Probability and Statistics
This online credit-bearing course will provide an introduction to both theoretical and practical aspects of Probability and Statistics.
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:
This course is aimed for those looking to gain a theoretical and practical understanding of the use of the statistical computer package R to perform several statistical analyses. See entry requirements for further details.
This is an introductory Masters-level course (SCQF Level 11). It provides foundational skills and/or an overview of the subject – no prior knowledge is needed. 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.
On completion of this course, the student will 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.
| Start Date: | 17th January 2022 |
| Course Duration: | 9 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. |
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:
Funding
Through the Scottish Funding Council (SFC) Upskilling Fund and National Transition Training Fund, a number of fully-funded places are available at The University of Edinburgh on short, standalone courses from the Data Skills Workforce Development Training portfolio.
Eligibility
Funded places are available to those who meet Scottish Funding Council (SFC) fee waiver criteria. Note that course places are limited and priority will be given to those who meet the criteria for SFC-funded places.
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:
- Information on current fee status regulations for studying in Scotland is available here: https://ukcisa.org.uk/Information--Advice/Fees-and-Money/Scotland-fee-status-for-students-starting-from-August-2021.
- You can check your likely fee status here: https://www.ed.ac.uk/tuition-fees/fee-status/work-out
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.
Funding eligibility criteria for the National Transitition Training Fund in the 2021/22 session is still to be confirmed.
How to Apply
Courses are now being updated for the 2021/22 academic year.
Each course will have a distinct start date which will be updated on the Schedule of Courses webpage and each Course page.
Application links will be available in each individual course webpage, which can be accessed from the Courses summary page. Details about entry requirements and any supporting documentation required can also be found on each page.
You can register your interest by joining our mailing list where courses do not have a live application form for this session.
If you have questions about the application process please contact:
You may find additional information in the FAQ section helpful as well:
Useful Links:
You will receive a certificate of attendance after the final assessment date if you have submitted your coursework. If you pass the course, you will receive a certificate of completion once marks have been ratified by the Board of Examiners - this may be several months after the final assessment deadline.
Applications for January 2022 are now closed. Subscribe to our mailing list to be kept up to date with course start dates and new courses.
This course is offered as part of Data Science, Technology and Innovation, a flexible, modular, online programme designed to fully equip tomorrow's data professionals with courses available from across The University of Edinburgh in the sciences, medicine, arts and humanities. You can use credits achieved on this course towards postgraduate study on this programme (MSc, PG Diploma or PG Certificate), subject to approval by the Programme Director.
You may also be able to use credits achieved on this course towards other University of Edinburgh postgraduate programmes, subject to approval by the relevant Programme Director:
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