ITEC202 - Data Analytics and Decision Making

Year

Credit points

Campus offering

Prerequisites

ITEC102 Python Fundamentals For Data Science ORITEC200 Data and Information Management

Incompatible

DATA201 Data Analytics and Visualisation , ITEC300 Data Visualisation , DATA300

Teaching organisation

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

Unit rationale, description and aim

The increasing use of digital technologies has resulted in generation and storage of vast amount of data. Data needs to be processed to provide valuable insights that enable improved decisions, processes, products or services. Hence, increasingly, organisations need people with the ability to extract, consolidate, analyse data from diverse sources and present information generated from data in an insightful manner, such as visualisations.

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 statistical computing and graphing techniques using the R programming language to generate useful information from data. In addition, students will learn 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.

The primary aim of this unit is to equip students with the 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 various computational and statistical techniques for data analytics and their applications (GA5, GA8)

LO2 - Apply data analytics tools and techniques to derive useful insights from raw data (GA5, GA10)

LO3 - Develop and evaluate basic predictive analytics models (GA4, GA5)

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 using visualisation software packages (e.g. Tableau, PowerBI)
• Fundamentals of R programming language
• Data Visualisation with R
• Exploratory Data Analysis with R
• Data Wrangling/Transformation with R
• Basic Modelling and Predictive analytics with R (Linear and Logistic Regression, Time series forecasting)
• Evaluation of predictive models
• 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. These are: “Attendance” mode, “Blended” mode and “Online” mode. This unit is offered in three modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In a weekly attendance mode, students will require face-to-face attendance in specific physical location/s. 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, most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.

Blended Mode

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.

Online Mode

This unit uses an active learning approach to support students in the exploration of the essential knowledge associated with working with technology. Students can explore the essential knowledge underpinning technological advances and develop knowledge in a series of online interactive lessons and modules. Students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge of working with technology. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

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

Assessment strategy and rationale

The assessment strategy for this unit is based on the need to determine authentic student achievement of the learning outcomes. 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 equipment students with the skills of creating novel data visualisations to effectively reveal the narratives behind the data. In assessment task 2, students will apply analytical techniques to build and evaluate basic predictive models. 

Overview of assessments

Representative texts and references

Zumel, N., Mount, J. 2020. Practical data science with R. Shelter Island, 2nd edition, NY: Manning Publications Co.

Wickham, H & Grolemund, G 2017, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O'Reilly Media.

Long, J 2019, R Cookbook, 2nd edition, O'Reilly Media.

Chang, W 2018, R Graphics Cookbook: Practical Recipes for Visualizing Data, 2nd edn, O'Reilly Media.

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.