Available in 2021
Course code



10 units


3000 level

Course handbook


The course introduces the students to the identification of patterns in data that can be used to derive knowledge for prediction and/or classification purposes. The course exposes the learners to a variety of established techniques and methodologies for the analysis of data.

The course is motivated by the inclusion of selected topics of data analytic problems arising in business and consumer analytics and data science and data engineering.

Availability2021 Course Timetables


  • Semester 2 - 2021

Learning outcomes

On successful completion of the course students will be able to:

1. Evaluate the processes and techniques for data analysis.

2. Apply well-established approaches and develop new systems for data analytics.

3. Discuss the practical, computational and scientific issues in data mining.


1)    Introduction to the Knowledge Discovery from Databases process: Representation issues and Feature Engineering.  

2)    Preprocessing of data: aggregation, sampling, discretization, attribute selection, identification of outliers, continuous and discrete measurements, missing values and imputation.  Decision trees, rule-based classifiers.

3)    Evaluating the performance of a classifier: precision, recall, TPR, FPR, TNR, FNR, sensitivity, specificity. Taking into account misclassification costs. The class imbalance problem. Confusion matrices. The Matthews Correlation Coefficient.

4)    Evaluating the performance of a model (cont.): cross-validation; bootstrap. Comparing models.

5)    Association rules (intro). The Apriori algorithm.

6)    Unsupervised methods: Basic concepts of clustering, K-means, the role of similarities measures. Clustering validation. Inter-rater reliability methods (Cohen’s and Fleiss’ kappa).   

Assumed knowledge

MATH1510 Discrete Mathematics, SENG1110 Object Oriented Programming

Assessment items

Written Assignment: Programming Assignment

Formal Examination: Final Exam *

* This assessment has a compulsory requirement.

Compulsory Requirements

In order to pass this course, each student must complete ALL of the following compulsory requirements:

Course Assessment Requirements:

  • Formal Examination: Minimum Grade / Mark Requirement - Students must obtain a specified minimum grade / mark in this assessment item to pass the course. - Students whose overall mark in the course is 50% or more, but who score less than 40% in the compulsory item and thus fail to demonstrate the required proficiency, will be awarded a Criterion Fail grade, which will show as FF on their formal transcript. However, students in this position who have scored at least 25% in the compulsory assessment item will be allowed to undertake a supplementary 'capped' assessment in which they can score at most 50% of the possible mark for that item.

Contact hours



Face to Face On Campus 2 hour(s) per Week for Full Term


Face to Face On Campus 2 hour(s) per Week for Full Term

The University of Newcastle acknowledges the traditional custodians of the lands within our footprint areas: Awabakal, Darkinjung, Biripai, Worimi, Wonnarua, and Eora Nations. We also pay respect to the wisdom of our Elders past and present.