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
Give your career the gift of Coursera Plus with $160 off, billed annually. Save today.
Regression Analysis: Simplify Complex Data Relationships
This course is part of Google Advanced Data Analytics Professional Certificate
Instructor: Google Career Certificates
Top Instructor
43,209 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.7
(432 reviews)
Advanced level
Designed for those already in the industry
Flexible schedule
Approx. 32 hours
Learn at your own pace
96%
Most learners liked this course
What you'll learn
Investigate relationships in datasets
Identify regression model assumptions
Perform linear and logistic regression using Python
Practice model evaluation and interpretation
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
27 quizzes
Taught in English
See how employees at top companies are mastering in-demand skills
Build your Data Analysis expertise
This course is part of the Google Advanced Data Analytics Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- 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
Instructor ratings
4.4 (106 ratings)Offered by
Recommended if you're interested in Data Analysis
University of Maryland, College Park
Google
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
Showing 3 of 432
4.7
432 reviews
- 5 stars
83.10%
- 4 stars
9.49%
- 3 stars
3.93%
- 2 stars
1.62%
- 1 star
1.85%
New to Data Analysis? Start here.
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
Frequently asked questions
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.