Faculty of Information Technology

Monash University

Undergraduate - Unit

This unit entry is for students who completed this unit in 2013 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

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FacultyFaculty of Information Technology
OfferedClayton Second semester 2013 (Day)


In recent years the world has seen an explosion in the quantity and variety of data routinely recorded and analysed by research and industry, prompting some social commentators to refer to this phenomenon as the rise of "big data," and the analysts and practitioners who investigate the data as "data scientists."

The data may come from a variety of sources, including scientific experiments and measurements, or may be recorded from human interactions such as browsing data or social networks on the Internet, mobile phone usage or financial transactions. Many companies too, are realising the value of their data for analysing customer behaviour and preferences, recognising patterns of behaviour such as credit card usage or insurance claims to detect fraud, as well as more accurately evaluating risk and increasing profit.

In order to obtain insights from big data new analytical techniques are required by practitioners. These include computationally intensive and interactive approaches such as visualisation, clustering and data mining. The management and processing of large data sets requires the development of enhanced computational resources and new algorithms to work across distributed computers.

This unit will introduce students to the analysis and management of big data using current techniques and open source and proprietary software tools. Data and case studies will be drawn from diverse sources including health and informatics, life sciences, web traffic and social networking, business data including transactions, customer traffic, scientific research and experimental data. The general principles of analysis, investigation and reporting will be covered. Students will be encouraged to critically reflect on the data analysis process within their own domain of interest.


At the completion of this unit students will have -

A knowledge and understanding of:

  • analysing large data sets;
  • data cleansing and preparation;
  • open source and proprietary software for data analytics;
  • techniques and tools for data analytics;
  • validation of results.

Developed attitudes that enable them to:

  • model business problems by transforming the problem into an analytics problem that can then be solved using data analytics techniques. The insights from the analysis are then related back to the original business problem;
  • interpret data within a domain-specific context;
  • understand how data analytics may be used within organisations to understand current practice and identify potential opportunities;
  • appreciate the value of data analytics over traditional statistical analysis and modelling;
  • critically evaluate the limitations and benefits of data analytics.

Gained practical skills to:

  • manage large data;
  • prepare data for analysis;
  • analyse large data sets; in particular textual data sets;
  • construct and test the reliability of predictive models;
  • techniques and tools for data analytics.

Demonstrated the communication skills necessary to:

  • frame a business problem in terms of a formulation suitable for the application of data analytics tools;
  • communicate and report analysis and findings.


Examination (2 hours): 60%; In-semester assessment: 40%

Chief examiner(s)

Contact hours

2 hrs lectures/wk, 2 hrs laboratories/wk

This unit applies to the following area(s) of study


FIT1006, ETC1000 or equivalent. (For example BUS1100, ETC1010, ETC2010, ETF2211, ETW1000, ETW1010, ETW1102, ETW2111, ETX1100, ETX2111, ETX2121, MAT1097, STA1010)

Additional information on this unit is available from the faculty at: