Regression Analysis: Simplify Complex Data Relationships from Coursera

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

What's inside

Syllabus

Introduction to complex data relationships

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.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches methods highly relevant in industry

Builds upon an existing foundation for intermediate learners

Taught by Google employees who currently work in the field

Examines methods highly relevant to business

Develops professional skills or deep expertise in data analytics

Requires learners to come in with extensive background knowledge first

Reviews summary

Practical regression analysis with python

According to learners, this course offers a clear and accessible introduction to regression analysis, particularly highlighting its hands-on approach with Python. Students commend the instructors' ability to simplify complex topics and provide relevant business examples. While many found it a solid foundation for data professionals, some more experienced learners noted it could be too basic or lack deeper dives into advanced techniques, occasionally requiring supplemental study.

Content progression from simple to complex regression is well-organized.

"The flow from simple linear to multiple linear and then logistic regression was logical."

"The structure is well-paced, and it builds knowledge progressively."

"Loved the practical, hands-on approach. The concepts of linear and logistic regression were made very accessible."

Emphasizes real-world application through Python labs and projects.

"The hands-on labs in Python were incredibly helpful for understanding practical applications."

"The practical approach with Python coding assignments was exactly what I needed to build my skills."

"The project was decent, though, and I appreciate the practical exercises. The examples were relevant to business problems, which is what I needed for my job."

Instructors effectively simplify complex regression concepts.

"The concepts were explained very clearly, and the hands-on labs in Python were incredibly helpful for understanding practical applications."

"Excellent course! The instructors made complex topics easy to digest. The flow from simple linear to multiple linear and then logistic regression was logical."

"It simplifies complex relationships effectively. The instructors are clear and concise."

Pacing can be rushed in parts, possibly requiring external resources.

"The content felt a bit rushed in parts, particularly the ANOVA section. While it covers a good range of topics, I felt some explanations were superficial."

"Had to supplement with outside resources to fully grasp some concepts."

"I had to consult other books and online tutorials frequently. Not what I expected from an 'advanced' certificate."

Offers a solid introduction but may lack depth for advanced learners.

"Good course for beginners, but I found some parts a bit too basic. I was hoping for more advanced techniques and a deeper dive..."

"A good overview, but not for experts. If you already have a strong background in stats, you might find it too introductory."

"Disappointed with the lack of depth. The course touches on many topics but doesn't go deep enough for practical application in real-world complex scenarios."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Regression Analysis: Simplify Complex Data Relationships with these activities:

Review Regression and Hypothesis Testing

Show steps

Refreshing your knowledge of regression and hypothesis testing will provide a solid foundation for deeper exploration of advanced topics in this course.

Browse courses on Regression

Show steps

Review course notes from previous coursework on regression and hypothesis testing.
Work through practice problems to test your understanding.
Read articles or watch videos to refresh your knowledge.

Practice Regression Modeling in Python

Show steps

Working through guided tutorials on regression modeling in Python will help you develop practical skills that can be applied in this course and beyond.

Browse courses on Regression Modeling

Show steps

Find guided tutorials on Python libraries for regression modeling.
Follow the tutorials step-by-step to build regression models using real-world data.
Experiment with different parameters and settings to observe their effects on model performance.

Collaborate on Regression Modeling Projects

Show steps

Collaborating on regression modeling projects with peers will foster teamwork skills and provide diverse perspectives on problem-solving.

Browse courses on Regression Modeling

Show steps

Find a group of peers with similar interests in regression modeling.
Choose a project to work on together, such as building a model for a real-world dataset.
Divide tasks among group members and work together to complete the project.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Attend a Workshop on Logistic Regression

Show steps

Attending a workshop on logistic regression will provide in-depth knowledge and practical skills in this specific regression technique.

Browse courses on Logistic Regression

Show steps

Find a workshop on logistic regression that aligns with your skill level.
Participate actively in the workshop, ask questions, and engage with the instructor.
Apply the techniques learned in the workshop to your own projects or assignments.

Solve Regression and Hypothesis Testing Problems

Show steps

Solving practice drills on regression and hypothesis testing will enhance your problem-solving skills and solidify your understanding of key concepts.

Browse courses on Regression

Show steps

Find practice problems or test papers on regression and hypothesis testing.
Attempt to solve the problems independently.
Check your solutions against provided answer keys or consult with experts.

Read 'Regression Analysis by Example' by Chatterjee and Hadi

Show steps

Reading 'Regression Analysis by Example' will provide a comprehensive understanding of regression concepts and techniques.

View Regression Analysis By Example Using R (Wiley... on Amazon

Show steps

Read the book thoroughly to grasp the fundamentals of regression analysis.
Work through the examples and exercises to reinforce your understanding.
Apply the concepts to real-world data analysis scenarios.

Develop a Regression Model for a Business Problem

Show steps

Creating a regression model for a business problem will allow you to apply the concepts learned in this course to a practical scenario.

Browse courses on Regression Modeling

Show steps

Identify a business problem that can be addressed using regression modeling.
Gather and prepare the necessary data.
Build a regression model using appropriate techniques.
Interpret the model results and draw insights for decision-making.

Contribute to Open-Source Regression Libraries

Show steps

Contributing to open-source regression libraries will give you hands-on experience with real-world regression modeling tools.

Browse courses on Open Source

Show steps

Identify an open-source regression library that interests you.
Find a feature or bug that you can contribute to.
Fork the repository, make your changes, and submit a pull request.

