ITEC300 - Data Visualisation

Year

Credit points

Campus offering

Prerequisites

ITEC202 Data Analytics and Decision Making

Incompatible

DATA300 Data Visualisation

Teaching organisation

Unit rationale, description and aim

The innovative digital technologies are enabling today's businesses to respond to the demands of the society to create and maintain personalised customer relationships. This is possible because businesses collect vast amount of end-user data via their systems thereby attaining tremendous insight into end-user's beliefs, needs and aims. However, the analyse and meaningful presentation of large volume of diverse data, called big data, to support corporate decision-making process is a challenging task. Current display methods such as graphs for analysis are not effective when dealing with extremely large volumes of diverse data. Hence understanding the techniques and methods to visualize large volumes of different types of data have become essential for today's businesses.

Data visualization is the presentation of data in a pictorial or graphical format for decision makers to easily identify underlining patterns. It enables organisations to present raw data in a contextual and meaningful way to generate the most value by addressing the quality of data through data governance. Thus, this unit explores ways in which different visual analytical techniques can be employed to create intelligible, useful and navigable data representations. In addition, it emphasises the need to understand and manage the various challenges associated with the interpretation and visualization of big data.

The primary aim of this unit is to demonstrate an understanding of the principles and techniques of data visualization and develop contextual data visualization models of big data to support corporate decision-making process while upholding human dignity.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - demonstrate an understanding of the need for data visualisation and its principles in the context of big data (GA5, GA8)

LO2 - analyse data visualisation models (GA4, GA6, GA8)

LO3 - create high-impact visualisations of common data using software tools (GA4, GA6, GA8)

LO4 - apply predictive analytics to improve business decision making (GA4, GA5, GA8)

LO5 - generate intelligible and useful reports and dashboards to assist in corporate decision making for the dignity of the human person (GA2, GA4, GA5, GA8, GA10)

Graduate attributes

GA2 - recognise their responsibility to the common good, the environment and society 

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA6 - solve problems in a variety of settings taking local and international perspectives into account

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include:

• introduction to data visualisation
• tools for data visualisation
• connecting and merging multiple data sources
• context of data visualisation
• different types of data visualisations and their effectiveness
• design and exploratory analysis
• designing meaningful visualisations for target audiences using data governance principles
• static and interactive visualisations
• data mapping
• creating dashboards
• practicing good ethics in data visualisation

Learning and teaching strategy and rationale

The standard mode of delivery for this unit is multi-mode over a twelve-week semester or equivalent study period. However, the unit may also be offered in online and intensive modes according to University and student requirements.

Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.

Students will have access to all primary learning materials online through LEO, along with formative and summative assessments, all of which will be available online, to provide a learning experience beyond the classroom. While there are no formal classroom lectures for this unit, students will be required to attend weekly two-hour workshops, which may include a seminar and specific tasks related to achievement of the unit learning outcomes.

Assessment strategy and rationale

A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. Such procedures may include, but are not limited to: essays, reports, examinations, student presentations, projects or case studies.

The assessment strategy for this unit is based on the need to determine authentic student achievement of the learning outcomes. In the assessment task 1, students will investigate the current visualisation techniques to become aware of the required and fundamental knowledge. In the assessment task 2, students will analyse different types of visualisation designs and develop some to gain practical skills. 

In the assessment task 3, students will integrate the knowledge and practical skills they have gained in assessment tasks 1 and 2 to implement data visualisation for a given case study to satisfy the needs of different stakeholders. The third assessment task also requires students to demonstrate the Catholic Social Teaching principle of dignity of the human person: how organisations organise, use, manage and secure data to maintain the dignity, privacy, rights and freedom of a person. 

Overview of assessments

Representative texts and references

Berinato, S 2016, Good charts: the HBR guide to making smarter, more persuasive data visualizations, Harvard Business Review Press, Cambridge, MA.

Hinderman, B 2015, Building responsive data visualization for the web, Wiley, Hoboken, NJ.

Jones B 2015, Communicating data with tableau: designing, developing, and delivering data visualizations, O’Reilley Media, Sebastapol, CA.

Kinley, P 2016, Data analytics: a basic guide to master data analytics, CreateSpace, San Francisco, CA.

Knaflic CN 2015, Storytelling with data: a data visualization guide for business professionals, Wiley, Hoboken, NJ.

Lin, HM 2013, Innovative approaches of data visualization and visual analytics, IGI Global, Hershey, PA.

Munzner, T 2014, Visualization analysis and design, A K Peters/CRC Press, Boca Raton, FL.

Nandeshwar A 2013, Tableau data visualization cookbook, Packt Publishing, San Francisco, CA.

Simon, P 2014, The visual organization: data visualization, big data, and the quest for better decisions, Wiley, Hoboken, NJ.

Tillman FA & Cassone, DT 2016, The seven rules for building effective analytical models for decisions, HTX Inc, Sunrise Beach, MO.