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career track

Statistician with R

Master the essential skills to land a job as statistician! Using statistics, you can help solve real-world problems in business, engineering, the sciences, and many other fields. In this track, you'll learn how to use statistical methods to explore and model data, draw conclusions from a wide variety of datasets, and interpret and report findings.

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Loved by learners at thousands of companies

1
R
Introduction to Statistics in R

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

4 hours

Maggie Matsui Headshot

Maggie Matsui

Curriculum Manager at DataCamp

2
R
Foundations of Probability in R

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

4 hours

David Robinson Headshot

David Robinson

Principal Data Scientist at Heap

3
R
Introduction to Regression in R

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

4 hours

Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp

4
R
Intermediate Regression in R

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

4 hours

Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp

5
R
Generalized Linear Models in R

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

4 hours

Richard Erickson Headshot

Richard Erickson

Data Scientist

6
R
Modeling with Data in the Tidyverse

Explore Linear Regression in a tidy framework.

4 hours

Albert Y. Kim Headshot

Albert Y. Kim

Assistant Professor of Statistical & Data Sciences

7
R
Sampling in R

Master sampling to get more accurate statistics with less data.

4 hours

Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp

8
R
Hypothesis Testing in R

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.

4 hours

Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp

9
R
Experimental Design in R

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

4 hours

kaelen medeiros Headshot

kaelen medeiros

Data Scientist

10
R
A/B Testing in R

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

4 hours

DataCamp Content Creator

Course Instructor

11
R
Dealing With Missing Data in R

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

4 hours

12
R
Handling Missing Data with Imputations in R

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

4 hours

Michał Oleszak Headshot

Michał Oleszak

Machine Learning Engineer

13
R
Analyzing Survey Data in R

Learn survey design using common design structures followed by visualizing and analyzing survey results.

4 hours

Kelly McConville Headshot

Kelly McConville

Assistant Professor of Statistics at Reed College

14
R
Survey and Measurement Development in R

Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.

4 hours

George Mount Headshot

George Mount

Data analytics educator

15
R
Hierarchical and Mixed Effects Models in R

In this course you will learn to fit hierarchical models with random effects.

4 hours

Richard Erickson Headshot

Richard Erickson

Data Scientist

16
R
Survival Analysis in R

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

4 hours

Heidi Seibold Headshot

Heidi Seibold

Statistician / Research & Education Ambassador, IGDORE

17
R
Mixture Models in R

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

4 hours

18
R
Fundamentals of Bayesian Data Analysis in R

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

4 hours

Rasmus Bååth Headshot

Rasmus Bååth

Data Science Lead at castle.io

19
R
Bayesian Regression Modeling with rstanarm

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

4 hours

Jake Thompson Headshot

Jake Thompson

Psychometrician, ATLAS, University of Kansas

20
R
Bayesian Modeling with RJAGS

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

4 hours

Alicia Johnson Headshot

Alicia Johnson

Associate Professor, Macalester College

21
R
Factor Analysis in R

Explore latent variables, such as personality using exploratory and confirmatory factor analyses.

4 hours

Jennifer Brussow Headshot

Jennifer Brussow

Psychometrician at Ascend Learning

22
R
Structural Equation Modeling with lavaan in R

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

4 hours

Erin Buchanan Headshot

Erin Buchanan

Professor at Harrisburg University of Science and Technology

23
R
Foundations of Inference

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

4 hours

Jo Hardin Headshot

Jo Hardin

Professor at Pomona College

24
R
Inference for Categorical Data in R

In this course you'll learn how to leverage statistical techniques for working with categorical data.

4 hours

Andrew Bray Headshot

Andrew Bray

Assistant Professor of Statistics at Reed College

25
R
Inference for Numerical Data in R

In this course you'll learn techniques for performing statistical inference on numerical data.

4 hours

Mine Cetinkaya-Rundel Headshot

Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio

26
R
Inference for Linear Regression in R

In this course you'll learn how to perform inference using linear models.

4 hours

Jo Hardin Headshot

Jo Hardin

Professor at Pomona College

27
R
Data Privacy and Anonymization in R

Publicly release data sets with a differential privacy guarantee.

4 hours

Claire Bowen Headshot

Claire Bowen

Postdoctoral Researcher at the Los Alamos National Laboratory

Instructors