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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Statistician

R Language
56 hours
14 Courses

1

Introduction to Data

Learn the language of data, study types, sampling strategies, and experimental design.

4 hours Play preview

Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Pr...
2

Exploratory Data Analysis

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

4 hours Play preview

Andrew Bray

Assistant Professor of Statistics at Reed College
3

Modeling with Data in the Tidyverse

Explore Linear Regression in a tidy framework.

4 hours Play preview

Albert Y. Kim

Assistant Professor of Statistical & Data Sciences at Smith Co...
4

Correlation and Regression

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

4 hours Play preview

Ben Baumer

Assistant Professor at Smith College
5

Multiple and Logistic Regression

In this course you'll learn to add multiple variables to linear models and to use logistic regression for classificat...

4 hours Play preview

Ben Baumer

Assistant Professor at Smith College
6

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 Play preview

Jo Hardin

Professor at Pomona College
7

Foundations of Probability in R

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

4 hours Play preview

David Robinson

Chief Data Scientist, DataCamp
8

Dealing With Missing Data in R

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

4 hours

Nicholas Tierney

Statistician
9

Experimental Design in R

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

4 hours Play preview

kaelen medeiros

Data Scientist, Product at DataCamp
10

A/B Testing in R

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

4 hours

Page Piccinini

Senior Data Scientist at Classy
11

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

Senior Data Scientist at King (Activision Blizzard)
12

Linear Algebra for Data Science in R

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data sci...

4 hours

Eric Eager

Data Scientist at Pro Football Focus
13

Inference for Categorical Data

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

4 hours

Andrew Bray

Assistant Professor of Statistics at Reed College
14

Bayesian Modeling with RJAGS

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

4 hours

Alicia Johnson

Associate Professor, Macalester College
Statement of accomplishment

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Mine Cetinkaya-Rundel

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

Andrew Bray

Assistant Professor of Statistics at Reed College

Albert Y. Kim

Assistant Professor of Statistical & Data Sciences at Smith College

Assistant Professor at Smith College

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