Program Overview
Introduction to Statistics using R
Overview
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.
Program Outline
R is a free, open-source software environment that offers a comprehensive suite of tools for data manipulation, calculation, and graphical display. It provides a wide range of statistical techniques (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible.
Objectives:
By the end of the course, participants will be able to:
- Apply appropriate statistical analysis methods for specific biomedical data types using R.
- Generate plots and summary statistics for reporting results from a biomedical investigation.
- Apply appropriate quality assurance methods to biomedical data.
Teaching:
The course is taught by Dr. Dipankar Sengupta, a Lecturer in Health Data Science at the School of Life Sciences, University of Westminster. Dr. Sengupta has extensive experience in data science, machine learning, relational databases, and software development, with a focus on clinical and life science applications. He has over 14 years of experience in both academic and industrial settings, including teaching computational subjects like scientific programming, statistical computing, artificial intelligence techniques, and data warehousing.
Careers:
The course is targeted towards individuals working in Life Sciences, Biomedical Sciences, or Bioinformatics environments. This could include roles within the NHS or private sector involving diagnostics and data analysis.
Other:
The course contributes 20 CPD hours towards RSS Data Analyst membership status.
University of Westminster
Overview:
University of Westminster is a public university located in London, England. It offers a wide range of undergraduate and postgraduate programs across various disciplines. The university is known for its focus on practical learning and its strong connections to the industry.
Services Offered:
Student Life and Campus Experience:
The university has four campuses across London, providing students with a vibrant and diverse campus experience. Students have access to various facilities, including a cinema, gallery spaces, and sports facilities. The university also offers a range of student support services, including career guidance, academic support, and mental health services.
Key Reasons to Study There:
Location:
The university's location in London provides students with access to a wealth of cultural and professional opportunities.Practical Learning:
The university emphasizes practical learning, with many programs incorporating work placements and industry projects.Industry Connections:
The university has strong connections to industry, providing students with opportunities for networking and career development.Diverse Student Body:
The university has a diverse student body, creating a welcoming and inclusive environment.Academic Programs:
The university offers a wide range of academic programs, including:
Undergraduate courses:
A broad range of undergraduate courses in various disciplines, including business, design, creative industries, and liberal arts.Postgraduate courses:
A variety of postgraduate study options, including master's degrees, research degrees, and short courses.Other:
The university has a strong commitment to research and innovation, with a focus on areas such as sustainability, social justice, and digital technologies. It also has a dedicated alumni network, providing support and opportunities for graduates.
