Bayes Centre

Probability and Statistics

This online credit-bearing course will provide an introduction to both theoretical and practical aspects of Probability and Statistics.

Course Summary

This online introductory course will cover both theoretical and practical aspects of Probability and Statistics. The first part of the course covers Probability Theory and the related concept of random variables. The second part of the course covers statistical methods for analysing data. Probability and Statistics are two hugely important fields as they provide the foundations for subjects such as Science Technology, Economy, Big Data, Artificial Intelligence and Forecasting.  The course is delivered online during 8 weeks, covering 11 Lectures.

On successful completion of this course, you should be able to: 

·       Demonstrate a conceptual understanding of fundamental concepts of probability and be able to derive basic results from them

·       Explain their reasoning about probability clearly and precisely, using appropriate technical language.

·       Apply statistical techniques to simple problems.

·       Interpret the output from statistical analyses.

·       Use the statistical computer package R to perform a number of statistical analyses.

Further information on the course can be found here:

Probability and Statistics

Course Delivery Information

Start Date: 17th January 2022
Course Duration: 8 weeks
Total Hours: 100 ( Online Activities 30, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Method of Assessment: Coursework 100 %
Level: 

This is an intermediate Masters-level course (SCQF Level 11). It develops your skills and/or provides a broad understanding of the subject in some detail - some foundational knowledge is required. Please see the entry requirements for further details. Masters-level courses are relatively intensive and require independent learning, critical thinking, analysis and reflection.

Entry Requirements

This online course gives an Introduction to Probability and Statistics and covers both theory and practical aspects using the statistical computer package R to perform several statistical analyses. Candidates should have some prior knowledge of Calculus, Combinatorics, Algebra and basic programming knowledge.  Candidate should be educated to a degree level as this course is catering for those seeking postgraduate academic credit.  However, professionals with relevant work experience may also apply even if they do not hold a degree qualification.

Check whether your international qualifications meet our general entry requirements:

English Language Requirements

You must be comfortable studying and learning in English if it is not your first language.

Course Fees and Funding

Course fees for 21/22 are £960 but funded places are available for people employed, unemployed or furloughed in Scotland who meet Scottish Funding Council elgibility criteria:

Determining eligibility for a funded place for upskilling takes a number of things into account, including fee status, but also location of employer. SFC provide guidance specifically for upskilling courses:

“Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.”

Organisations like UKCISA, and the University, provide guidance on how to determine your fee status:

If your fee status is Scotland Fee Rate, RUK Fee Rate, or EU-EEA Pre/Settled Scotland Fee rate, you may be eligible for a funded upskilling place. To determine this we will look at your fee status, residency information and, where relevant, details of your employer, to confirm whether the employer is based in, or has a significant presence, in Scotland.

Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. Please email us at bayes-training@ed.ac.uk if you would like to discuss your funding eligibility before applying.

Please note that full-time students (including full-time PhD students) are not eligible for funding.

 

Apply Now

Applications for January 2022 are now open.

In order to verify that you meet the entry requirements for this course, you will be required to provide either:

  • a degree certificate/transcript for your highest/most relevant academic qualification, or
  • a CV and reference from an employer (if employed) or professional associate (if unemployed) outlining your suitability for the course

The degree certificate/transcript or CV should be emailed to bayes-training@ed.ac.uk within 24 hours of submitting your application. References should be emailed to the same address (from an institutional/company account if from your employer) within 1 week of submitting your application. 

Once complete, your application will be processed in 1-2 weeks. Applications will be processed on a first come, first served basis with priority given to applicants who meet the criteria for a funded place. We aim to email all applicants within 2 weeks of submission regardless of the outcome of their application.

APPLY HERE

Applications will close on 4th January 2022