Introduction to statistics using R and Rstudio
- Level(s) of Study: Professional / Short course
- Start Date(s): Wednesday 3 April to Thursday 4 April, 2024
- Duration: 2 days, 9:30 am – 5:30 pm
- Study Mode(s): Short course
- Campus: City Campus
- Entry Requirements:
More information
Introduction:
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 1-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 kelly.smith@ntu.ac.uk, 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.
Campus and facilities
Entry requirements
This course is aimed at anyone who is interested in using R for data science or statistics, such as researchers or analysts studying for/ have already studied a PhD in a field of science that involves extensive statistical analysis.
This module is the first in the series and provides a comprehensive introduction to R for use in later modules.
Getting in touch
If you need more help or information, get in touch through our enquiry form
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:
You can book your place via the NTU online store: