Practical Data Science using R
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R is an open-source programming language for statistics, data analysis and visualisation. It consists of an extensive set of packages that provide functionality for almost every single data-related task (i.e. load data from excel sheets, collecting data from Social Media sites, finding hidden patterns in data, visualising trends, detecting outliers, and so on). R provides simple and easy-to use packages for advanced and mathematically complex machine learning and data mining models and is considered as one of the most powerful tools in Data Science and Analytics.
Session 1: Introduction to R and RStudio and Data Exploration:
An overview of R and RSTudio, and learn how to load, process and explore different datasets. You will learn how to explore data by means of simple summary statistics, linear regression models and basic visualisation techniques (i.e. Histograms, Box plots, and others).
Session 2: Data manipulation and Visualisation:
You will learn how to manipulate, aggregate and summarise data using the most popular packages in ‘R’ such dplyer. You will learn how to handle missing values, redundant features, reshape your datasets and others to prepare it for further analytics. You will learn to tell the story of large and complex datasets visually using the R package ‘ggplot2’
Session 3: Data Modelling:
Build and evaluate different state-of-the-art machine learning algorithms using real-life datasets (SVM, Random Forest …). You will learn the underlying concepts of these models in a very practical way. You will then be able to apply these models to real datasets, evaluate and communicate the results in visual form.
Session 4: Interactive Reports:
An overview of markdown in ‘R’. You will learn how to produce an interactive reproducible results that communicate the workflow in an easy-to interpret reproducible format.
The key concepts will be delivered via short lectures to give you the opportunity to spend most of the time applying your learning via hands-on interactive labs.
The Disability & Dyslexia Centre advises and supports students who disclose a sensory or mobility impairment, chronic medical condition, mental health issue, dyslexia and other specific learning differences. Applicants are encouraged to arrange a pre-entry visit to discuss any concerns and to view the facilities.
Staff delivering this course
Dr Eyad Elyan is a Senior Lecture in the School of Computing Science and Digital Media and the course leader of the MSc Data Science. Dr Elyan has strong background and research interest in Advanced Machine Learning and Data Analytics, and had led several projects with industrial partners and public funding bodies (i.e. Innovate UK, OGIC, Data Lab, Historic Environment…) to successful completion.
Study Skills Support
The Study Support Team provides training and support to all students in:
- Academic writing
- Study skills (note taking, exam techniques, time management, presentation)
- Maths and statistics
- English language
- Information technology support
For professionals who wish to build strong analytical skills to process and manipulate different types of data.
For new intakes course fees are reviewed and published annually for each mode of delivery. Tuition fees are fixed for the duration of a course at the rate confirmed in the offer letter. For further information see:
Taking this course, you will have access to some of the world's best facilities.
We've invested more than £100 million in the development of our campus, new facilities and new resources.University Wide Facilities
Register direct to the university using our online application form.
Have a question about the course? Get in touch with the team and we'll do our best to help.