This is a Further IT core unit in the BBIS (as an alternative to FIT3003).

10/02/2021: Admin - Adding Reasons for Change for CE - modified Workload to reflect that tutorials (not labs) are being run in this unit. Tutorials were always run, although historically we needed to call them labs so they would be scheduled in computer laboratories (students use data science software as well as performing hand calculations in the tutorials).

21/09/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

20/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

25/01/2018 - Admin: updated prereqs to include ETF1100 as advised by BusEco.

19/09/2017: Admin - adding Reasons for Change on behalf of the CE - add prohibition of BusEco unit for Semester 1, 2018 implementation.

12/06/2017: Admin - updating location of offering to reflect actual campus offerings at the ADE's request.

Sept 2015: Updated for course architecture programs. Effective 2016.

June 2014: Update objectives to learning outcomes for AQF compliance.

November 2012: Added STA1010 to list of possible prerequisites to make the unit available to Science students and BCS students completing the decision support major.

SEPT 2016: This is a co-core unit in the BIS major of the BIT and one of 4 level 3 units (of which students must choose 2) from the Data Science specialisation in the Bachelor of Computer Science. This unit aims to provide students with the necessary analytical and data modelling skills for the role of data scientist. Students will be introduced to established and contemporary techniques for data exploration, analysis and presentation.

The unit is also a core unit (alternative to FIT3003) in the third year of the Bachelor of Business Information Systems. It is part of the Decision Support Systems stream. Together with (FIT1006, FIT2017 and) FIT3003 this unit aims to provide students with the necessary analytical and data modelling skills for the roles of a business analyst and data scientist. Students will be introduced to established and contemporary techniques for data exploration, analysis and presentation.

The applicability of the modelling and analysis techniques taught to data from a wide variety of sources make this unit relevant to students considering a wide range of careers. In particular the unit is relevant to studying any discipline where access to large data sets arising from observation, experimentation or human activity is likely. Thus, students from the physical and social sciences; business and economics, especially finance and risk modelling, as well as medical and biomedical research and related fields would benefit from this unit.

On completion of this unit, you should be able to:

1. demonstrate the ability to transform real world problems into ones that can then be solved using data analytics techniques;
2. cleanse and prepare data for analysis;
3. analyse large data sets using a range of statistical, graphical and machine-learning techniques;
4. validate and critically assess the results of analysis;
5. interpret the results of analysis and communicate these to a broad audience.

020307

In recent years the world has seen an explosion in the quantity and variety of data routinely recorded and analyzed 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 behavior 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.

Linhoff, G. S. and Berry, M. J. A., Data Mining Techniques 3rd Ed, Wiley, 2011

Recommended resources

M. Allerhand. (2011) A Tiny Handbook of R. SpringerLink (Online service), Online access via Library.

G. James, D. Witten, D, T. Hastie, R. Tibshirani. (2013) An Introduction to Statistical Learning. Springer. Online access via Library.

F. Provost and T. Fawcett. (2013) Data Science for Business. O'Reilly Media, Inc.

P.-N. Tan, M. Steinbach, V. Kumar. (2006) Introduction to Data Mining. Addison-Wesley.

W. N. Venables, D. M. Smith. (2018) An Introduction to R. Available from: http://www.cran.r-project.org/doc/manuals/R-intro.pdf.

H. Wickham, G Gromelund. (2017) R for Data Science. O'Reilly Media, Inc. Also available online from: http://r4ds.had.co.nz/

On-campus

Lecture and tutorials or problem classes This teaching and learning approach helps students to initially encounter information at lectures, discuss and explore the information during tutorials, and practice in a hands-on lab environment.

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

1. Assignment 1: - 20% - ULO: 1, 2, 3, 4, 5
2. Assignment 2: - 20% - ULO: 1, 2, 3, 4, 5
3. Examination 1: - 60% - ULO: 1, 2, 3, 4, 5

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Additional requirements (all students):

FIT

FIT1006, ETC1000, ETF1100, FIT2086 or equivalent. (For example BUS1100, ETC1010, ETC2010, ETF2211, ETW1000, ETW1010, ETW1102, ETW2111, ETX1100, ETX2111, ETX2121, MAT1097, STA1010)