ITEC622 - Data Analytics and Visualisation

Year

Credit points

Campus offering

Prerequisites

ITEC610 Python Fundamentals for Data Science or ITEC617 Data and Information Management

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

Unit rationale, description and aim

The explosion in data and digital technologies has opened new ways of obtaining data-driven insights. To take advantage of these opportunities, organisations need people with the ability to extract, consolidate, analyse and visualise data from very large diverse data sets.

Data analytics refers to a range of computational and statistical techniques used to extract ‘meaning’ (i.e. comprehensible and useable information) from raw data sets. These techniques transform, organise and model the data to draw conclusions and identify patterns of activity that enable organisations to make more-informed decisions about their activities.

In this unit students will learn the foundational concepts in data analytics including a range of computational and statistical techniques used to extract ‘meaning’ (i.e. comprehensible and useable information) from raw datasets. Also, students will learn how to apply data visualisation methods and tools that enable presentation of large volumes of data in a graphical format for decision makers to easily identify underlining patterns. This unit is designed in alignment with Microsoft’s curriculum and provides a pathway to the Microsoft Power Platform Fundamentals certification.

The primary aim of this unit is to equip students with practical data analytics and data visualisation knowledge and skills required to solve the real-world data problems, including data-driven solutions to support our responsibility to the common good, the environment and society.

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 - Recognise business value and various techniques for data analytics and visualisation, and their applications (GA5, GA8)

LO2 - Integrate existing data from multiple sources into a single store using a scalable data service and build no-code apps for data entry (GA5, GA10) 

LO3 - Apply data analytics and visualisation tools and techniques to derive useful insights from raw data (GA5, GA10)

LO4 - Create intelligible and insightful data visualisation artefacts such as charts and dashboards to support our responsibility to the common good, the environment and society (GA2, GA5)

Graduate attributes

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

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

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

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

Content

Topics will include:

• Data visualisation concepts and principles
• Introduction to Microsoft Power Platform
• Creating data-driven apps in a no-code environment for capturing data
• Cleaning and transforming data
• Connecting and merging multiple data sources
• Different types of data visualisations and their purpose
• Creating fully functional visualisations with Power BI
• Reports and dashboards
• Visualising geodata
• Communicating data analysis and visualisation results
• Social and environmental applications of Data Analytics and Visualisation

Learning and teaching strategy and rationale

This unit is offered in different modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Blended modes

In a blended mode, students will require face-to-face attendance in blocks of time determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise. 

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

ACU Online

This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous weekly discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.

Assessment strategy and rationale

To pass this unit, students are required to achieve an aggregate mark of at least 50%. Marking will be in accordance with a rubric specifically developed to measure the level of achievement of the learning outcomes for each item of assessment.

Assessment methods incorporate problem-based tasks, case studies and practical/hands-on tasks that are relevant to the real-world needs. The first assessment provides students with an opportunity to perform various data analytics and visualisations tasks in the lab. In assessment task 2, students will apply the knowledge and practical skills they have gained in the unit to implement data visualisation for a given case study to satisfy the needs of different stakeholders. The aim of this assignment is to equip students with the skills of creating novel data visualisations to effectively reveal the narratives behind the data. Assessment 3 allows students to demonstrate the depth of their knowledge and understanding of data visualisation concepts and tools through Microsoft Certification Exam.

Overview of assessments

Representative texts and references

Microsoft Power Platform Fundamentals (https://docs.microsoft.com/en-us/learn/paths/power-plat-fundamentals/)

Microsoft Certified: Data Analyst Associate (https://docs.microsoft.com/en-us/learn/certifications/data-analyst-associate/ )

Mitchell Pearson, Brian Knight, Devin Knight, Manuel Quintana, 2020, Pro Microsoft Power Platform: Solution Building for the Citizen Developer, O'Reilly Media, Inc, USA.

Albright, SC & Winston, WL 2020, Business analytics: data analysis and decision making, 7th edn, Cengage Learning Inc.

Haider, M 2016, Getting started with Data Science: making sense of data with analytics, Pearson Higher Education, Upper Saddle River, NJ.

Kirk, A 2019, Data Visualisation: A Handbook for Data Driven Design, 2nd edition, SAGE publications Ltd, London.

Grolemund, G 2017, Hands-On Programming with R, O'Reilly Media

Wilke C O 2019, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, O'Reilly Media, Inc, USA

Tominski, C & Schumann, H 2019, Interactive Visual Data Analysis, CRC Press, Taylor & Francis Group.

Loth, A 2019, Visual Analytics with Tableau, Wiley.

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