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Google Career Certificates
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. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.
Read more
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. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.
Read more
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. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.
Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.
By the end of this course, learners will:
- Examine the benefits of using the R programming language.
- Discover how to use RStudio to apply R to your analysis.
- Explore the fundamental concepts associated with programming in R.
- Understand the contents and components of R packages including the Tidyverse package.
- Gain an understanding of dataframes and their use in R.
- Discover the options for generating visualizations in R.
- Learn about R Markdown for documenting R programming.
Register for this course and see more details by visiting:
OpenCourser.com/course/gph4ea/data
What's inside
Syllabus
Programming and data analytics
R is a programming language that can help you in your data analysis process. In this part of the course, you’ll learn about R and RStudio, the environment you’ll use to work in R. You’ll explore the benefits of using R and RStudio as well as the components of RStudio that will help you get started.
Read more
Syllabus
Programming and data analytics
R is a programming language that can help you in your data analysis process. In this part of the course, you’ll learn about R and RStudio, the environment you’ll use to work in R. You’ll explore the benefits of using R and RStudio as well as the components of RStudio that will help you get started.
Read more
Programming using RStudio
Using R can help you complete your analysis efficiently and effectively. In this part of the course, you’ll explore the fundamental concepts associated with R. You’ll learn about functions and variables for calculations and other programming. In addition, you'll discover R packages, which are collections of R functions, code and sample data that you’ll use in RStudio.
Working with data in R
The R programming language was designed to work with data at all stages of the data analysis process. In this part of the course, you’ll examine how R can help you structure, organize, and clean your data using functions and other processes. You’ll learn about data frames and how to work with them in R. You’ll also revisit the issue of data bias and how R can help.
More about visualizations, aesthetics, and annotations
R is a tool well-suited for creating detailed visualizations. In this part of the course, you’ll learn how to use R to generate and troubleshoot visualizations. You’ll also explore the features of R and RStudio that will help you with the aesthetics of your visualizations and for annotating and saving them.
Documentation and reports
When you’re ready to save and present your analysis, R has different options to consider. In this part of the course, you’ll explore R Markdown, a file format for making dynamic documents with R. You’ll find out how to format and export R Markdown, including how to incorporate R code chunks in your documents.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Intended for data science and spreadsheet users who want to enhance their analytic skills
Led by Google Data Analysts, who provide real-world insights and expertise
Develops practical skills in data cleaning, organization, analysis, visualization, and reporting
Introduces R programming and RStudio for efficient data analysis
Covers fundamental concepts of programming in R, including functions, variables, and packages
Provides hands-on experience in utilizing data frames, visualizations, and R Markdown
Save this course
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Reviews summary
R programming language analytics
learners say this Google Data Analytics course is largely positive. They describe it as well structured, thorough, beginner friendly, and practical. Many note that it's challenging but in a way that's helpful for building a strong foundation in the subject. Some say the instructor provides clear explanations and concise examples that make the content easier to understand. Others highlight the usefulness of the hands-on activities and practice exercises for reinforcing concepts and building confidence. A number of reviewers mention that the course materials and resources are comprehensive and helpful for further learning. Overall, this course seems to be well received by learners and may be a suitable option for those looking to gain a foundational understanding of R programming in the context of data analytics.
The instructor was great. Thumbs up to her. She made the course enjoyable and she always has a smile on her face. I have never learnt any programming language before. R is the first and I must say I was scared at first but each module with Carrie was exciting and I was eager to continue learning more.
"The instructor was great."
"Thumbs up to her."
"She made the course enjoyable and she always has a smile on her face."
"I have never learnt any programming language before."
"R is the first and I must say I was scared at first but each module with Carrie was exciting and I was eager to continue learning more."
This courses teaches how to "start using" R programming language. R is a very capable program when it comes to analytics, and one has to "do" a lot of R programming to start appreciating it's capabilities. That being said, this course did a fantastic Job of introduction.
"This courses teaches how to "start using" R programming language."
"R is a very capable program when it comes to analytics, and one has to "do" a lot of R programming to start appreciating it's capabilities."
"That being said, this course did a fantastic Job of introduction."
This was a well-structured and organized course on Data Analysis with R Programming from Google, providing a comprehensive introduction to the subject. It not only laid a solid foundation in R programming, but also reinforced the importance of asking the right questions and understanding the context of data analysis. The hands-on exercises, coupled with real-world examples, helped drive home the concepts and facilitated a practical understanding of the material. Furthermore, the course materials and additional resources shared throughout the course proved invaluable for further learning and reference, leaving me with the confidence to explore and apply my newly acquired skills in real-world scenarios.
