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Regression Analysis: Simplify Complex Data Relationships

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Regression Analysis: Simplify Complex Data Relationships

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Gain insight into a topic and learn the fundamentals.
4.7

599 reviews

Advanced level
Designed for those already in the industry
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
4.7

599 reviews

Advanced level
Designed for those already in the industry
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace

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

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Assessments

27 assignments

Taught in English
96%
Most learners liked this course

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

There are 6 modules in this course

This is the fourth course 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 eight 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

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 assignments2 plugins

8 videosTotal 39 minutes
  • Introduction to Course 4 5 minutes
  • Tiffany: Gain actionable insights with regression models3 minutes
  • Welcome to module 12 minutes
  • PACE in regression analysis 5 minutes
  • Introduction to linear regression 9 minutes
  • Mathematical linear regression 6 minutes
  • Introduction to logistic regression7 minutes
  • Wrap-up3 minutes
3 readingsTotal 20 minutes
  • Helpful resources and tips8 minutes
  • Course 4 overview8 minutes
  • Glossary terms from module 14 minutes
4 assignmentsTotal 68 minutes
  • Module 1 challenge50 minutes
  • Test your knowledge: PACE in regression analysis6 minutes
  • Test your knowledge: Linear regression8 minutes
  • Test your knowledge: Logistic regression4 minutes
2 pluginsTotal 20 minutes
  • Categorize: Linear and logistic regression10 minutes
  • [Turkish learners ONLY] Categorize: Linear and logistic regression - Türkçe10 minutes

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 assignments5 ungraded labs

9 videosTotal 45 minutes
  • Welcome to module 24 minutes
  • Jerrod: The incredible value of mentorship3 minutes
  • Ordinary least squares estimation5 minutes
  • Make linear regression assumptions5 minutes
  • Explore linear regression with Python10 minutes
  • Evaluate uncertainty in regression analysis 5 minutes
  • Model evaluation metrics5 minutes
  • Interpret and present linear regression results6 minutes
  • Wrap-up 2 minutes
8 readingsTotal 56 minutes
  • Explore ordinary least squares8 minutes
  • Correlation and the intuition behind simple linear regression8 minutes
  • The four main assumptions of simple linear regression8 minutes
  • Code functions and documentation8 minutes
  • Interpret measures of uncertainty in regression8 minutes
  • Evaluation metrics for simple linear regression 4 minutes
  • Correlation versus causation: Interpret regression results8 minutes
  • Glossary terms from module 2 4 minutes
5 assignmentsTotal 74 minutes
  • Module 2 challenge50 minutes
  • Test your knowledge: Foundations of linear regression6 minutes
  • Test your knowledge: Assumptions and construction in Python 8 minutes
  • Test your knowledge: Evaluate a linear regression model6 minutes
  • Test your knowledge: Interpret linear regression results4 minutes
5 ungraded labsTotal 180 minutes
  • Annotated follow-along guide: Explore linear regression with Python20 minutes
  • Activity: Run simple linear regression60 minutes
  • Exemplar: Run simple linear regression20 minutes
  • Activity: Evaluate simple linear regression60 minutes
  • Exemplar: Evaluate simple linear regression20 minutes

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 assignments3 ungraded labs2 plugins

10 videosTotal 47 minutes
  • Welcome to module 34 minutes
  • Introduction to multiple regression4 minutes
  • Represent categorical variables6 minutes
  • Make assumptions with multiple linear regressions5 minutes
  • Interpret multiple regression coefficients6 minutes
  • Interpret multiple regression results with Python6 minutes
  • The problem with overfitting4 minutes
  • Top variable selection methods4 minutes
  • Regularization: Lasso, Ridge, and Elastic Net regression4 minutes
  • Wrap-up3 minutes
4 readingsTotal 24 minutes
  • Multiple linear regression scenarios4 minutes
  • Multiple linear regression assumptions and multicollinearity8 minutes
  • Underfitting and overfitting8 minutes
  • Glossary terms from module 34 minutes
5 assignmentsTotal 76 minutes
  • Module 3 challenge50 minutes
  • Test your knowledge: Understand multiple linear regression6 minutes
  • Test your knowledge: Model assumptions revisited8 minutes
  • Test your knowledge: Model interpretation4 minutes
  • Test your knowledge: Variable selection and model evaluation8 minutes
3 ungraded labsTotal 100 minutes
  • Annotated follow-along resource: Interpret multiple regression results with Python20 minutes
  • Activity: Perform multiple linear regression60 minutes
  • Exemplar: Perform multiple linear regression20 minutes
2 pluginsTotal 20 minutes
  • Identify: Multiple regression assumptions10 minutes
  • [Turkish learners ONLY] Identify: Multiple regression assumptions - Türkçe10 minutes

