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Data Mining - STAT828

Data mining is an important analytical tool as organisations deal with increasingly large data sets. It is about discovering patterns in the big data sets, and converting data into information or learning from data. Data mining uses techniques from different disciplines such as statistics, computing and machine learning. This unit introduces relevant data mining techniques using a white box approach to illuminate the underlying algorithms and statistical principles. This unit is designed to inform students about the data mining techniques by arming them with a deeper understanding of the algorithms and statistical principles underlying the techniques. At least two different software packages will be used to apply the different methods to discover information from different data sources. The first part of the unit will cover descriptive data mining, which will concentrate on exploratory tools such as graphical displays and descriptive statistics by using R and IBM SPSS Modeler. The second part will introduce the model building and predictive data mining such as classification, market basket analysis and clustering.

Credit Points: 4
When Offered:

S1 Evening - Session 1, North Ryde, Evening

S1 External - Session 1, External (with on campus sessions)

Staff Contact(s): Associate Professor Ayse Bilgin
Prerequisites:

 

Corequisites:

((Admission to MAppStat or GradCertAppStat or GradDipAppStat or MSc) and (STAT683 or STAT680)) or (admission to MActPrac or MInfoTech or MDataSc)

NCCW(s):
Unit Designation(s):

Commerce

Science

Assessed As: Graded
Offered By:

Department of Mathematics and Statistics

Faculty of Science and Engineering

Course structures, including unit offerings, are subject to change.
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