2024/25 Taught Postgraduate Module Catalogue

BMSC5125M Advanced Data Analysis Techniques

15 creditsClass Size: 40

Module manager: Dr Jamie Johnston
Email: J.Johnston@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

Pre-requisite qualifications

BSc in a STEM subject

This module is not approved as an Elective

Module summary

Modern biomedical sciences often generate large and complex datasets which require sophisticated analysis techniques to fully explore their potential. This module will provide practical experience in advanced data analysis techniques that are employed in the neurosciences. The emphasis will be on the range of analysis techniques available, their principles and application. Students will have significant experience employing these techniques on real data sets.

Objectives

The objectives of the module are:

- To equip students with a basic understanding of programming for analysis of large/complex data sets;

- To provide students with practical experience in advanced analysis techniques;

- To make students aware of the considerations needed when employing advanced data analysis techniques.

Learning outcomes
On completion of the module, students should be able to:

1. Analyse and manipulate large datasets using Python;

2. Be proficient in data visualisation;

3. Understand the interplay between sampling, noise and filtering;

4. Apply pre-processing techniques. e.g. image segmentation, video tracking;

5. Perform relevant analytical and statistical test, e.g. time-frequency analysis, classification and dimensionality reduction techniques.

Skills outcomes
Computational tools for data analysis.

Syllabus

This module will cover the following areas:

Computational tools for programming, analysis and visualisation – (All students)

- Python and relevant libraries.

Data acquisition, pre-processing and frequency analysis – (Neuroscience, Biomedical Science and SES students)

- Sources and characteristics of noise;

- Sampling theorem;

- Filtering;

- FFT, time frequency, coherence.

Image analysis (Neuroscience, Biomedical Science and SES students)

- Segmentation of time varying and static images;

- Object tracking.

Advanced statistical techniques (All students)

- Binary classifiers and ROC;

- Clustering;

- Dimensionality reduction;

- Classification.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lectures	1	2.00	2.00
Practical	10	3.00	30.00
Independent online learning hours			18.00
Private study hours			100.00
Total Contact hours			32.00
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Formative feedback will be given on the data plotting and visualisation task. Monitoring of student progress will occur during the supervised practical sessions where students will be able to show completed jupyter notebooks.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	The students will have to pick 2 from a choice of 4 data analysis task which will be opened exercises, where a student will employ all their learned skills to analyse a given data sets, make figures and draw conclusions from their analysis.	70.00
In-course MCQ	15 question MCQ test in Minerva assessing essential knowledge	30.00
In-course Assessment	Formative assessment: students will be set a data plotting and visualisation task and will receive feedback.	0.00
Total percentage (Assessment Coursework)		100.00

The standard release time for an MCQ/MRQ in Minerva will be 24hrs. Further guidance on the assessment will be provided in the module handbook/Minerva. If a resit is required the project report will be resubmitted and addressed by the same methodology as the first attempt.

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:10:51

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