Data Analysis with R Programming from Coursera

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.

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.

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Foundational r for data analytics

According to learners, this course offers a solid and practical introduction to R programming, particularly for those pursuing a career in data analysis. Students appreciate the hands-on approach and the integration with RStudio and the Tidyverse package, which are crucial for modern data analysis workflows. While it provides a strong foundation for beginners, some more experienced students suggest that the course might be too basic for intermediate learners or those seeking advanced statistical techniques. The clear explanations and practical exercises are frequently highlighted as strengths, making it accessible even for those with no prior programming experience. The inclusion of R Markdown for reporting is also seen as a valuable takeaway.

Valuable section on reproducible reporting.

"The module on R Markdown was a game-changer for me; I can now create dynamic, reproducible reports effortlessly."

"Learning to use R Markdown for documentation is a skill I immediately started using in my personal projects."

"It's great that the course covers reporting. Being able to combine code, output, and text is very powerful."

Equips learners for entry-level data analyst roles.

"Completing this course made me feel much more confident in applying for entry-level data analyst positions."

"It teaches the practical skills needed to handle real-world data analysis tasks, which is what employers look for."

"The final projects and R Markdown documentation skills are directly transferable to a professional portfolio."

Focuses on essential R, RStudio, and Tidyverse skills.

"The hands-on activities using RStudio and the Tidyverse packages were incredibly useful and directly applicable."

"I learned how to apply R for cleaning, organizing, and visualizing data effectively, which is what I needed for my job."

"The course introduces key R packages and functions that are industry standards, making the learning highly relevant."

Excellent for newcomers to R and data analysis concepts.

"This course provided a really solid foundation in R for someone like me who had no prior programming experience."

"I found the explanations incredibly clear and easy to follow, even for complex topics. It's perfect for beginners."

"It truly lives up to its promise of requiring no previous experience, making R accessible to everyone."

Some find the course too basic or fast-paced at times.

"For someone already familiar with basic programming, some parts felt a bit slow, but it's great for absolute beginners."

"I found the course quite fast-paced in some sections, requiring me to rewatch videos and do extra practice."

"It provides a great overview, but I had to seek external resources for more in-depth understanding of certain statistical concepts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Analysis with R Programming with these activities:

Review: Basic statistics

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Refresh your memory on basic statistical concepts.

Browse courses on Statistics

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Review your notes
Take a practice quiz

Guided tutorial: Getting started with RStudio

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Get up and running with the environment you will be using for coding in R.

Browse courses on R Programming

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Log into Coursera
Go to the course page
Find the guided tutorial
Complete the guided tutorial

Practice exercises: Functions and variables in R

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Practice working with functions and variables in R.

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Log into Coursera
Go to the course page
Find the practice exercises
Complete the practice exercises

Five other activities

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Review the book: The Art of R Programming

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Expand your knowledge of R programming.

View The Art of Machine Learning: A Hands-On Guide... on Amazon

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Purchase a copy of the book
Read the book
Take notes
Complete the review

Project: Data analysis using R

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Get practice and apply your learnings in a practical setting.

Browse courses on Data Analysis

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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

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Further develop your understanding of R and practice writing about your findings.

Browse courses on Data Analysis

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Come up with a problem
Collect data
Analyze data
Write a blog post

Presentation: Data analysis with R

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Practice communicating your findings to others.

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Choose a topic
Collect data
Analyze data
Create slides
Practice your delivery

Kaggle competition: Titanic

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Challenge yourself and practice your skills in a competitive setting.

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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

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Visualization Specialist

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.

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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.

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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.

See salaries and explore the career path for Statistician

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.

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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.

See salaries and explore the career path for Data Engineer

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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Risk Analyst

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.

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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.

See salaries and explore the career path for Actuary