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.

Simple linear regression

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.

Multiple linear regression

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.

Advanced hypothesis testing

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.

Logistic regression

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.

Course 5 end-of-course project

You’ll complete an end-of-course project by building a regression model to analyze a workplace scenario dataset.

Good to know

Know what's good

, what to watch for

, 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

Career center

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

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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