Big Data

10 Units

The pervasion of information technology into every aspect of life and the recent explosion of social media have resulted in the creation of huge volumes (Big Data) of typically unstructured data: web logs, videos, speech, photographs, purchase patterns, e-mails, GPS data and Tweets. Organisations have understood that there is a strong need for professional data analysts who are able to manage big data, to apply appropriate data analytics techniques (e.g. statistical learning, classification, and dimension reduction methods), and to utilise the critical knowledge within these enormous amounts of data. This course brings together several key theories and technologies used in manipulating, storing, and analyzing big data.

Faculty Faculty of Engineering and Built Environment
School School of Electrical Engineering and Computing
Availability Trimester 3 - 2018 (Callaghan)
Trimester 3 - 2018 (Online)
Learning Outcomes

On successful completion of this course, students will be able to:

  1. An understanding of the problems and limitations of existing enterprise technology in handling large datasets and how Big Data technologies can be used to overcome them.
  2. An appreciation of the business requirements and applications of Big Data technologies.
  3. Acquired the knowledge and skills required to use contemporary Big Data technologies to store, manipulate and analyse large unstructured data sets.

Students will learn;

  1. The Characteristics of Big Data
  2. Structured, Unstructured and High Dimensional Data
  3. Data Intensive Distributed Applications and Architectures
  4. Data Analytics Techniques for Big Data
  5. Implementation of Data Analytics Techniques in Software Environments
  6. Extraction, communication and retention of Critical Knowledge from Big Data for Decision Support
  7. Integration of Big Data into Enterprise Systems
  8. Design and Production of Reports from Large Unstructured Datasets
Assumed Knowledge Exposure to modern Database technology
Assessment Items
  • Project: Projects
  • Written Assignment: Data Analysis Report
  • Written Assignment: Essays / Written Assignments

Contact Hours


Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term


Face to Face On Campus 1 hour(s) per Week for Full Term



Online 1 hour(s) per Week for Full Term


Online 2 hour(s) per Week for Full Term

Timetable 2018 Course Timetables for INFT6201
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