University of St Andrews

Key information

SCOTCAT credits : 15

ECTS credits : 7

Level : SCQF level 11

Semester: Both

Availability restrictions: Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Machine learning workflows are key to effective Data Science. This module is focussed on using python packages to perform end-to-end data-driven analyses.

Learning and teaching methods and delivery

Weekly contact: Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours: 6

Guided independent study hours: 148

Assessment pattern

As used by St Andrews: Coursework = 100%

As defined by QAA
Written examinations : 0%
Practical examinations : 0%
Coursework: 0%

Re-assessment: Coursework = 100%

Personnel

Module coordinator: Professor T W Kelsey
Module teaching staff: Professor Tom Kelsey

Intended learning outcomes

• Determine what models are applicable for different data and objectives
• Conduct hyperparameter-tuning/model-selection as appropriate to the model
• Manipulate data, fit models, and summarise/display their results/performance and objectively compare models
• Conduct comprehensive analysis of large real-world data covering: data preparation; model fitting, critique & refinement; and presentation of results to a range of audiences