ITEC622 - Data Analytics and Decision Making

Year

Credit points

Campus offering

Prerequisites

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

Teaching organisation

 3 hours per week for twelve weeks or equivalent.

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 to apply statistical computing and graphics techniques using the R programming language to generate useful information from data. In addition, this unit will develop students’ skill in using data visualisation 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 equipping 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 various computational and statistical concepts and techniques and their applications in data analytics and visualisation (GA5, GA8)

LO2 - Create and evaluate data visualisation artefacts such as charts and dashboards to support evidence-based decisions and preservation of the common good, environment and society (GA2, GA5) 

LO3 - Apply data analytics concepts, tools and techniques to real life data to support evidence-based decisions (GA5, GA10)

LO4 - Develop and evaluate predictive models and justify choice of analytical techniques for evidence-based decisions (GA5, GA10)

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:

• Fundamentals of R programming language
• Statistical techniques for Data Analytics
• 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
• Data Visualisation with R
• Introduction to Data Visualisation tools (e.g. Tableau, PowerBI)
• Big Data visualisation
• Social and environmental applications of Data Analytics and Visualisation

Learning and teaching strategy and rationale

This unit is offered in different modes over a twelve-week semester or equivalent study period. 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.

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 workshop and one-hour lab for the achievement of the unit learning outcomes. Workshops facilitate learning by theory comprehension and problem solving while lab sessions focus on hands on practices, which in combination is particularly effective for learning information technology skills.

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 data cleaning/transformation and exploratory data analysis on a dataset and produce descriptive analytics models using the R programming language. In assessment task 2, students will apply analytical techniques to build and evaluate basic predictive models. In assessment task 3, 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.

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