Unit information: Statistical Computing and Empirical Methods in 2020/21

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

Description including Unit Aims

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).

Intended Learning Outcomes

By the end of the unit students will be able to:

1. Select and successfully apply appropriate parametric or nonparametric statistical significance tests for any given collection of data, to evaluate some hypothesis, using supplementary supporting tests (e.g. tests of distribution) as necessary.
2. Construct and justify an appropriate design for an experiment, given the necessary prior information regarding the experiment's aims and constraints.
3. Demonstrate their ability to select and employ appropriate digital tools to gather, clean, and curate large-scale data sets.
4. Collaboratively develop productive working practices within a contemporary integrated development environment (IDE) for teamwork-based statistical analysis of data in empirical scientific and/or engineering research.
5. Assess, analyse, and solve a data science problem by appraising, selecting, and applying appropriate tools and techniques, choosing among remotely-accessed services and options for bespoke problem-specific software development.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities and self-directed exercises.

Assessment Information

Coursework (100%)

Reading and References

• D. Montgomery (2019) Design and Analysis of Experiments, 9^th Edition, Wiley.
• M. Pett (2015) Nonparametric Statistics for Health Care Research: Statistics for Small Samples and Unusual Distributions. 2^nd Edition. Sage Publications.
• J. Vandenplas (2016) Python Data Science Handbook: Essential Tools for Working with Data. O' Reilly.
• S. Baumer, D. Kaplan, & N. Horton (2017) Modern Data Science with R. CRC Press.
• P. Bruce, A. Bruce, & P. Gedeck (2020) Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. 2^nd Edition, O’Reilly.
• A. Field & G. Hole (2003) How to Design & Report Experiments. SAGE Publications.
• R. Mitchell (2018) Web Scraping With Python, Second Edition, O’Reilly.