"This was a well-structured and organized course on Data Analysis with R Programming from Google, providing a comprehensive introduction to the subject."
"It not only laid a solid foundation in R programming, but also reinforced the importance of asking the right questions and understanding the context of data analysis."
"The hands-on exercises, coupled with real-world examples, helped drive home the concepts and facilitated a practical understanding of the material."
"Furthermore, the course materials and additional resources shared throughout the course proved invaluable for further learning and reference, leaving me with the confidence to explore and apply my newly acquired skills in real-world scenarios."
Overall I was pleased with the course, the instruction and the hands on practice. I thought R was more intuitive a choice than python, and I'm really glad I got exposed to it. I found the language itself to be useful in manipulating and working with data, and R studio to be pretty intuitive as well. The hands on activities were also very good. I would recommend this course to anyone who wants to start learning R.
This course, together with the one on spreadsheets and SQL, was the best in the bundle. The one setback that I had (that I wish the course had included a workaround) was that I could not install RStudio Desktop for some reason and that conditioned me a bit because of the time-cap on Rstudio Cloud.
"This course, together with the one on spreadsheets and SQL, was the best in the bundle."
"The one setback that I had (that I wish the course had included a workaround) was that I could not install RStudio Desktop for some reason and that conditioned me a bit because of the time-cap on Rstudio Cloud."
Activities
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in Data Analysis with R Programming with these
activities:
Review: Basic statistics
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Refresh your memory on basic statistical concepts.
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Statistics
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Review your notes
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Guided tutorial: Getting started with RStudio
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Go to the course page
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Practice exercises: Functions and variables in R
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Practice working with functions and variables in R.
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Write a report
Blog post: How I used R to solve a real-world problem
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Come up with a problem
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Choose a topic
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Kaggle competition: Titanic
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Create a Kaggle account
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Join the competition
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Download the data
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Build a model
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Submit your results
Review: Basic statistics
Show steps
Refresh your memory on basic statistical concepts.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes
- Take a practice quiz
Guided tutorial: Getting started with RStudio
Show steps
Get up and running with the environment you will be using for coding in R.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Log into Coursera
- Go to the course page
- Find the guided tutorial
- Complete the guided tutorial
Practice exercises: Functions and variables in R
Show steps
Practice working with functions and variables in R.
Browse courses on
Functions
Show steps
Show steps
Show steps
- Log into Coursera
- Go to the course page
- Find the practice exercises
- Complete the practice exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review the book: The Art of R Programming
Show steps
Expand your knowledge of R programming.
View
The Art of Machine Learning: A Hands-On Guide...
on Amazon
Show steps
Show steps
Show steps
- Purchase a copy of the book
- Read the book
- Take notes
- Complete the review
Project: Data analysis using R
Show steps
Get practice and apply your learnings in a practical setting.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Log into Coursera
- Go to the course page
- Find the project
- Download the data
- Write a report
Blog post: How I used R to solve a real-world problem
Show steps
Further develop your understanding of R and practice writing about your findings.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Come up with a problem
- Collect data
- Analyze data
- Write a blog post
Presentation: Data analysis with R
Show steps
Practice communicating your findings to others.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a topic
- Collect data
- Analyze data
- Create slides
- Practice your delivery
Kaggle competition: Titanic
Show steps
Challenge yourself and practice your skills in a competitive setting.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Create a Kaggle account
- Join the competition
- Download the data
- Build a model
- Submit your results
Career center
Learners who complete Data Analysis with R Programming will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst studies data to extract insights and trends, summarizing and visualizing data to aid in decision-making. With R's robust data analysis and visualization capabilities, alongside the training and examples taught in this course, a Data Analyst can leverage R to effectively analyze data, generate meaningful insights, and present them visually. This hands-on course will equip you to apply for and succeed in this career.
Data Scientist
Data Scientist
A Data Scientist uses statistical and computational techniques to analyze and interpret data, often to build machine learning models. R is extensively used by Data Scientists for data analysis and visualization. This course can equip aspiring Data Scientists with a solid foundation in R, enabling them to effectively work with data and derive meaningful insights, enhancing their competitiveness in this field.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist creates visual representations of data to communicate insights and trends. R has emerged as a powerful tool for data visualization, and this course provides a comprehensive introduction to the language for aspiring Data Visualization Specialists. By mastering R, they can enhance their ability to create engaging and informative visualizations, effectively conveying complex data-driven stories to audiences.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst applies mathematical and statistical techniques to analyze data for financial decision-making. This course provides an excellent opportunity for individuals seeking to enter this field by providing a comprehensive introduction to R, a popular programming language in quantitative finance. Understanding R's capabilities and applications will empower aspiring Quantitative Analysts to handle data more efficiently and uncover valuable insights.
