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.
Regression Analysis: Simplify Complex Data Relationships
Regression Analysis: Simplify Complex Data Relationships
本课程是 Google Advanced Data Analytics 专业证书 的一部分
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您将学到什么
Investigate relationships in datasets
Identify regression model assumptions
Perform linear and logistic regression using Python
Practice model evaluation and interpretation
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要了解的详细信息
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作业
27 项作业
授课语言：英语（English）
积累 Data Analysis 领域的专业知识
本课程是 Google Advanced Data Analytics 专业证书 专项课程的一部分
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该课程共有6个模块
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.
涵盖的内容
8个视频3篇阅读材料4个作业2个插件
8个视频•总计39分钟
- Introduction to Course 4 •5分钟
- Tiffany: Gain actionable insights with regression models•3分钟
- Welcome to module 1•2分钟
- PACE in regression analysis •5分钟
- Introduction to linear regression •9分钟
- Mathematical linear regression •6分钟
- Introduction to logistic regression•7分钟
- Wrap-up•3分钟
3篇阅读材料•总计20分钟
- Helpful resources and tips•8分钟
- Course 4 overview•8分钟
- Glossary terms from module 1•4分钟
4个作业•总计68分钟
- Test your knowledge: PACE in regression analysis•6分钟
- Test your knowledge: Linear regression•8分钟
- Test your knowledge: Logistic regression•4分钟
- Module 1 challenge•50分钟
2个插件•总计20分钟
- Categorize: Linear and logistic regression•10分钟
- [Turkish learners ONLY] Categorize: Linear and logistic regression - Türkçe•10分钟
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.
涵盖的内容
9个视频8篇阅读材料5个作业5个非评分实验室
9个视频•总计45分钟
- Welcome to module 2•4分钟
- Jerrod: The incredible value of mentorship•3分钟
- Ordinary least squares estimation•5分钟
- Make linear regression assumptions•5分钟
- Explore linear regression with Python•10分钟
- Evaluate uncertainty in regression analysis •5分钟
- Model evaluation metrics•5分钟
- Interpret and present linear regression results•6分钟
- Wrap-up •2分钟
8篇阅读材料•总计56分钟
- Explore ordinary least squares•8分钟
- Correlation and the intuition behind simple linear regression•8分钟
- The four main assumptions of simple linear regression•8分钟
- Code functions and documentation•8分钟
- Interpret measures of uncertainty in regression•8分钟
- Evaluation metrics for simple linear regression •4分钟
- Correlation versus causation: Interpret regression results•8分钟
- Glossary terms from module 2 •4分钟
5个作业•总计74分钟
- Test your knowledge: Foundations of linear regression•6分钟
- Test your knowledge: Assumptions and construction in Python •8分钟
- Test your knowledge: Evaluate a linear regression model•6分钟
- Test your knowledge: Interpret linear regression results•4分钟
- Module 2 challenge•50分钟
5个非评分实验室•总计180分钟
- Annotated follow-along guide: Explore linear regression with Python•20分钟
- Activity: Run simple linear regression•60分钟
- Exemplar: Run simple linear regression•20分钟
- Activity: Evaluate simple linear regression•60分钟
- Exemplar: Evaluate simple linear regression•20分钟
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.
涵盖的内容
10个视频4篇阅读材料5个作业3个非评分实验室2个插件
10个视频•总计47分钟
- Welcome to module 3•4分钟
- Introduction to multiple regression•4分钟
- Represent categorical variables•6分钟
- Make assumptions with multiple linear regressions•5分钟
- Interpret multiple regression coefficients•6分钟
- Interpret multiple regression results with Python•6分钟
- The problem with overfitting•4分钟
- Top variable selection methods•4分钟
- Regularization: Lasso, Ridge, and Elastic Net regression•4分钟
- Wrap-up•3分钟
4篇阅读材料•总计24分钟
- Multiple linear regression scenarios•4分钟
- Multiple linear regression assumptions and multicollinearity•8分钟
- Underfitting and overfitting•8分钟
- Glossary terms from module 3•4分钟
5个作业•总计76分钟
- Test your knowledge: Understand multiple linear regression•6分钟
- Test your knowledge: Model assumptions revisited•8分钟
- Test your knowledge: Model interpretation•4分钟
- Test your knowledge: Variable selection and model evaluation•8分钟
- Module 3 challenge•50分钟
3个非评分实验室•总计100分钟
- Annotated follow-along resource: Interpret multiple regression results with Python•20分钟
- Activity: Perform multiple linear regression•60分钟
- Exemplar: Perform multiple linear regression•20分钟
2个插件•总计20分钟
- Identify: Multiple regression assumptions•10分钟
- [Turkish learners ONLY] Identify: Multiple regression assumptions - Türkçe•10分钟
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.
