Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: DTS204TC
Module Title: Data Visualisation
Module Level: Level 2
Module Credits: 2.50
Academic Year: 2021/22
Semester: SEM2
Originating Department: Shool of AI and Advanced Computing
Pre-requisites: N/A
   
Aims
Data visualisation is a tool for both the exploration of a data set and for the clear explanation of the findings. In this module students will gain practical experience in data visualisation methods for analysing the structure and dependencies of data sets. Students will also study techniques for creating effective visual data presentations.
Learning outcomes 
A. Use data visualisation methods and techniques to analyse a given data set.

B. Use software tools to analyse a given data set.

C. Select appropriate analysis and visual encoding methods for a given data type.

D. Design an effective visualisation to highlight particular features of a given data set.

E. Critically evaluate the effectiveness of a given data visualisation.

F. Create visualizations using interactive web graphics programming in JavaScript, and D3.js.

G. Describe the fundamentals of 2-D and 3-D graphics.
Method of teaching and learning 
This module is taught using a combination of lectures and computer lab sessions. In the computer lab sessions, students will gain experience with software tools for data visualisation.


During the semester students will complete two projects, each consisting of an exploratory analysis of a chosen data set and a visual presentation of the findings. Assessment of projects will involve a peer-review element.
Syllabus 
Introduction to software tools and packages for data preparation and visualisation (JavaScript, D3.js).


Exploratory data visualisation

Linear unsupervised algorithms (principal component analysis, metric multidimensional scaling, Nystrom approximation);

Nonlinear algorithms (locally linear embedding, ISOMAP, Laplacian eigenmaps);

Supervised algorithms (Neighbourhood component analysis, relevant component analysis);

Applications to graph realisation and partitioning (clustering, classification).


Explanatory data visualisation

Influence of graphical perception and cognition on the design of data visualisations;

Chart types for data comparison, relationships, composition and distribution;

Colour, interaction, animation;

Approaches for the visualisation of numerical, ordinal or categorical data, times series, networks and geographical data;

Critical evaluation of effectiveness of data visualisations.

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 13      13    49  75 

Assessment

Sequence Method % of Final Mark
1 Final Exam 60.00
2 Project 1 40.00

Module Catalogue generated from SITS CUT-OFF: 6/5/2020 5:40:52 PM