COMP6340
10 units
6000 level
Course handbook
Description
The course introduces students to the identification of patterns in data that can be used to derive knowledge for prediction and/or classification purposes. The course exposes 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.
Availability2024 Course Timetables
Online
- Semester 2 - 2024
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.
4. Discuss the key aspects of data mining.
Content
- Introduction to the Knowledge Discovery from Databases process: Representation issues and Feature Engineering.
- Preprocessing of data: aggregation, sampling, discretization, attribute selection, identification of outliers, continuous and discrete measurements, missing values and imputation. Decision trees, rule-based classifiers.
- 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.
- Evaluating the performance of a model (cont.): cross-validation; bootstrap. Comparing models.
- Association rules (intro). The Apriori algorithm.
- Unsupervised methods: Basic concepts of clustering, K-means, the role of similarities measures. Clustering validation. Inter-rater reliability methods (Cohen’s and Fleiss’ kappa).
Requisite
This course has similarities to COMP3340. If you have completed COMP3340 you cannot enrol in this course.
Assumed knowledge
MATH1510 Discrete Mathematics, SENG6110 Object Oriented Programming
Assessment items
Written Assignment: Programming Assignment
Online Open Book Formal Examination: Final Exam
Compulsory Requirement: Pass requirement 40% - Must obtain 40% in this assessment item to pass the course.
Contact hours
Semester 2 - 2024 - Online
Lecture-1
- Online 2 hour(s) per week(s) for 13 week(s) starting in week 1
Workshop-1
- Online 2 hour(s) per week(s) for 13 week(s) starting in week 1
Course outline
Course outline not yet available.
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.