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Unit (2019)

Information on this page, including unit offerings, is from the 2019 academic year.

Data Analytics (ICT513)

School School of Engineering and Information Technology
Credit Points 3
Availability MURDOCH: S2-internal, S2-external
Teaching Timetables Murdoch S2
Description This unit examines topics relevant to data science. This includes data cleaning, summarising data using descriptive statistics and graphical displays, using linear models for both inference and prediction, and applying methods for dimension reduction and classification. Throughout, advanced statistical software will play an important role in data visualisation and analysis, and topics will be motivated through the presentation of a range of real research problems. A project will be used to simulate statistical problems commonly encountered by data scientists in the workplace.
Unit Learning Outcomes On successful completion of the unit students should be able to:
ULO1. Carry out a variety of statistical analyses using statistical software. In particular, students should be able to:
a. use linear regression for both inferential analyses and prediction,
b. apply the bootstrap and jackknife for variance estimation
c. use cross-validation to assess predictive performance for a model,
d. utilise principal component analysis as a means of dimension reduction,
e. use discriminant analysis and cluster analysis for classification,
f. and be familiar with a variety of other statistical methods.
ULO2. Explain conceptually the various statistical methods covered in the unit, the correct application of these methods, and interpret statistical software output.
ULO3. Write a technical report of findings based on statistical analyses.
Timetabled Learning Activities Lectures: 3 x 1 hour per week; tutorials: 1 x 1 hour per week; workshops: 3 x 2 hours per week (Weeks 1 - 3).
Unit Learning Experiences This unit uses a mixture of structured activities (in the form of lectures and tutorials), semi-structured activities (in the form of workshops), and assessments (in the form of assignments, a project and a final examination) to assist students in learning the material covered in the unit. It is essential for internal students to attend the tutorials in this unit.
Assessment Your ability to correctly apply statistical methods will be assessed at regular intervals during the semester via assignments and a project. These assessments are designed to allow you to demonstrate your ability in each of the content areas of the unit and to give you regular feedback on your progress, helping you to identify your areas of strength or weakness during the semester. Assignment solutions and results will be posted progressively on the Learning Management System.
The weightings for assessment items are as follows:
Assignments (3) - 30%
Project - 15%
Final Examination - 55%
Prerequisites Prior studies equivalent to MAS183 Statistical Data Analysis and enrolment in a Graduate IT course or permission of the Academic Chair.
Exclusions Students who have completed MAS223 Applied Statistics require permission of the Academic Chair to enrol.
Previously 2016: ICT509
Appears in these Courses/Majors:
see individual structures for context
Graduate Diploma in Data Science (GradDipDataSci)
Master of Business Administration + Master of Information Technology (Data Science) (MBA)+(MIT) [New in 2019]
Master of Information Technology (MIT)
Internet Access RequirementsMurdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.

Contacts

Unit Coordinator
ICT513
Associate Professor Nicola Armstrong
Associate Professor

Murdoch Campus
t: 9360 2480
e: N.Armstrong@murdoch.edu.au
o: 245.3.036 - Science and Computing, Murdoch Campus
Unit Contacts
ICT513

MURDOCH: S2-External
MURDOCH: S2-Internal
Associate Professor Nicola Armstrong
Associate Professor

Murdoch Campus
t: 9360 2480
e: N.Armstrong@murdoch.edu.au
o: 245.3.036 - Science and Computing, Murdoch Campus
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