Unit name | Statistical Computing and Empirical Methods |
---|---|
Unit code | EMATM0061 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Reeve |
Open unit status | Not open |
Pre-requisites |
None Note: This unit is suitable to be taken primarily by students on the TQEC PGT degrees whose first degree (or equivalent prior experience) is in Computer Science, Software Engineering, or a very similar subject. |
Co-requisites |
None |
School/department | School of Engineering Mathematics and Technology |
Faculty | Faculty of Engineering |
The aim of this unit is to provide students with a broad introduction to the principles of rigorous design of experiments, and to statistical analysis of empirical data using the free 'R' programming language. These topics are commonly taught in STEM subjects such as physics, psychology, or engineering mathematics, but are very rarely covered in any depth on Computer Science (CS) or Software Engineering (SE) degrees. For that reason, this unit is aimed primarily at postgraduate students with a strong background in CS/SE.
This unit is complementary to the Software Development, Programming, and Algorithms (SPDA) unit which equips non-CS/SE STEM-background PGT students with essential programming skills and understanding of contemporary software development and engineering practices.
The core skills taught to CS/SE students on this unit are required in order for them to be able to understand, implement and apply data science techniques across all other units of the suite of data-science-based PGT degrees offered within SCEEM (and intended for delivery at the University's new Temple Quarter Enterprise Campus, when it opens).
By the end of the unit students will be able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities and self-directed exercises.
Coursework (100%)