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Introduction to Statistical Programming

Key Details

Date: 7th October 2019 - 11th October 2019
Time: 09:30-17:30
Venue:  Computer Rooms A & B, Main Building, Institute of Psychiatry, Psychology & Neurosciences (IoPPN), King's College London. View Map.


Course Aim

The Statistical Programming course is focused on extending existing skills in analysing data from quantitative research in health research using R or STATA. Programming skills are particularly useful when confronted with complex and/or large datasets, when conducting regular statistical analyses, or when there is a need to adapt existing codes to your own requirements. Statistical programming expertise is increasingly needed in imaging, bioinformatics, omics, cohort and clinical trials with complex interventions.



Learning Outcomes

Subject Specific: Knowledge, Understanding and Skills

At the end of the course the students should be able to demonstrate subject-specific knowledge, understanding and skills and have the ability to:

  • Develop a theoretical understanding of the basics of programming;
  • Understand the use and usefulness of conditionals, iterations, and functions;
  • Identify appropriate methods for solving particular data manipulation problems;
  • Formulate sensible programming solutions for a data set, and demonstrate an ability to check the correctness of one’s manipulations;
  • Become an independent user of R and Stata, in finding help online, and demonstrate how to select appropriate packages for one’s problems.

General: Knowledge, Understanding and Skills.

On successful completion of this course the student should be able to:

  • Understand and explain basic programming concepts;
  • Justify and critique the use of programming solutions for real life applications;
  • Show confidence in the use of R and Stata in manipulating data sets for real life application;
  • Communicate ideas effectively and professionally by written, oral and visual means;
  • Study autonomously and undertake activities with minimum guidance;
  • Select and properly manage information drawn from books, journals, and the internet;
  • Interact effectively in a group.

Cost and Booking

  • External Early bird: £950
  • KCL Staff Early bird: £712.5
  • KCL Student Early bird: £4750
  • Other student Early bird: £712.50
  • King's Health Partners Early bird: £712.50

50% discount to King's College London PhD students, 25% discount to
other students and staff at King's College London and King's Health Partners.


Booking for this course has now closed.

Course Team

Dr Cedric Ginestet (Academic Lead)

CedricGinestetCedric has received a PhD in Biostatistics from Imperial College London.

Cedric has been affiliated to the Neuroimaging Department in King's College London, as well as to the Mathematics and Statistics Department in Boston University, before joining the Department of Biostatistics and Health Informatics in the Institute of Psychiatry and Neuroscience in 2014.

Research interests

Causal inference; network analysis; object data analysis; statistical learning.

Research profile

See Cedric's research profile here.

Dr Ewan Carr

Ewan CarrEwan joined Biostatistics in 2017 as a BRC Statistician. He was previously employed within Epidemiology and Public Health at UCL. In 2014 he completed a PhD in Social Statistics (University of Manchester) and prior to this trained in Social Research Methods and Statistics (MSc, University of Manchester).

His is interested in the application of latent variable and multilevel modelling techniques to further the understanding of mental health outcomes.

Research profile

See Ewan's research profile here.

Mr George Vamvakas

GeorgeVamvakasGeorge is a statistician in the Department of Biostatistics. Having worked as a health economist he switched to statistics after completing the MSc in Statistics at Lancaster University.

Since then he worked on a missing data method for clinical trials and on propensity score matching for observational data. The main focus of his research at the IoPPN is the analysis of developmental data.

George's primary interests lie in the construction of growth models with an emphasis on structural equation modelling. Areas of application include autism, language disorders, and reoffending behaviour.

Research profile

See George's research profile here.


Your place will not be confirmed until payment has been made. Failure to cancel without sufficient notice will forfeit your course fee and access to future courses. If you would like to pay by internal transfer, please contact

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