UO Predictive Analytics

Course aim

To enable students to perform, on large data sets,  data exploration, visualisation, and preliminary analysis using predictive modelling techniques.

Course content

Data exploration, visualisation and preliminary analysis: advanced exploratory data analysts; data visualisation; data quality verification; missing data analysis; data manipulation and cleaning.
Predictive modelling: logistic regression and generalised linear models; decision trees; random forests; generalised boosted models; naive Bayes classifiers and other techniques.

Textbooks

Tan, P; Steinbach, M; Kumar, V 2013, Introduction to Data Mining: Pearson New International Edition, Pearson

Prerequisites

Corequisite(s)

Nil

Teaching Method

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Assignment, Continuous assessment, Examination

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
How to determine your Commonwealth Supported course fee. (Opens new window)

Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
Non-award tuition fees are set by the university. To determine the cost of this course, go to:
How to determine the relevant non award tuition fee. (Opens new window)

Not all courses are available on all of the above bases, and students must check to ensure that they are permitted to enrol in a particular course.

* Equivalent Full Time Study Load. Please note all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation