details to know
Add to your LinkedIn profile
27 quizzes
See how employees at top companies are mastering in-demand skills
Build your Data Analysis expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 6 modules in this course
You’ll begin by exploring the main steps for building regression models, from identifying your assumptions to interpreting your results. Next, you’ll explore the two main types of regression: linear and logistic. You’ll learn how data professionals use linear and logistic regression to approach different kinds of business problems.
What's included
8 videos3 readings4 quizzes1 plugin
You’ll explore how to use models to describe complex data relationships. You’ll focus on relationships of correlation. Then, you’ll build a simple linear regression model in Python and interpret your results.
What's included
9 videos8 readings5 quizzes5 ungraded labs
After simple regression, you’ll move on to a more complex regression model: multiple linear regression. You’ll consider how multiple regression builds on simple linear regression at every step of the modeling process. You’ll also get a preview of some key topics in machine learning: selection, overfitting, and the bias-variance tradeoff.
What's included
10 videos4 readings5 quizzes3 ungraded labs1 plugin
You’ll build on your prior knowledge of hypothesis testing to explore two more statistical tests: Chi-squared and analysis of variance (ANOVA). You’ll learn how data professionals use these tests to analyze different types of data. Finally, you’ll conduct two kinds of Chi-squared tests, as well as one-way and two-way ANOVA tests.
What's included
9 videos3 readings4 quizzes3 ungraded labs
You’ll investigate binomial logistic regression, a type of regression analysis that classifies data into two categories. You’ll learn how to build a binomial logistic regression model and how data professionals use this type of model to gain insights from their data.
What's included
8 videos4 readings5 quizzes3 ungraded labs
You’ll complete an end-of-course project by building a regression model to analyze a workplace scenario dataset.
What's included
5 videos10 readings4 quizzes6 ungraded labs
Instructor
Offered by
Why people choose Coursera for their career
Learner reviews
Showing 3 of 426
426 reviews
- 5 stars
82.94%
- 4 stars
9.57%
- 3 stars
3.97%
- 2 stars
1.63%
- 1 star
1.86%
Recommended if you're interested in Data Science
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy