Statistical Computing with R and Stata
Complement your statistical skills with expert methods in R and Stata
Learn to programme statistical packages in order to complement statistical skills with advanced techniques. In each of the two statistical packages, students begin with 20 essential commands and progress towards computer-intensive statistical methods such as simulation, advanced regression modelling techniques, multiple imputation, cross-validation and bootstrapping.
Learn to programme two statistical packages in order to use advanced methods that complement the statistical techniques taught on our other modules. In part one students begin with 20 essential Stata commands and progress to multiple imputation and resampling methods. In part two students start with 20 essential R commands and progress to simulation, advanced regression techniques and statistical learning. Led by Dr Jason Oke, senior statistician in the NDPCHS statistics group, an experienced teaching team guide students from the basics to advanced topics in R and Stata.
This course is delivered and assessed wholly online over an intensive 8 weeks.
Aims of the module
The overall aims of this module are to enable students:
- To gain confidence in two high-level professional statistics packages, and complement the techniques learnt on other modules with advanced techniques such as multiple imputation to overcome missing data.
Intended learning outcomes are:
- Learn fundamental programming techniques such as loops, and apply them in contexts such as Monte-Carlo simulation power calculations.
- To develop the ability to complement the techniques learnt on other modules with computer-intensive techniques such as multiple imputationand resampling methods such as the bootstrap.
Students should leave the course with confidence that in the future they could manage challenging datasets with state-of-the-art R packages; address missing data with multiple imputation; use simulation to evaluate statistical power, or check model assumptions; use bootstrap and permutation methods to calculate confidence intervals and p-values in non-standard situations; apply multi-level statistical models to bigger data sets, in which each individual contributes repeated outcome measurements.
Students will also have an introductory view of Bayesian statistical modelling; an overview of statistical learning methods (“machine learning”, or “algorithms” in the popular press); and the ability to understand when each of these might be useful for a problem in evidence-based medicine.
Please ensure that you have access to a computer that meets the specifications detailed on our technical support page.
Short Course in Health Sciences: £2285.00
Students enrolled on MSc in Evidence-Based Health Care: £1850.00
Students enrolled on Postgraduate Cert in Health Research: £1850.00
Students enrolled on Postgraduate Dip in Health Research: £1850.00
Details of funding opportunities, including grants, bursaries, loans, scholarships and benefit information are available on our financial assistance page.
If you are an employee of the University of Oxford and have a valid University staff card you may be eligible to receive a 10% discount on the full stand-alone fee. To take advantage of this offer please submit a scan/photocopy of your staff card along with your application. Your card should be valid for a further six months after attending the course.
Dr Jason Oke is a senior statistician at the Department of Primary Care Health Sciences, Oxford. His research interests are in cancer diagnostics, evaluating monitoring and screening programmes.
- Each unit includes exercises to consolidate understanding
- The assessment consists of statistical problems in health research designed to give insight into real statistical problems in healthcare and to test ability to apply and understand correct statistical analysis.
Applicants may take this course for academic credit. The University of Oxford Department for Continuing Education offers Credit Accumulation and Transfer Scheme (CATS) points for this course. Participants attending at least 80% of the taught course and successfully completing assessed assignments are eligible to earn credit equivalent to 20 CATS points which may be counted towards a postgraduate qualification.
Applicants can choose not to take the course for academic credit and will therefore not be eligible to undertake the academic assignment offered to students taking the course for credit. Applicants cannot receive CATS (Credit Accumulation and Transfer Scheme) points or equivalence. Credit cannot be attributed retrospectively. CATS accreditation is required if you wish for the course to count towards a further qualification in the future.
A Certificate of Completion is issued at the end of the course.
Applicants registered to attend ‘not for credit’ who subsequently wish to register for academic credit and complete the assignment are required to submit additional information, which must be received one calendar month in advance of the course start date. Please contact us for more details.
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This course requires you to complete the application form and submit along with a copy of your CV. If you are applying to take this course for academic credit you will also need to complete section two of the reference form and forward it to your referee for completion. Please note that if you are not applying to take the course for academic credit then you do not need to submit a reference.
Please ensure you read the guidance notes before completing the application form, as any errors resulting from failure to do so may delay your application.
The last date for receipt of complete applications is 5pm Friday 8th March 2019. Regrettably, late applications cannot be accepted.
To apply for the course you should:
- be a graduate or have successfully completed a professional training course
- have professional work experience in the health service or a health-related field
- have a good working knowledge of email, internet, word processing and Windows applications (for communications with course members, course team and administration)
- show evidence of the ability to commit time to study and an employer's commitment to make time available to study, complete course work and attend course and university events and modules.
- have familiarity with basic statistical concepts (p-value; mean, standard deviation, standard error, confidence interval, normal distribution) and the essential methods used by medical statisticians such as linear, logistic regression and Cox regression.
Terms and conditions
Terms and conditions for applicants and students on this course
Sources of funding
Information on financial support