涵盖的内容
9个视频3篇阅读材料4个作业3个非评分实验室
9个视频•总计41分钟
- Welcome to module 4 •4分钟
- Hypothesis testing with chi-squared•6分钟
- Introduction to the analysis of variance •5分钟
- Explore one-way vs. two-way ANOVA tests with Python •5分钟
- ANOVA post hoc tests with Python•5分钟
- Ignacio: Discovery at every stage of your career•3分钟
- ANCOVA: Analysis of covariance •6分钟
- More dependent variables: MANOVA and MANCOVA •5分钟
- Wrap-up •2分钟
3篇阅读材料•总计16分钟
- Chi-squared tests: Goodness of fit versus independence •8分钟
- More about ANOVA•4分钟
- Glossary terms from module 4•4分钟
4个作业•总计68分钟
- Test your knowledge: The chi-squared test•6分钟
- Test your knowledge: Analysis of variance•6分钟
- Test your knowledge: ANCOVA, MANOVA, and MANCOVA•6分钟
- Module 4 challenge •50分钟
3个非评分实验室•总计100分钟
- Annotated follow-along guide: Explore one-way vs. two-way ANOVA tests with Python•20分钟
- Activity: Hypothesis testing with Python•60分钟
- Exemplar: Hypothesis testing with Python•20分钟
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.
涵盖的内容
8个视频4篇阅读材料5个作业3个非评分实验室
8个视频•总计35分钟
- Welcome to module 5•3分钟
- Find the best logistic regression model for your data•6分钟
- Construct a logistic regression model with Python•4分钟
- Evaluate a binomial logistic regression model•4分钟
- Key metrics to assess logistic regression results•5分钟
- Interpret the results of a logistic regression•6分钟
- Answer questions with regression models•4分钟
- Wrap-up •2分钟
4篇阅读材料•总计28分钟
- Common logistic regression metrics in Python•8分钟
- Interpret logistic regression models•8分钟
- Prediction with different types of regression•8分钟
- Glossary terms from module 5•4分钟
5个作业•总计70分钟
- Test your knowledge: Foundations of logistic regression•4分钟
- Test your knowledge: Logistic regression with Python•6分钟
- Test your knowledge: Interpret logistic regression results•6分钟
- Test your knowledge: Compare regression models•4分钟
- Module 5 challenge•50分钟
3个非评分实验室•总计100分钟
- Annotated follow-along resource: Construct a logistic regression model with Python•20分钟
- Activity: Perform logistic regression•60分钟
- Exemplar: Perform logistic regression•20分钟
You’ll complete an end-of-course project by building a regression model to analyze a workplace scenario dataset.
涵盖的内容
5个视频10篇阅读材料4个作业6个非评分实验室
5个视频•总计10分钟
- Welcome to module 6•2分钟
- Leah: Strategies for sharing models and modeling techniques •2分钟
- Introduction to your Course 4 end-of-course portfolio project•1分钟
- End-of-course project wrap-up and tips for ongoing career success•2分钟
- Course wrap-up•2分钟
10篇阅读材料•总计60分钟
- Explore your Course 4 workplace scenarios•8分钟
- Course 4 end-of-course portfolio project overview: Automatidata•8分钟
- Activity Exemplar: Create your Course 4 Automatidata project•4分钟
- Course 4 end-of-course portfolio project overview: TikTok•8分钟
- Activity Exemplar: Create your Course 4 TikTok project•4分钟
- Course 4 end-of-course portfolio project overview: Waze•8分钟
- Activity Exemplar: Create your Course 4 Waze project•4分钟
- Reflect and connect with peers•2分钟
- Course 4 glossary•10分钟
- Get started on the next course•4分钟
4个作业•总计130分钟
- Activity: Create your Course 4 Automatidata project•30分钟
- Activity: Create your Course 4 TikTok project•30分钟
- Activity: Create your Course 4 Waze project•30分钟
- Assess your Course 4 end-of-course project •40分钟
6个非评分实验室•总计240分钟
- Activity: Course 4 Automatidata project lab•60分钟
- Exemplar: Course 4 Automatidata project lab•20分钟
- Activity: Course 4 TikTok project lab•60分钟
- Exemplar: Course 4 TikTok project lab•20分钟
- Activity: Course 4 Waze project lab•60分钟
- Exemplar: Course 4 Waze project lab•20分钟
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自 2018开始学习的学生
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自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
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''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
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学生评论
4.7
597 条评论
- 5 stars
81.74%
- 4 stars
10.21%
- 3 stars
4.18%
- 2 stars
1.67%
- 1 star
2.17%
显示 3/597 个
B
BT
5·
已于 Apr 13, 2023审阅
very good course, but this course is the most difficult for me
I
IC
5·
已于 Feb 15, 2024审阅
good instructor, slightly complicated notebooks, too much of new information and formulas at once, regression analysis is very powerful technique
J
JG
5·
已于 Apr 23, 2024审阅
too good excellent don't hesitate to buy this course
常见问题
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.
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