ITEC302 - Big Data Analytics

Year

Credit points

Campus offering

Prerequisites

ITEC202 Data Management and Visualisation

Teaching organisation

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

Unit rationale, description and aim

Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes. Big data refers to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big Data is a fast-evolving field where employers are increasingly desiring skilled practitioners in this area.

This unit will provide students with an in-depth understanding of the methods and technologies to tackle three key characteristics of Big Data: the volume, variety, and velocity of data (the "3 V's"). This unit will introduce (i) cloud and high-performance computing and storage infrastructure for dealing with large volumes of data; and (ii) data analytics methods, such as data integration, statistical analysis, and machine learning, for analysing a wide variety of data in combination.

The aim of this unit is to develop students' knowledge of big data analytics and hands-on experience of applying data analytics methods in the context of big data, also their ability to evaluate the effects of Big Data on society including ethical, sustainability, and social aspects.

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.

Content

Topics will include:

• Overview of Big Data analytics, Characteristics of Big Data, Analysis flow for Big Data. 
• NoSQL systems: HBase, MongoDB 
• Big Data acquisition and storage
• Big Data manipulation techniques: Batch Analytics (Hadoop Map-Reduce), Real-Time Analytics, Interactive Querying
• Big Data languages/tools: Pig; Spark, Hive
• Big Data applications (e.g. recommendation systems, time series analysis; text analytics)
• Effects of Big Data on society, including ethical, sustainability, and social aspects

Learning and teaching strategy and rationale

Students should anticipate undertaking 150 hours of study for this unit over a twelve-week semester or equivalent study period, including class attendance, readings, online forum participation and assessment preparation.

This unit is offered in different modes (“Attendance” mode and “Online” mode) 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 attendance 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 utilises an active learning approach whereby students will engage in e-module activities, readings and reflections, and opportunities to collaborate with peers in an online environment. This can involve, but is not limited to, online workshops, online discussion forums, chat rooms, guided reading, and webinars. To deliver core content, pre-recorded lectures will be incorporated within the online learning environment and e-modules. In addition, electronic readings will be provided to guide students’ reading and extend other aspects of online learning. 

Assessment strategy and rationale

Assessment methods incorporate problem-based tasks, case studies, and practical/hands-on tasks that are relevant to real-world needs. The first assessment item consists of several problem-based tasks designed to develop students' big data analytics skills and prepare them for the next two assessment tasks. Assessments 2 and 3 allow students to demonstrate the depth of their knowledge and understanding of big analytics technology concepts and tools. In assessment task 2 students will develop a proposal and plan for using big data analytics to solve a real-world problem. In assessment 3, students will provide evidence of their data analysis, and report their data findings/insights.

The assessment tasks for this unit are designed to demonstrate the achievement of each learning outcome. To pass this unit, students are required to obtain an overall mark of at least 50%.

Overview of assessments

Representative texts and references

Nataraj Dasgupta, 2018, Practical Big Data Analytics, Packt Publishing.

Simone Gressel, David J. Pauleen, Nazim Taskin, 2020, Management Decision-Making, Big Data and Analytics, Sage Publications.

Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020, Learning Spark, 2nd Edition, O'Reilly Media, Inc.

David Loshin, 2013, Big Data Analytics From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph. Elsevier Inc.