Career Path

Data Analyst in R

Learn how to manipulate and analyze data.

This path covers everything you need to learn to work as a data analyst using R.

You'll learn the fundamentals of R syntax, dig into data analysis and data viz using popular tidyverse packages, query databases with SQL, and study statistics, among other things!

Each path is designed so that there are no prerequisites and no prior experience required. Everything you need to learn to work as a data analyst, you'll learn on this path!

As you learn, you'll apply each concept immediately by writing code right in your browser that's automatically checked by our system to give you near-instant feedback on your progress.

We think the best way to learn is to learn by doing, so you'll be challenged every step of the way to really apply the concepts you're learning, and you'll build a variety of projects using real-world data to solve real data analysis problems.

By the end of this path, you'll have the skills you need to work as a data analyst, and you'll be comfortable with things like:

  • Basic and intermediate programming concepts
  • Modern R workflows with RStudio and tidyverse packages
  • Probability and statistics for data analysis
  • How to clean and visualize data
  • Collaboration tools like git and SQL databases
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Learn Data Analysis with R

Introduction to Data Analysis in R

Learn the basics of R, a popular programming language for data analysis.

Data Structures in R

Learn about vectors, matrices, lists, dataframes, and more in R.

Control Flow, Iteration, and Functions in R

Enhance your R programming skills with if statements, for loops, and much more.

Specialized Data Processing in R: Strings and Dates

Learn to work with specialized data types like text data, times, and dates in R.

Data Visualization in R

Learn to use the ggplot2 package for exploratory data visualization in R.

Data Cleaning in R

Learn to perform common data cleaning tasks.

Storytelling Through Data Visualization in R

Learn how to communicate insights and tell stories using data visualization.

Data Cleaning in R: Advanced

Learn advanced techniques for cleaning data in R.

SQL Fundamentals

Learn the basics of working with SQL databases.

SQL Intermediate in R

Learn to work with multi-table databases.

APIs in R

Learn how to acquire data from APIs and the web.

Web Scraping in R

Learn how to acquire data from APIs and the web.

Statistics Fundamentals for R Users

Learn the basics of statistics.

Statistics Intermediate in R: Averages and Variability

Learn some intermediate statistic techniques such as calculating z-scores.

Probability Fundamentals for R Users

Learn the fundamentals of probability for data science.

Hypothesis Testing in R

Learn the fundamentals of hypothesis testing using R

Conditional Probability in R

Learn about conditional probability and Naive Bayes in R

Linear Modeling in R

Learn linear regression modeling in R.

Introduction to Machine Learning

Learn the fundamentals of machine learning with R and the caret library.

Introduction to Shiny in R

Learn how to make interactive web-based data apps with R and Shiny.


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