Bayes Centre

Practical Introduction to Data Science (Short Course)

This online credit-bearing course is an introduction to data science for technically-minded people with some basic programming experience who want to apply data science concepts to their work, particularly those in the workplace who need a shorter stand-alone course.

Course Summary

This online course is designed to help you apply ideas from data science to your work. It is designed for technically-minded people and assumes some basic knowledge of computer programming.  It introduces ideas from data science, data management and data engineering.  It is broad rather than deep, but it aims to provide you with enough practical skills to tackle a real data science problem by the end of the course. 

The course is made up of 7 taught blocks followed by an assessed piece of coursework. The taught blocks will cover:

  • Motivation & Groundwork
  • Data Concepts & Processes
  • Munging, Cleaning, Storing & Accessing
  • Exploring, Summarising & Visualising
  • Experimenting & Predicting
  • Describing & Sharing
  • Deploying & Scaling

The course is based around recorded lectures, broken into short videos. These recorded lectures will be complemented with a weekly interactive online tutorial with the course organiser which will allow you to ask questions and discuss topics of interest. The concepts and ideas introduced in the lectures are explored in practical exercises to give hands-on experience of applying the techniques.

Further course information can be found in the University's course catalogue:

Practical Introduction to Data Science (Short Course)

Course Delivery Information

Start Date: 2nd May 2022
Course Duration: 12 weeks
Total Hours: 100 (Tutorial Hours 5; Feedback Hours 5; Assessment Hours 20; Independent Study Hours 70)
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

A UK 2:1 honours degree, or its international equivalent ideally in a “numerate” discipline (maths, computing, sciences, engineering, economics, …). Other degrees (or no degree) can be accepted with sufficient work experience, e.g. 4 years in a job involved in working with data in a nontrivial way (e.g. administering or querying databases, doing data analysis with spreadsheets, etc.) OR programming/software engineering on a day-to-day basis.

Please note all candidates should have some basic programming knowledge in one of Python, R, C (or C++, Objective C), Fortran, Java, Swift, Go, JavaScript to the point where there is an understanding of the concepts of variables, loops, conditionals, functions (or methods, procedures).

If you are unsure whether you meet these criteria please send your CV to bayes-training@ed.ac.uk

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 May 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 17th April 2022.