Career center

Learners who complete Regression Analysis: Simplify Complex Data Relationships will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use statistical methods to collect, analyze, and interpret data. The Regression Analysis course can provide a comprehensive understanding of statistical modeling and hypothesis testing to aspiring Statisticians. By learning how to build and evaluate regression models, Statisticians can gain the skills to analyze data, draw inferences, and make informed conclusions, enhancing their ability to contribute to research and decision-making.

See salaries and explore the career path for Statistician

Market Researcher

Market Researchers collect, analyze, and interpret data to understand market trends and consumer behavior. The Regression Analysis course can be beneficial for individuals in this field as it provides a comprehensive understanding of statistical modeling techniques. By learning how to identify relationships between variables and build predictive models, Market Researchers can gain valuable insights into consumer behavior and market dynamics, enhancing their ability to make informed decisions and develop effective marketing strategies.

See salaries and explore the career path for Market Researcher

Data Analyst

Data Analysts examine and interpret data to extract meaningful insights. This course in Regression Analysis can help students develop foundational skills in statistical modeling and hypothesis testing, which are essential for success in this field. By learning how to build and evaluate regression models, learners can gain the ability to analyze complex data relationships and draw informed conclusions, a valuable skill for any Data Analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to understand business processes and identify opportunities for improvement. The Regression Analysis course can be valuable for Business Analysts as it provides a comprehensive understanding of statistical modeling and its application in business scenarios. By learning how to build and interpret regression models, Business Analysts can gain the skills to analyze data, identify key factors influencing business outcomes, and make data-driven recommendations for improvement.

See salaries and explore the career path for Business Analyst

Data Scientist

Data Scientists use advanced statistical techniques and machine learning algorithms to extract insights from data. The Regression Analysis course can provide a strong foundation for aspiring Data Scientists by introducing them to statistical modeling and its application in real-world scenarios. By learning how to build and evaluate regression models, Data Scientists can gain the skills to analyze data, identify patterns, and make predictions, increasing their value in the data science field.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Regression Analysis course can provide a solid foundation for aspiring Quantitative Analysts by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze financial data, identify patterns, and build predictive models, enhancing their ability to make informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to optimize business processes and improve decision-making. The Regression Analysis course can provide a strong foundation for aspiring Operations Research Analysts by introducing them to statistical modeling and its application in business scenarios. By learning how to build and interpret regression models, Operations Research Analysts can gain the skills to analyze data, identify key factors influencing business outcomes, and develop data-driven recommendations for improvement.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data analysis to evaluate and forecast financial performance. The Regression Analysis course can provide a strong foundation for aspiring Financial Analysts by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze financial data, identify trends, and make informed recommendations, increasing their value in the financial industry.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use statistical techniques to assess and manage risks. The Regression Analysis course can provide a strong foundation for aspiring Risk Analysts by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze data, identify patterns, and build predictive models, enhancing their ability to evaluate risks and make informed decisions.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess and manage financial risks. The Regression Analysis course can provide a strong foundation for aspiring Actuaries by introducing them to statistical modeling and hypothesis testing. By mastering these techniques, learners can develop the skills necessary to analyze financial data, identify patterns, and build predictive models, enhancing their ability to assess risks and make informed decisions.

See salaries and explore the career path for Actuary

Econometrician

Econometricians use statistical techniques to analyze economic data. The Regression Analysis course can provide a foundation for aspiring Econometricians by introducing them to statistical modeling and hypothesis testing. By learning how to build and evaluate regression models, Econometricians can develop the skills necessary to analyze economic data, identify relationships between economic variables, and draw informed conclusions.

See salaries and explore the career path for Econometrician

Biostatistician

Biostatisticians apply statistical methods to analyze biological and health-related data. The Regression Analysis course can provide a strong foundation for aspiring Biostatisticians by introducing them to statistical modeling and hypothesis testing. By learning how to build and interpret regression models, Biostatisticians can gain the skills to analyze data, identify relationships between health-related variables, and draw informed conclusions, enhancing their contributions to research and healthcare decision-making.

See salaries and explore the career path for Biostatistician

Epidemiologist

Epidemiologists investigate the distribution and determinants of health-related states and events in defined populations. The Regression Analysis course can provide a strong foundation for aspiring Epidemiologists by introducing them to statistical modeling and hypothesis testing. By learning how to build and interpret regression models, Epidemiologists can gain the skills to analyze data, identify factors influencing health outcomes, and draw informed conclusions, enhancing their ability to develop and implement public health interventions.

See salaries and explore the career path for Epidemiologist

Survey Researcher

Survey Researchers design, conduct, and analyze surveys to collect and analyze data. The Regression Analysis course can benefit Survey Researchers by providing them with a comprehensive understanding of statistical modeling and hypothesis testing. By learning how to build and interpret regression models, Survey Researchers can gain the skills to analyze survey data, identify relationships between variables, and draw informed conclusions, enhancing the quality and impact of their research.

See salaries and explore the career path for Survey Researcher

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. The Regression Analysis course may provide a useful foundation for aspiring Machine Learning Engineers by introducing them to statistical modeling and hypothesis testing. By learning how to build and evaluate regression models, Machine Learning Engineers can gain a better understanding of the underlying principles of machine learning algorithms and their application in real-world scenarios.

See salaries and explore the career path for Machine Learning Engineer