Introduction to Statistics using R | University of Westminster

Overview

Course summary

This course provides an introduction to statistics using R for analysis of data in Life Sciences. R is a open source (free) software environment which offers an integrated suite of facilities for data manipulation, calculation, and graphical display. In nutshell, it provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.

Participants will be taken through the process of statistical analysis from the starting point of entering data through quality control, exploratory data analysis and data visualisation to carrying out statistical testing and generating the summary statistics needed for report writing.

The target audience is anyone working in a Life Sciences, Biomedical Sciences or Bioinformatics environment. This could mean that you're working within the NHS or private sector with diagnostics and data analysis.

Entry requirements

You are encouraged to have a standard knowledge of IT and computer systems, including installing software and use of MS Excel.

Accreditation

Contributes 20 CPD hours towards RSS Data Analyst membership status.

Course structure

This is an online module that will be broken down into eight weekly parts:

Introduction and basics of R programming
Becoming familiar with your data
Study design
Exploratory data analysis – visualisation methods
Statistical testing for associations in data
Statistical testing for differences in data
Survival analysis
Report writing and presenting data

Learning outcomes

By the end of the course, participants will be able to:

Apply appropriate statistical analysis method for the particular biomedical data type using R
Generate plots and summary statistics for reporting a result from a biomedical investigation
Apply appropriate quality assurance methods to biomedical data
Design appropriate strategy for exploration of biomedical data for a particular disease

Our tutor

Dr Dipankar Sengupta

Dr Dipankar Sengupta is a Lecturer in Health Data Science at the School of Life Sciences, University of Westminster.

With an undergraduate and postgraduate background in Bioinformatics, he has ~8 years of post-doctoral research experience (Artificial Intelligence Lab, Vrije Universiteit Brussels, Belgium and Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, UK) in data science, machine learning, relational databases, and software development as applied to clinical and life science.

He has work experience of more then 14 years, spanning across academic and industrial sectors, including teaching (~11 years) computational subjects as diverse as scientific programming and statistical computing, techniques of artificial intelligence, data warehouse & its clinical applications, etc. He completed his higher education training from CED, Queen's University Belfast and was awarded the the status of Fellow (FHEA) in recognition of attainment against the UK Professional Standards Framework for teaching and learning support in higher education.

His research aspires to unveil the precision medicine intricacies like patient stratification, tools for diagnosis/prognosis, etc. using data science, machine learning and other computational approaches. He also works on translational computing of clinical data with a considerable focus on temporal mining and auguring the state of a disease for a patient.