Key facts
UNE unit code: COSC102
*You are viewing the 2026 version of this unit which may be subject to change in future.
- Trimester 2 - On Campus
- Trimester 2 - Online
- Armidale Campus
- UNE Sydney Campus
- Yes
- No
- Yes
- 6
Unit information
The world now runs on data. If knowledge is power, data science is increasingly how we discover that knowledge. With limitless possibilities for exploration and innovation, it is a field driven by discovery and communication.
This unit introduces you to data science using simple and efficient toolkits in Python. You will explore various datasets and apply machine learning algorithms to them.
In doing so, you will gain an understanding of data processing workflows, exploration, and visualisation, as well as a conceptual understanding of many of the algorithms themselves.
Designed to be exploratory and collaborative, the unit offers you invaluable insights into a rapidly evolving field with diverse and increasingly important applications.
Offerings
For further information about UNE's teaching periods, please go to Principal Dates.
| Teaching period | Mode/location |
|---|---|
| Trimester 2 | On Campus, Armidale Campus |
| Trimester 2 | On Campus, UNE Sydney Campus |
| Trimester 2 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
Please refer to the student handbook for current details on this unit.
Unit coordinator(s)
Learning outcomes
Upon completion of this unit, students will be able to:
- explain the concepts behind introductory machine learning algorithms for classification and clustering;
- apply machine learning libraries and toolkits to explore datasets and discover knowledge;
- visualise the output of machine learning algorithms and describe their meaning;
- select appropriate techniques to clean and analyse data; and
- explain and consider ethical issues in data science.
Assessment information
Assessments are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
| Title | Must Complete | Weight | Offerings | Assessment Notes |
|---|---|---|---|---|
| Assessment 1 | Yes | 10% | All offerings | Data science assessment |
| Assessment 2 | Yes | 10% | All offerings | Data science assessment |
| Assessment 3 - Assurance Task | Yes | 30% | All offerings | Portfolio Assessment with Verification (Collaborative data science assessment). Students must pass the Final Examination and also pass the sum of the Final Examination and Assessment 3 in aggregate in order to pass the unit. |
| Assessment 4 | Yes | 10% | All offerings | Computational assessment |
| Final Examination - Assurance Task | Yes | 30% | All offerings | Online proctored examination. Students must pass the Final Examination and also pass the sum of the Final Examination and Assessment 3 in aggregate in order to pass the unit. |
| Quiz | No | 10% | All offerings | 5 Quizzes at 2% each |
Learning resources
Textbooks are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
Note: Recommended material may be held in the University Library — purchase is optional.
Hands-On Machine Learning with Scikit-Learn and TensorFlow
ISBN: 9781098125974
Geron, A., O'Reilly 3rd ed. 2022
Text refers to: All offerings
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