Introduction to Statistics Using R and RStudio – Online
- Level(s) of Study: Short course; Professional
- Course Fee:
£360
- Start Date(s): 29 May 2025
- Duration: Two days (Thursday 29 and Friday 30 May 2025) 9:30 am – 5:30 pm
- Study Mode(s): Part-time
- Entry Requirements: More information
Introduction:
Discover the power of RStudio for data analysis and statistics in this practical, online workshop.
Whether you’re an early-career researcher, a PhD student or academic looking to broaden your methods, or a professional data analyst interested in robust statistical tooling, this course provides you with a comprehensive understanding of how to use RStudio for data wrangling, visualisation, and statistical modelling, allowing you to move confidently toward more advanced analysis.
During the course you’ll:
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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. -
Expand your knowledge of data visualisation, and RMarkdown -
Learn how to apply some of the most widely used statistical methods such as linear regression, Anovas, correlations, Chi square tests.
Feedback from previous students
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
R is a major tool in modern data analysis and statistics, used extensively in academic research as well as by data analysts in the public and private sectors. Its flexibility, comprehensive ecosystem of packages, and active community have made it highly suited for all aspects of data analysis.
This course offers a thorough, hands-on introduction to working with R and RStudio, which is the most widely used integrated development environment for R. By participating, you’ll gain the foundational skills needed to handle real-world datasets, develop reproducible analytical workflows, create effective data visualisations, and conduct a wide range of common statistical techniques.
What will I gain?
By the end of the course, you’ll have the tools and understanding needed to work effectively in R, from initial data exploration through to producing compelling analyses and reports. This foundation sets the stage for more advanced techniques and methods as your needs evolve.
- Guided Tour of RStudio Learn how to set up and navigate RStudio’s key features. We’ll discuss best practices for organising your workspace, working with scripts, and using the console efficiently.
- Fundamentals of Coding in R Get comfortable with R’s core concepts. You’ll learn how to install and manage packages, create and manipulate variables, write and run R scripts, and import and summarise datasets.
- Data Cleansing and Preprocessing Discover how to prepare real-world data for analysis. Using tools like dplyr, you’ll learn techniques for filtering, selecting, reshaping, and summarising data, ensuring it’s ready for meaningful interpretation.
- Data Visualisation Explore ggplot2 to create clear, informative plots. We’ll cover scatterplots, boxplots, histograms, and introduce the principles of effective visual communication.
- RMarkdown & Quarto Learn to combine analysis and documentation in a single, reproducible document. We’ll show you how to generate reports, presentations, and more, seamlessly integrating code, text, figures, and tables.
- Introduction to Statistics using R Gain familiarity with basic statistical methods in R. We’ll cover widely used techniques such as t-tests, correlations, linear regression, and ANOVA, giving you a taste of R’s extensive statistical capabilities.
How you’re taught
This course is designed to provide a practical, engaging learning experience incorporating hands-on workshops and coding sessions, concise expert-led lectures, and real-world data examples to reinforce key concepts.
Delivery Format: Online via Zoom allowing you to interact with instructors and fellow participants in real time. You’ll also have access to downloadable resources including code, datasets, and exercises for you to continue practicing and applying your skills after the sessions.
Contact hours
The course will take 6 contact hours per day plus two 1-hour breaks.
The sessions will be as follows:
- Session 1: 9:30am - 11:30am
- Session 2: 12:30pm - 2:30pm
- Session 3: 3:30pm - 5:30pm.
Careers and employability
Certificate of attendance and digital badge
Upon successful completion of the course, you will receive a digital certificate of attendance and a digital badge powered by .
Your is more than just a certificate – it’s secure, verifiable, and protected against fraud through encryption and blockchain technology.
They also come with detailed metadata, including an overview of the skills you have achieved on the course, evidence of completion, and assessment criteria if appropriate.
Share your achievements seamlessly with friends, customers, and potential employers online, and proudly add your badge or certificate to social media platforms such as LinkedIn, so all the right people can see it.
Entry requirements
This course is designed for anyone looking to build or enhance their skills in data analysis and statistics using R, it is particularly well-suited for the following groups:
Early-Career Researchers and PhD Students
- If you’re beginning your research journey, this course will help you develop robust data analysis skills essential for conducting high-quality, reproducible studies. Learn to navigate RStudio confidently and apply statistical techniques that will elevate your research output.
Postdoctoral Researchers and Academics
- As a Postdoc or Academic, this course provides an opportunity to deepen your expertise in data science. Enhance your ability to analyse complex datasets, visualise results, and streamline your workflow with RMarkdown. These skills can improve your efficiency, strengthen your grant applications, and make your research more impactful.
Data Analysts and Statisticians
- For professionals working in data-heavy roles, such as those in government, healthcare, or private sector organisations, this course offers tools and techniques to tackle statistical challenges efficiently. Gain skills in data wrangling, visualisation, and foundational statistical modelling that will add value to your current role and career progression.
Industry Professionals Transitioning to Data Science
- If you’re transitioning into data science or statistical analysis roles, this course provides an excellent foundation. Learn how to use RStudio effectively for data manipulation, creating visualisations, and conducting statistical analyses that are increasingly in demand across industries like finance, marketing, and technology.
This module is the first in the series and provides a comprehensive introduction to R for use in later modules. Modules can be taken individually or as a full series.
No prior experience with R is required.
Fees and funding
How to apply
Ready to Elevate Your Data Skills?
Any questions?
Contact the short course team:
Email: SOCCommercial@ntu.ac.uk
Tel: +44 (0)115 848 4083