Data Scientist with R

Data Scientist with R

Gain the career-building R skills you need to succeed as a data scientist. No prior coding experience required. In this track, you’ll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you’ll get hands-on with some of the most popular R packages, including ggplot2 and tidyverse packages like dplyr and readr. You’ll then work with real-world datasets to learn the statistical and machine learning techniques you need to write your own functions and perform cluster analysis. Start this track, grow your R skills, and begin your journey to becoming a confident data scientist.

R95 hours24 Courses

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Introduction to R

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

4 hours

Intermediate R

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

6 hours

Introduction to the Tidyverse

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

4 hours

Data Manipulation with dplyr

Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.

4 hours

Joining Data with dplyr

Learn to combine data across multiple tables to answer more complex questions with dplyr.

4 hours

Introduction to Statistics in R

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

4 hours

Introduction to Data Visualization with ggplot2

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

4 hours

Intermediate Data Visualization with ggplot2

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

4 hours

Data Manipulation with R

Reporting with R Markdown

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

4 hours

Data Communication Concepts

No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.

3 hours

Introduction to Importing Data in R

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

3 hours

Intermediate Importing Data in R

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

3 hours

Cleaning Data in R

Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.

4 hours

Working with Dates and Times in R

Learn the essentials of parsing, manipulating and computing with dates and times in R.

4 hours

Importing & Cleaning Data with R

Introduction to Writing Functions in R

Take your R skills up a notch by learning to write efficient, reusable functions.

4 hours

R Programming

Exploratory Data Analysis in R

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

4 hours

Case Study: Exploratory Data Analysis in R

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

4 hours

Introduction to Regression in R

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

4 hours

Intermediate Regression in R

Learn to perform linear and logistic regression with multiple explanatory variables.

4 hours

Sampling in R

Master sampling to get more accurate statistics with less data.

4 hours

Hypothesis Testing in R

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

4 hours

Statistics Fundamentals with R

Supervised Learning in R: Classification

In this course you will learn the basics of machine learning for classification.

4 hours

Supervised Learning in R: Regression

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

4 hours

Unsupervised Learning in R

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

4 hours

Loved by learners at thousands of companies

Instructors

Join over .css-1wxderj-TracksPage{color:#03ef62;}10,780,000 learners and start Data Scientist with R today!

Create Your Free Account

Join over 10,780,000 learners and start Data Scientist with R today!