Generative AI for Business Leaders
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How Google does Machine Learning
Our Courses
R Programming and Tidyverse Capstone Project
In this third and final course of the "Expressway to Data Science: R Programming and Tidyverse" specialization you will reinforce and display your R and tidyverse skills by completing an analysis of COVID-19 data! Here is a chance to apply your skills to a real-world dataset that has effected all of us. Throughout the capstone, you will import COVID-19 data; clean, tidy, and join datasets; and develop visualizations. You will also provide some analysis and interpretation to your results, preparing you for your journey into data science.
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Course by
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Self Paced
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10 hours
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English
Data Analysis with R Programming
This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R, and the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways.
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Course by
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Self Paced
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37 hours
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English
The Data Scientist's Toolbox
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
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Course by
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Self Paced
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18 hours
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English
Introduction to Probability and Data with R
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project.
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Course by
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Self Paced
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14 hours
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English
Linear Regression and Modeling
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother?
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Course by
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Self Paced
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10 hours
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English
Introduction to Business Analytics with R
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations.
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Course by
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Self Paced
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17 hours
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English