Skip to content

Introduction to statistics using R and Rstudio

  • Level(s) of Study: Short course
  • Start Date(s): To be confirmed
  • Duration: 9:30 am – 5:30 pm
  • Study Mode(s): Short course
  • Campus: City Campus
  • Entry Requirements:
    More information


In this two day course, you will obtain a comprehensive introduction to R and RStudio and how it can be used for data science and statistics in an academic or professional setting.

This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Level: CPD, Advanced / Professional

The course will cover these key topics:

  • The ‘what and why of R’; what is used for, and why has it become so popular?
  • RStudio – a tour of the most widely used interface to R, all its features and how to use it effectively
  • Fundamentals of R and the R environment, including variables and assignment, data structures (such as vectors and data frames), operations on data structures, functions, scripts, installing and loading packages, using RStudio projects and reading in data
  • Data wrangling; the art of cleaning and restructuring data (focusing on filtering, slicing, selecting, renaming, and mutating data frames)
  • Data visualization with an introduction to ggplot, scatterplots, boxplots and histograms
  • RMarkdown, a powerful tool for creating reproducible research reports, as well as slides, scientific website and posters
  • An introduction to statistics using R (linear regression, anova, and some other simple tests).

The course will take 6 contact hours per day plus two 2-hour breaks.

The sessions will be as follows:

  • Session 1: 9:30am-11:30am;
  • Session 2: 12:30am-2:30pm;
  • Session 3: 3:30pm-17:30pm

Tutor Profile: Mark Andrews is an Associate Professor at Nottingham Trent University whose research and teaching is focused on statistical methodology in research in the social and biological sciences. He is the author of 2021 textbook on data science using R that is aimed at scientific researchers, and has a forthcoming new textbook on statistics and data science that is aimed at undergraduates in science courses. His background is in computational cognitive science and mathematical psychology.

Other available online CPD courses in this series include

Introduction to Data Wrangling using R and tidyverse

Introduction to Data Visualization with R using ggplot

Introduction to Generalized Linear Models in R

Introduction to Multilevel (hierarchical, or mixed effects) Models in R

Introduction to Bayesian Data Analysis with R

Any questions?  Contact, Commercial Manager, School of Social Sciences.

Mark’s explanations were superb and very clear. I never imagined that I could take in so many concepts of R in such a short time.

What you’ll study

During the course you’ll:

  • Gain a comprehensive understanding of R and how it can be used for data science and statistics
  • Develop a thorough introduction to Rstudio, a powerful interface for using R
  • Discover the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, and projects.
  • Recognise the basics of data processing and formatting (such as, data wrangling)
  • Expand your knowledge of data visualization, and RMarkdown
  • Learn how to use some of the most widely used statistical methods such as linear regression, Anovas, correlations, Chi square tests.

What will I gain?  

By the end of the course, you’ll be able to competently and confidently use the R language and R statistical computing environment for common and fundamental data analysis tasks, including data processing, data visualization, and statistical data analysis.

  • On completion of at least 80% of the course, you’ll receive a certificate of attendance.

Where you'll learn

The course is delivered through interactive online workshops via Zoom. It will be practical, hands-on, and workshop based. There will be some brief lecture style presentations throughout, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Throughout the course, and we will use real-world data sets and coding examples.

Staff Profiles

Mark Andrews

Associate Professor

School of Social Sciences

Mark Andrews

Campus and facilities

Fees and funding

The fee for this course is £360 (VAT Inclusive) - £300 (VAT Exclusive)

Payment is due at the time of booking.

The fee for this course is £360 (VAT Inclusive) - £300 (VAT Exclusive)

Payment is due at the time of booking.

How to apply

You can book your place via the NTU online store:

Book your spot here.

You can book your place via the NTU online store:

Book your spot here.