Practical Data Science using R
This short course has been specifically designed for professionals who wish to build strong analytical skills to process and manipulate different types of data.
By attending this course, you will be able to develop strong practical analytical skills, which will enable you to transform data into knowledge that can inform your business practices. 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.
2. What you will study
- 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.
3. How you will learn
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.
This course is delivered by Dr Eyad Elyan.
Dr 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.
4. How to apply
Back to short courses