Business Analyst
Business Analyst
A Business Analyst identifies and analyzes an organization's business needs and processes to facilitate improvements. This course can provide a solid foundation for a Business Analyst hoping to leverage data analysis and visualization to better understand business processes and make recommendations for enhancements. The R programming language, with its data analysis and visualization capabilities, can be a valuable tool for Business Analysts seeking to advance their careers.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. R is a widely used programming language among Statisticians for its statistical functions and data visualization capabilities. This course provides Statisticians with a solid foundation in R, enhancing their ability to handle complex statistical analyses, build statistical models, and communicate their findings effectively.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations and evaluate the performance of companies. This course provides a solid foundation for aspiring Financial Analysts by introducing them to R, a powerful tool for financial data analysis and visualization. By leveraging R's capabilities, Financial Analysts can enhance their ability to analyze financial statements, build models, and make informed investment decisions.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data systems and infrastructure. R is increasingly used in data engineering for data cleaning, transformation, and analysis tasks. This course provides Data Engineers with a solid foundation in R, equipping them to contribute more effectively to the data engineering lifecycle and manage the increasing volume and complexity of data.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve complex business problems. R is increasingly used in operations research for data analysis and optimization tasks. This course provides Operations Research Analysts with a valuable skillset in R, equipping them to leverage data more effectively and develop innovative solutions to business challenges.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to analyze and interpret data in the field of healthcare. R has gained prominence in biostatistics, and this course provides a solid introduction to the language for aspiring Biostatisticians. By completing the course, they will gain proficiency in R's statistical functions and data visualization capabilities, enabling them to analyze and interpret health-related data more effectively.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. R is increasingly used in software engineering for data analysis, visualization, and modeling tasks. This course provides Software Engineers with a valuable skillset in R, enabling them to enhance their ability to work with data, develop data-driven applications, and stay competitive in the tech industry.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of health and disease in populations. R is widely used in epidemiology for data analysis and visualization. This course provides Epidemiologists with a valuable opportunity to enhance their skills in R, empowering them to analyze epidemiological data more effectively, identify risk factors, and develop strategies for disease prevention and control.
Market Researcher
Market Researcher
A Market Researcher gathers and analyzes data to understand market trends and customer behaviors. This course can provide Market Researchers with a valuable skillset in R for data analysis and visualization. By learning R, Market Researchers can enhance their ability to uncover insights from market data, identify trends, and make better recommendations.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and manages risks within an organization. R is gaining popularity in risk analysis for its data analysis and modeling capabilities. This course provides Risk Analysts with a solid foundation in R, enabling them to leverage data more effectively, develop risk models, and make informed decisions to mitigate risks.
Actuary
Actuary
An Actuary analyzes and manages financial risks using mathematical and statistical techniques. R is gaining popularity in the actuarial field for its data analysis and modeling capabilities. This course can provide aspiring Actuaries with a strong foundation in R, enabling them to stay competitive in the job market and effectively contribute to risk assessment and management.
For more career information including salaries, visit:
OpenCourser.com/course/gph4ea/data
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Analysis with R Programming.
Save
Is the second edition of the best-selling guide to data science using R. It provides comprehensive coverage of R and its ecosystem, including data manipulation, graphics, and statistical modeling.
Classic introduction to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
More advanced treatment of statistical learning. It covers topics such as Bayesian methods, support vector machines, and boosting.
Save
Comprehensive guide to the R programming language. It covers everything from basic data types and structures to advanced statistical modeling.
Save
More advanced treatment of R programming. It covers topics such as data structures, object-oriented programming, and functional programming.
Practical guide to using R for data analysis. It covers a wide range of topics, including data visualization, machine learning, and statistical modeling.
Practical guide to using R for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection.
Save
Collection of recipes for solving common problems in R. It covers a wide range of topics, including data manipulation, visualization, and statistical modeling.
Practical guide to using R for deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Save
Beginner-friendly guide to using R for data analysis. It covers the basics of data manipulation, visualization, and statistical modeling.
Save
Is the definitive guide to using R Markdown. It covers everything from basic syntax to advanced features.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gph4ea/data
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Google Career Certificates
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Good to know
Intended for data science and spreadsheet users who want to enhance their analytic skills
Led by Google Data Analysts, who provide real-world insights and expertise
Develops practical skills in data cleaning, organization, analysis, visualization, and reporting
Introduces R programming and RStudio for efficient data analysis
Covers fundamental concepts of programming in R, including functions, variables, and packages
Provides hands-on experience in utilizing data frames, visualizations, and R Markdown
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