Functional Data Analysis STATS5056

  • Academic Session: 2018-19
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: No

Short Description

This course introduces methods in functional data analysis, with an emphasis on practical issues and applications. It gives students an opportunity to learn more about one additional topic in functional data by reading books and papers and writing an essay to summarise what they have learnt.


15 lectures (1 or 2 each week)

5 2-hour computer-based practicals

Requirements of Entry


Excluded Courses

STATS4xxx (The level 4 course on FDA)



Coursework (100%): Includes class presentation of a chosen topic from the ILO's, a take home exam and project work.

Course Aims

To introduce the students to functional data analysis methods applied to a wide array of application areas;

To illustrate common numerical and estimation routines to perform functional data analysis;

To demonstrate applications where functional data analysis techniques have clear advantage over classical multivariate techniques.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ identify scenarios where data may be considered to be smooth functions and apply functional data analysis techniques;

■ construct visualization strategies and implement nonparametric smoothing for exploring functional data;

■ formulate and fit several types of functional linear models;

■ describe the functional principal component analysis algorithm and apply it in simple cases;

■ construct suitable methods for analysis involving derivatives and apply these techniques to provide solutions to practical problems;

■ discuss the principles behind registration and apply this technique to practical problems where registration is a crucial pre processing step;

■ Understand one of the above topic in depth, and introduce/present it to the class during lecture.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.