Your upfront cost: $0
Subjects may require attendance
- 27 Jun 2022
QS RANKING 2022
Times Higher Education Ranking 2022
By the end of this 10-week course, you’ll be able to:
- Apply standard processes to prepare large data sets for data exploration.
- Perform data exploration on large data sets using visualisation, statistical techniques, and data mining techniques to identify relationships and opportunities.
- Develop accurate descriptive and predictive models based on large data sets.
- Perform predictive analytics on large data sets using an industry standard software toolset.
- Building Predictive Models
- Data Preparation
- Classification Models: KNN and Naïve Bayes
- Regression Problems
- Decision Trees
- Model Evaluation
- Neural Network
- Support Vector Machines
- Ensemble Methods
No eligibility requirements
No additional requirements
Many industries are turning to predictive analytics to improve and transform their current practices. In this course, you will be exposed to cutting-edge theory that will help you to understand the logic of key predictive analytics techniques. Learn how to thoughtfully design a response to a classification problem. These types of problems are found everywhere – from determining probable traces of cancer in an automated scanning process to picking the best new recruits for a professional sports team.
By the end of this course, you’ll leave with the skills to perform data exploration, visualisation, and preliminary analysis using predictive modelling techniques on large data sets.
UniSA Online’s 10-week short courses give you the flexibility to upskill in a certain area, stay current with developments in your field, diversify your knowledge, or even explore a new direction in your career – without having to commit to the time and cost of a full university degree.
Delivered 100% online, you’ll be able to study where and when it suits you. Access online academic and student support seven days a week, fit study around work and life commitments, view learning resources 24/7, and log in to the interactive online environment anywhere, any time and on any device.
This is a third-year course from UniSA Online’s Bachelor of Data Analytics degree. Please note: you are responsible for completing any relevant prerequisite courses before enrolling in this course.
Should your course have an exam it will be scheduled for Australian Central Standard Time or Australian Central Daylight Time, depending on the time of the year.
- Continuous Assessment (70%)
- Invigilated Exam - 2 hours (30%)
Current study term: 26 Jun 22 to 02 Sep 22
Introduction to Data Mining, Pearson New International Edition
Tan, P., et al.