Data Science Studio 1
Your upfront cost: $0
Online and other materials
Subjects may require attendance
- 29 Jun 2020
The University of New England is the only Australian public university to be awarded the maximum 5 stars for Overall Experience by the Good Universities Guide, 13 years in a row. UNE has delivered distance education since 1955—that’s longer than any other Australian university. Perhaps that’s why students continue to rate UNE so highly for student satisfaction and teaching quality. With over 170 degrees offered online, and more than 20,000 online students, UNE is the expert in online education.
QS RANKING 2020
Times Higher Education Ranking 2020
Upon completion of this subject, 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.
- Topics will be available to enrolled students in the subjects Learning Management System site approximately one week prior to the commencement of the teaching period.
You must either have successfully completed the following subject(s) before starting this subject, or enrol in the following subject(s) to study at the same time or prior to this subject:
- EquipmentDetails - Headphones or speakers (required to listen to lectures and other media) Headset, including microphone (highly recommended) Webcam (may be required for participation in virtual classrooms and/or media presentations).
- SoftwareDetails - It is essential for students to have reliable internet access in order to participate in and complete your units, regardless of whether they contain an on campus attendance or intensive school component. For additional information please visit UNE Hardware Requirements: https://www.une.edu.au/current-students/support/it-services/hardware
- TravelDetails - Travel may be required to attend the Final Examination for this subject.
- OtherDetails -
Textbook information is not available until approximately 8 weeks prior to the commencement of the Teaching period.
Students are expected to purchase prescribed material.
Textbook requirements may vary from one teaching period to the next.
The world now runs on data. If knowledge is power, data science is increasingly how we discover that knowledge.
This subject 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, exploration, and visualisation, as well as a conceptual understanding of many of the algorithms themselves.
The unit is designed to be exploratory and collaborative. Data science is a field that seeks to discover new knowledge and communicate it.
5 Quizzes at 2% each. Relates to Learning Outcomes (LOs) 1-5 Assessment 1: Data Science Assessment. Relates to Learning Outcomes (LOs) 1-4 Assessment 2: Data Science Assessment. Relates to Learning Outcomes (LOs) 1-4 Assessment 3: Collaborative data science assessment. Relates to Learning Outcomes (LOs) 1-5 Assessment 4: Computational Assessment. Relates to Learning Outcomes (LOs) 1-5 Final Examination. 2 hr 15 mins. Relates to Learning Outcomes (LOs) 1-5. There is a supervised exam at the end of the teaching period in which you are enrolled. The exam will either be paper-based and offered at an established exam venue or online with supervision via webcam and screen sharing technology. Coordinated by UNE Exams Unit. UNE manages supervised exams associated with your UNE subjects. Prior to census date, UNE releases exam timetables. They’ll email important exam information directly to your UNE email address.
- Quizzes (10%)
- Assessment 1 (10%)
- Assessment 2 (10%)
- Assessment 3 (30%)
- Assessment 4 (10%)
- Final Examination (30%)
Hands-On Machine Learningh with Scikit-Learn and TensorFlow