NCHNAP557 Data Visualisation Course Descriptor

Course Summary

This course introduces the use of design, interaction, and visualisation techniques and strategies to support the effective presentation and manipulation of business information. Based on principles from art, design, psychology, and information science. It offers learners opportunities to learn how to successfully choose appropriate methods of representing various kinds of business data to support analysis, decision making, and communication to organisational stakeholders. Learners will have the opportunity to apply their knowledge of data visualisation using industry-standard cloud-based technology e.g. using ServiceNow training.

Course Aims

• Train learners to quickly summarise, compare, understand and interpret data using visualisation methods.
• For learners to explore data visualisation methods and how graphics can be created using bespoke algorithms and standard software packages.
• Train learners to balance data analysis with design skills in order to create visuals that stimulate viewer attention and engagement.

Learning Outcomes

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

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• On-line learning
• On-line discussion groups
• On-line assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Assessment will be in two forms:

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.

Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

Indicative Reading

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

Books 

• Tufte, E., (2001), The Visual display of quantitative information, Cheshire, Conn.: Graphics Press
• Few, S., (2012), Show me the numbers: Designing tables and graphs to enlighten, Burlingame, Calif.: Analytics

Journals

Learners are encouraged to consult relevant journals on data visualisation. 

Electronic Resources

Learners are encouraged to consult relevant electronic resources on data visualisation. 

Indicative Topics

• Evaluation of data visualisation methods for effective communication to specialist and non-specialist audiences
• Practical use of data visualisation methods
• Building bespoke algorithms for data analyses and visualisation

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