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 assignments3 ungraded labs

9 videosTotal 41 minutes
  • Welcome to module 4 4 minutes
  • Hypothesis testing with chi-squared6 minutes
  • Introduction to the analysis of variance 5 minutes
  • Explore one-way vs. two-way ANOVA tests with Python 5 minutes
  • ANOVA post hoc tests with Python5 minutes
  • Ignacio: Discovery at every stage of your career3 minutes
  • ANCOVA: Analysis of covariance 6 minutes
  • More dependent variables: MANOVA and MANCOVA 5 minutes
  • Wrap-up 2 minutes
3 readingsTotal 16 minutes
  • Chi-squared tests: Goodness of fit versus independence 8 minutes
  • More about ANOVA4 minutes
  • Glossary terms from module 44 minutes
4 assignmentsTotal 68 minutes
  • Module 4 challenge 50 minutes
  • Test your knowledge: The chi-squared test6 minutes
  • Test your knowledge: Analysis of variance6 minutes
  • Test your knowledge: ANCOVA, MANOVA, and MANCOVA6 minutes
3 ungraded labsTotal 100 minutes
  • Annotated follow-along guide: Explore one-way vs. two-way ANOVA tests with Python20 minutes
  • Activity: Hypothesis testing with Python60 minutes
  • Exemplar: Hypothesis testing with Python20 minutes

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 assignments3 ungraded labs

8 videosTotal 35 minutes
  • Welcome to module 53 minutes
  • Find the best logistic regression model for your data6 minutes
  • Construct a logistic regression model with Python4 minutes
  • Evaluate a binomial logistic regression model4 minutes
  • Key metrics to assess logistic regression results5 minutes
  • Interpret the results of a logistic regression6 minutes
  • Answer questions with regression models4 minutes
  • Wrap-up 2 minutes
4 readingsTotal 28 minutes
  • Common logistic regression metrics in Python8 minutes
  • Interpret logistic regression models8 minutes
  • Prediction with different types of regression8 minutes
  • Glossary terms from module 54 minutes
5 assignmentsTotal 70 minutes
  • Module 5 challenge50 minutes
  • Test your knowledge: Foundations of logistic regression4 minutes
  • Test your knowledge: Logistic regression with Python6 minutes
  • Test your knowledge: Interpret logistic regression results6 minutes
  • Test your knowledge: Compare regression models4 minutes
3 ungraded labsTotal 100 minutes
  • Annotated follow-along resource: Construct a logistic regression model with Python20 minutes
  • Activity: Perform logistic regression60 minutes
  • Exemplar: Perform logistic regression20 minutes

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 assignments6 ungraded labs

5 videosTotal 10 minutes
  • Welcome to module 62 minutes
  • Leah: Strategies for sharing models and modeling techniques 2 minutes
  • Introduction to your Course 4 end-of-course portfolio project1 minute
  • End-of-course project wrap-up and tips for ongoing career success2 minutes
  • Course wrap-up2 minutes
10 readingsTotal 60 minutes
  • Explore your Course 4 workplace scenarios8 minutes
  • Course 4 end-of-course portfolio project overview: Automatidata8 minutes
  • Activity Exemplar: Create your Course 4 Automatidata project4 minutes
  • Course 4 end-of-course portfolio project overview: TikTok8 minutes
  • Activity Exemplar: Create your Course 4 TikTok project4 minutes
  • Course 4 end-of-course portfolio project overview: Waze8 minutes
  • Activity Exemplar: Create your Course 4 Waze project4 minutes
  • Reflect and connect with peers2 minutes
  • Course 4 glossary10 minutes
  • Get started on the next course4 minutes
4 assignmentsTotal 130 minutes
  • Assess your Course 4 end-of-course project 40 minutes
  • Activity: Create your Course 4 Automatidata project30 minutes
  • Activity: Create your Course 4 TikTok project30 minutes
  • Activity: Create your Course 4 Waze project30 minutes
6 ungraded labsTotal 240 minutes
  • Activity: Course 4 Automatidata project lab60 minutes
  • Exemplar: Course 4 Automatidata project lab20 minutes
  • Activity: Course 4 TikTok project lab60 minutes
  • Exemplar: Course 4 TikTok project lab20 minutes
  • Activity: Course 4 Waze project lab60 minutes
  • Exemplar: Course 4 Waze project lab20 minutes

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Instructor ratings
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Google
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MB
·

Reviewed on Jul 12, 2023

Great course! Especially the statistical touch in it

ML
·

Reviewed on Dec 18, 2024

Very well structured. This course and the instructor simplifies many difficult concepts into bite-sized chunks that are easy to digest without feeling overwhelmed.

JG
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Reviewed on Apr 23, 2024

too good excellent don't hesitate to buy this course

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.

The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data ana

During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.

This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders. 

The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate. These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.

You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analytics.

We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Financial aid available,