Want to join the many thousands of data professionals who use R to perform data analysis? This course will teach you the basics of how R works, and it will give you an introduction to how R contributes to the data analysis workflow. In this introductory course, we start from the very beginning — no prerequisites necessary. By the end of this course, you’ll be writing simple computer programs.
This course focuses on the following:
80% of our students learned Python or R from scratch with Dataquest. Join them today!
What You'll Learn
We’ll begin with an introduction to the fundamentals of working in R: syntax, data types, variables, and values. From there, we’ll proceed to using R for simple calculations, updating variables, and combining logical expressions.
At the end of the course, you’ll discover incredible R libraries in the Tidyverse package for simple data exploration and visualization. If some of this doesn’t make sense yet, don’t worry! By the end of these lessons, you’ll be an R fundamentals pro!
Introduction to Data Analysis in R provides absolute beginners with the knowledge and tools they need to start programming in R and gaining experience in data analysis. This course will get you started programming with R, from basic syntax to simple data analysis and visualization.
Enroll in this course if you want to learn how to do the following:
Statistical Programmer Analyst average $87k/yr per Glassdoor.
Facebook, Google, IBM, and other top tech firms rely on R, ListenData reports.
Per Indeed data analysts projected 20% growth between 2018 and 2028
How Our Intro to Data Analysis in R Course Works
The data scientist career path starts you out at the beginning, which means that it can be for anyone. There is no experience required to start.
This career path is for individuals who are ready for an exciting career switch, data professionals who are looking to advance in their field, college students pursuing data science who want to get job-ready, and more!
We created this course in response to the prevalence of R programming among top tech firms — and its versatility in data analysis. We want to help you position yourself to take advantage of the surge in demand for data jobs.
At Dataquest, we want you to retain the knowledge and skills you develop throughout our courses. That’s why we don’t teach using training videos. Instead, we craft all of our lessons to facilitate an interactive and hands-on learning experience. We believe that the best way to learn R is to code with R, and that’s exactly what you’ll do starting on day one!
This Intro to Data Analysis in R course is for beginners and requires no previous programming knowledge or experience.
On its own, this introductory course is a great learning opportunity. However, if you want the complete R programming learning experience, you can explore the various Paths that contain this course. If you’re learning R to pursue a career as a data analyst, check out our Data Analyst in R career path! Or maybe you simply want to build proficiency with R — you can enroll in the R Basics for Data Analysis skill path. There’s no right or wrong choice!
One of the many things our students prefer about Dataquest over other learning platforms is our guided projects. At the end of every course, you’ll complete a realistic project that applies all of the skills you learned from the previous lessons. They not only help you retain what you learned, but they also help you get hired! A strong portfolio that demonstrates your skills is precisely what hiring managers are looking for in qualified candidates.
Furthermore, once you join Dataquest, you’ll become a part of our vibrant and supportive community of data students and professionals ready to learn together. If you need help or simply want to share your success, our engaging community is just one click away.
Here’s a glance at our Introduction to Data Analysis in R course:
Enroll in this Course to learn Intro to Data Analysis in R!
Introduction to Data Analysis in R Course Lesson List
Introduction to Programming in R
Learn the basics of R, a popular programming language for data scientists.
Arithmetic Expressions and Variables in R
Learn more about the fundamentals of R syntax, how to assign and manipulate variables, and more.
Logical Expressions in R
Learn to write and work with logical expressions in your code.
Data Manipulation with R: Basics
Learn about the data analysis workflow in the context of R programming.
Guided Project: Install RStudio
Learn how to install and use RStudio, a free and open-source development environment for R.
Who Is this Introduction to Data Analysis in R Course for?
If you’re a beginner looking to get started or build a data analysis foundation, we’ve got you covered. Or, perhaps you’re someone more experienced who wants to brush up on the basics—you’re in the right place, too.
Here are just a few examples of people who can benefit from this course:
- Data science beginners looking for fundamental R programming knowledge
- People who want a career as a data analyst or R programmer
- People seeking remote work
- Anyone who works with data in telecommunication, finance, education, and healthcare
- Junior data analysts or R programmers who want to advance in their current position
- Students who want to develop a competitive portfolio
- R users who want to fill in the gaps and brush up on the basics
Students Who Enrolled in this Course Also Enrolled in the Following:
If you’re among the many thousands learning R for a potential career in data, we invite you to take advantage of any of the Paths below. We’ve crafted them to take you from absolute beginner to job-ready in under a year!
Qualify for In-demand Jobs in Data Science with R
R offers powerful tools for statistical analysis, and it has many applications in science, academia, and business. Additionally, many top tech firms like Facebook, Google, and IBM use R extensively. Proficiency in R can lead to your employment in any of the following positions:
- R programmer
- Data analyst
- Financial analyst
- Data scientist
- Data engineer
- Business analyst
- Technical Data Analyst
- Informatician
- R Shiny Developer
- GIS analyst
- Biostatistician
- Statistician
- Research data analyst
- Biotech analyst
