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Statistician with R

Master the essential skills to land a job as a 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

Data Evangelist 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

Data Evangelist at DataCamp

5
R
Sampling in R

Master sampling to get more accurate statistics with less data.

4 hours

Richie Cotton Headshot

Richie Cotton

Data Evangelist at DataCamp

6
R
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

Richie Cotton Headshot

Richie Cotton

Data Evangelist at DataCamp

7
R
Experimental Design in R

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

4 hours

Joanne Xiong Headshot

Joanne Xiong

Consultant & Data Analyst

8
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

9
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

10
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

11
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

12
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

13
R
Foundations of Inference in R

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

14
R
How Environmental Factors Influence Wildlife Populations

Explore habitat data using factor and survival analysis tools.

2 hours

Dr. Chester Ismay Headshot

Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

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Instructors

  • Maggie Matsui Headshot
    Maggie MatsuiCurriculum Manager at DataCampSee Maggie Matsui's Portfolio
  • David Robinson Headshot
    David RobinsonPrincipal Data Scientist at HeapSee David Robinson's Portfolio
  • Richie Cotton Headshot
    Richie CottonData Evangelist at DataCampSee Richie Cotton's Portfolio
  • Joanne Xiong Headshot
    Joanne XiongConsultant & Data AnalystSee Joanne Xiong's Portfolio

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