Key facts
UNE unit code: COSC531
*You are viewing the 2027 version of this unit which may be subject to change in future.
- Trimester 2 - On Campus
- Trimester 2 - Online
- Armidale Campus
- Yes
- No
- Yes
- 6
Unit information
Data has become a driving force that shapes decisions, innovations, and strategies across all industries and domains. Our capacity to capture and store data has expand at an unprecedented pace and, when coupled with machine learning and artificial intelligence, data can shape the experiences we encounter in many areas of our lives. This unit covers the technologies and paradigms that enable us to extract meaning from large datasets. You will work through topics that cover transformation, mapping and filtering data using workflows based on industry-leading platforms for big data processing. You will work with pre-processing and feature engineering approaches and use these in machine learning workflows for classification and regression tasks on large data sets.
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 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
COSC531 requires a knowledge of and programming experience with a high-level language. Experience with the use of the Linux/UNIX operating system is highly recommended.
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:
- describe and analyse the challenges involved with processing large datasets;
- develop applications and assess workflows for transforming, mapping and filtering large datasets using a distributed processing platform;
- work with datasets using a range of suitable structures, formats and tools that enable efficient processing at a large scale;
- analyse and apply pre-processing and feature engineering workflows to large datasets using a distributed processing platform;
- apply machine learning algorithms, interpret their outputs and assess their performance using suitable technologies and workflows; and
- analyse and develop programs that apply data processing and machine learning according to a given specification.
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 |
|---|---|---|---|---|
| Development Project - Assurance Task | Yes | 25% | All offerings | Major project. Implementation of a machine learning workflow to solve a complex problem. It is mandatory to pass this component in order to pass the unit. |
| Final Examination - Assurance Task | Yes | 60% | All offerings | Final Exam. Students must obtain 40% in this component in order to pass the unit. There is a supervised exam at the end of the teaching period in which you are enrolled. The exam will be offered online with supervision via webcam and screen sharing technology. Coordinated by UNE Exams Unit. |
| Practical Assignment 1 | Yes | 7.5% | All offerings | Programming Task |
| Practical Assignment 2 | Yes | 7.5% | All offerings | Programming Task |
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: Students are expected to purchase prescribed material. Please note that textbook requirements may vary from one teaching period to the next.
Spark: The Definitive Guide
ISBN: 9781491912218
Bill Chambers and Matei Zaharia, O'Reilly Media, Inc. February 2018
Text refers to: All offerings
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