Regression Analysis: Simplify Complex Data Relationships
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Regression Analysis: Simplify Complex Data Relationships
This course is part of Google Advanced Data Analytics Professional Certificate
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599 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
Skills you'll gain
Tools you'll learn
Details to know
27 assignments
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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 videos•Total 39 minutes
- Introduction to Course 4 •5 minutes
- Tiffany: Gain actionable insights with regression models•3 minutes
- Welcome to module 1•2 minutes
- PACE in regression analysis •5 minutes
- Introduction to linear regression •9 minutes
- Mathematical linear regression •6 minutes
- Introduction to logistic regression•7 minutes
- Wrap-up•3 minutes
3 readings•Total 20 minutes
- Helpful resources and tips•8 minutes
- Course 4 overview•8 minutes
- Glossary terms from module 1•4 minutes
4 assignments•Total 68 minutes
- Module 1 challenge•50 minutes
- Test your knowledge: PACE in regression analysis•6 minutes
- Test your knowledge: Linear regression•8 minutes
- Test your knowledge: Logistic regression•4 minutes
2 plugins•Total 20 minutes
- Categorize: Linear and logistic regression•10 minutes
- [Turkish learners ONLY] Categorize: Linear and logistic regression - Türkçe•10 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 videos•Total 45 minutes
- Welcome to module 2•4 minutes
- Jerrod: The incredible value of mentorship•3 minutes
- Ordinary least squares estimation•5 minutes
- Make linear regression assumptions•5 minutes
- Explore linear regression with Python•10 minutes
- Evaluate uncertainty in regression analysis •5 minutes
- Model evaluation metrics•5 minutes
- Interpret and present linear regression results•6 minutes
- Wrap-up •2 minutes
8 readings•Total 56 minutes
- Explore ordinary least squares•8 minutes
- Correlation and the intuition behind simple linear regression•8 minutes
- The four main assumptions of simple linear regression•8 minutes
- Code functions and documentation•8 minutes
- Interpret measures of uncertainty in regression•8 minutes
- Evaluation metrics for simple linear regression •4 minutes
- Correlation versus causation: Interpret regression results•8 minutes
- Glossary terms from module 2 •4 minutes
5 assignments•Total 74 minutes
- Module 2 challenge•50 minutes
- Test your knowledge: Foundations of linear regression•6 minutes
- Test your knowledge: Assumptions and construction in Python •8 minutes
- Test your knowledge: Evaluate a linear regression model•6 minutes
- Test your knowledge: Interpret linear regression results•4 minutes
5 ungraded labs•Total 180 minutes
- Annotated follow-along guide: Explore linear regression with Python•20 minutes
- Activity: Run simple linear regression•60 minutes
- Exemplar: Run simple linear regression•20 minutes
- Activity: Evaluate simple linear regression•60 minutes
- Exemplar: Evaluate simple linear regression•20 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 videos•Total 47 minutes
- Welcome to module 3•4 minutes
- Introduction to multiple regression•4 minutes
- Represent categorical variables•6 minutes
- Make assumptions with multiple linear regressions•5 minutes
- Interpret multiple regression coefficients•6 minutes
- Interpret multiple regression results with Python•6 minutes
- The problem with overfitting•4 minutes
- Top variable selection methods•4 minutes
- Regularization: Lasso, Ridge, and Elastic Net regression•4 minutes
- Wrap-up•3 minutes
4 readings•Total 24 minutes
- Multiple linear regression scenarios•4 minutes
- Multiple linear regression assumptions and multicollinearity•8 minutes
- Underfitting and overfitting•8 minutes
- Glossary terms from module 3•4 minutes
5 assignments•Total 76 minutes
- Module 3 challenge•50 minutes
- Test your knowledge: Understand multiple linear regression•6 minutes
- Test your knowledge: Model assumptions revisited•8 minutes
- Test your knowledge: Model interpretation•4 minutes
- Test your knowledge: Variable selection and model evaluation•8 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along resource: Interpret multiple regression results with Python•20 minutes
- Activity: Perform multiple linear regression•60 minutes
