Data Science Studio 1
UndergraduateUNE-COSC1022024
Course information for 2024 intake View information for 2025 course intake
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- Study method
- 100% online
- Assessments
- 100% online
- Entry requirements
- Part of a degree
- Duration
- 16 weeks
- Loan available
- FEE-HELP available
Data Science Studio 1
About this subject
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.
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 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 workflows, exploration, and visualisation, as well as a conceptual understanding of many of the algorithms themselves. Designed to be exploratory and collaborative, the subject offers you invaluable insights into a rapidly evolving field with diverse and increasingly important applications.
5 Quizzes at 2% each. Relates to Learning Outcomes 1-5;
Assessment 1: Data Science Assessment. Relates to Learning Outcomes 1-4;
Assessment 2: Data Science Assessment. Relates to Learning Outcomes 1-4;
Assessment 3: Collaborative data science assessment. Relates to Learning Outcomes 1-5;
Assessment 4: Computational Assessment. Relates to Learning Outcomes 1-5;
Final Examination. Open Book Examination - 2 hr 15 mins. It is mandatory to pass this component in order to pass this unit. Relates to Learning Outcomes 1-5.
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 - Data science assessment (10%)
- Assessment 2 - Data science assessment (10%)
- Assessment 3 - Collaborative data science assessment (30%)
- Assessment 4 - Computational assessment (10%)
- Final Examination - Open Book Examination 2 hrs 15 min (30%)
For textbook details check your university's handbook, website or learning management system (LMS).
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Entry requirements
Part of a degree
To enrol in this subject you must be accepted into one of the following degrees:
Elective
- UNE-INF-DIP-2024 - Diploma in Information Technology
Prior study
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:
Additional requirements
- Equipment requirements - 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).
- Software requirements - 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
- Other requirements -
Textbook requirements:
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.
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.
Related degrees
Once you’ve completed this subject it can be credited towards one of the following courses
Diploma in Information Technology
UndergraduateUNE-INF-DIP