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 at the link below:

Practical Introduction to Data Science (Short Course) Course Catalogue

Course Delivery Information

Start Date: 2nd of 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.

Applications for 2021/22 will open Autumn 2021.

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.