- Exemplar: Perform multiple linear regression•20 minutes
2 plugins•Total 20 minutes
- Identify: Multiple regression assumptions•10 minutes
- [Turkish learners ONLY] Identify: Multiple regression assumptions - Türkçe•10 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 videos•Total 41 minutes
- Welcome to module 4 •4 minutes
- Hypothesis testing with chi-squared•6 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 Python•5 minutes
- Ignacio: Discovery at every stage of your career•3 minutes
- ANCOVA: Analysis of covariance •6 minutes
- More dependent variables: MANOVA and MANCOVA •5 minutes
- Wrap-up •2 minutes
3 readings•Total 16 minutes
- Chi-squared tests: Goodness of fit versus independence •8 minutes
- More about ANOVA•4 minutes
- Glossary terms from module 4•4 minutes
4 assignments•Total 68 minutes
- Module 4 challenge •50 minutes
- Test your knowledge: The chi-squared test•6 minutes
- Test your knowledge: Analysis of variance•6 minutes
- Test your knowledge: ANCOVA, MANOVA, and MANCOVA•6 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along guide: Explore one-way vs. two-way ANOVA tests with Python•20 minutes
- Activity: Hypothesis testing with Python•60 minutes
- Exemplar: Hypothesis testing with Python•20 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 videos•Total 35 minutes
- Welcome to module 5•3 minutes
- Find the best logistic regression model for your data•6 minutes
- Construct a logistic regression model with Python•4 minutes
- Evaluate a binomial logistic regression model•4 minutes
- Key metrics to assess logistic regression results•5 minutes
- Interpret the results of a logistic regression•6 minutes
- Answer questions with regression models•4 minutes
- Wrap-up •2 minutes
4 readings•Total 28 minutes
- Common logistic regression metrics in Python•8 minutes
- Interpret logistic regression models•8 minutes
- Prediction with different types of regression•8 minutes
- Glossary terms from module 5•4 minutes
5 assignments•Total 70 minutes
- Module 5 challenge•50 minutes
- Test your knowledge: Foundations of logistic regression•4 minutes
- Test your knowledge: Logistic regression with Python•6 minutes
- Test your knowledge: Interpret logistic regression results•6 minutes
- Test your knowledge: Compare regression models•4 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along resource: Construct a logistic regression model with Python•20 minutes
- Activity: Perform logistic regression•60 minutes
- Exemplar: Perform logistic regression•20 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 videos•Total 10 minutes
- Welcome to module 6•2 minutes
- Leah: Strategies for sharing models and modeling techniques •2 minutes
- Introduction to your Course 4 end-of-course portfolio project•1 minute
- End-of-course project wrap-up and tips for ongoing career success•2 minutes
- Course wrap-up•2 minutes
10 readings•Total 60 minutes
- Explore your Course 4 workplace scenarios•8 minutes
- Course 4 end-of-course portfolio project overview: Automatidata•8 minutes
- Activity Exemplar: Create your Course 4 Automatidata project•4 minutes
- Course 4 end-of-course portfolio project overview: TikTok•8 minutes
- Activity Exemplar: Create your Course 4 TikTok project•4 minutes
- Course 4 end-of-course portfolio project overview: Waze•8 minutes
- Activity Exemplar: Create your Course 4 Waze project•4 minutes
- Reflect and connect with peers•2 minutes
- Course 4 glossary•10 minutes
- Get started on the next course•4 minutes
4 assignments•Total 130 minutes
- Assess your Course 4 end-of-course project •40 minutes
- Activity: Create your Course 4 Automatidata project•30 minutes
- Activity: Create your Course 4 TikTok project•30 minutes
- Activity: Create your Course 4 Waze project•30 minutes
6 ungraded labs•Total 240 minutes
- Activity: Course 4 Automatidata project lab•60 minutes
- Exemplar: Course 4 Automatidata project lab•20 minutes
- Activity: Course 4 TikTok project lab•60 minutes
- Exemplar: Course 4 TikTok project lab•20 minutes
- Activity: Course 4 Waze project lab•60 minutes
- Exemplar: Course 4 Waze project lab•20 minutes
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Reviewed on Jul 12, 2023
Great course! Especially the statistical touch in it
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.
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.
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