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Academic Handbook Data and Artificial Intelligence

Information Presentation and Visualisation Course Descriptor

Course code LDSCI5209 Discipline Computer & Data Science
UK credit 15 US credit 4
FHEQ level 5 Date approved November 2022
Core attributes None
Pre-requisites LDSCI4210 Intermediate Programming with Data

OR

LCSCI4208 Fundamentals of Computer Science II

Co-requisites None

Course Overview

This course introduces foundational principles, methods, and techniques of visualisation to enable the creation of effective information representations suitable for exploration and discovery. The course covers the design and evaluation process of visualisation creation, visual representations of data, relevant principles of human perception and cognition, and basic interactivity principles. It emphasises good programming practices for both static and interactive visualisations. Finally, the course requires extensive writing, including documentation, explanations, and discussions of the findings from the data analyses and the visualisations.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

K1b Demonstrate knowledge and critical understanding of well-established concepts in information design and data visualisation techniques.
K2b Demonstrate knowledge of human perception and cognition to assess the quality and effectiveness of a data visualisation.
K3b Demonstrate the ability to identify the appropriate data visualisation techniques for exploration and discovery.

Subject Specific Skills

S1b Apply data visualisation techniques in an appropriate manner to a given data set across application domains.
S2b Develop a static or interactive reproducible data visualisation in Python.
S3b Design an effective data visualisation using human perception and cognition principles.

Transferable and Employability Skills

T1b Constructively critique and assess a data visualisation.
T3b Demonstrate a sound technical proficiency in written English and skill in selecting vocabulary so as to communicate effectively to specialist and non-specialist audiences.

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage students in their studies.

The scheduled teaching and learning activities for this course are:

Lectures/labs. 40 scheduled hours – typically including induction, consolidation or revision, and assessment activity hours:

  • Version 1:All sessions in the same sized group

OR

  • Version 2: most of the sessions in larger groups; some of the sessions in smaller groups

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.

Indicative total learning hours for this course: 150

Assessment

Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.

Summative Assessments

AE: Assessment Activity Weighting

(%)

Duration Length

(words)

1 Set Exercises 60 24-32 hours  
2 Written Assignment 40   2,500

Further information about the assessments can be found in the Course Syllabus.

Feedback

Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

  • Colin Ware. 2010. Visual Thinking for Design. Elsevier.
  • Tamara Munzner. 2014. Visualisation Analysis and Design. A K Peters/CRC Press.
  • Cole Nussbaumer Knaflic. 2015. Storytelling With Data: A Data Visualisation Guide for Business Professionals. Wiley.

Indicative Topics

Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below are used as a general guide and part of the approval/modification process only.

  • Visual encodings
  • Colour perception and cognition
  • Interaction
  • Trees and networks
  • Filtering & Aggregation
  • Time-series and geographical data
Title: LDSCI5209 Information Presentation and Visualisation

Approved by: Dr Alison Statham

Location: academic-handbook/programme-specifications-and-handbooks/undergraduate-programmes

Version number Date approved Date published Owner Proposed next review date Modification (as per AQF4) & category number
1.0 November 2022 January 2023 Dr Alexandros Koliousis November 2027  
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