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Applied Data Science (MAST30034)
Undergraduate level 3Points: 12.5Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Overview
Availability | Semester 2 - Dual-Delivery |
---|---|
Fees | Look up fees |
AIMS
This capstone subject for the Data Science major combines statistical reasoning and practical computing skills to solve challenging problems with big data.
INDICATIVE CONTENT
Students will learn about communication of quantitative information and insights; presentation skills; report writing; project management; problem formulation using case studies; data collection and measurement protocols; data from surveys and experiments; issues in capturing and dealing with “big data”; dimension reduction; data visualisation; fitting formulated models to data to infer insightful information about populations; ethics in quantitative research; working effectively in teams.
Intended learning outcomes
On completion of this subject students should be able to extract useful information from large data sets. In particular they should be able to:
- Display data in a manner that highlights its principal structures
- Formulate problems presented to them according to data science principles
- Organise and structure data for the purposes of analysis, particularly "big data"
- Fit and refine models
- Understand the dynamics of working in a team
- Communicate effectively with team members and non-technical subject matter experts
- Harness the collective skills in a team to achieve an efficient outcome
- Apply sound ethical principles to ethical issues that arise in data science
- Interpret the results of analysis and communicate insights effectively
Generic skills
In addition to learning specific statistical and computational skills, in this subject you will have the opportunity to develop generic skills that will assist you in your future career. You will develop communication, co-operation and problem solving skills (especially through the group assignment), such as how to come up with relevant strategies to solve unfamiliar problems. You will develop skills in working as a team, and you will develop analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP30027 | Machine Learning | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Description | Timing | Percentage |
---|---|---|
Two individual assignments (up to 15 pages each), addressing Intended Learning Outcomes (ILO's) 1 to 4, due in weeks 3-4 and 6-7 (20% each). (*)
| From Week 3 to Week 7 | 40% |
Group assignment (written component, up to 50 pages), addressing ILO's 1 to 9, and especially 5 to 9. To proceed in two phases: formulation followed by a review in weeks 9-10, then implementation, due in week 12.(*)
| Week 12 | 50% |
Individual self-reflection report that also outlines the individual's contribution to the group assignment. The individual's contribution to the project will be measured using change logs from document and source-code repositories.
| During the examination period | 10% |
Additional details
*Due to extenuating factors related to COVID-19, assessment due dates for this subject in 2021 may differ with Handbook publication. Please check your subject LMS page at the start of semester for confirmed assessment timings.
Last updated: 3 November 2022
Dates & times
- Semester 2
Coordinator Karim Seghouane Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours, comprising of one 1-hour lecture and one 2-hour workshop per week Total time commitment 170 hours Teaching period 26 July 2021 to 24 October 2021 Last self-enrol date 6 August 2021 Census date 31 August 2021 Last date to withdraw without fail 24 September 2021 Assessment period ends 19 November 2021 Semester 2 contact information
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
None
- Related Handbook entries
This subject contributes to the following:
Type Name Major Data Science Informal specialisation Science Discipline subjects - new generation B-SCI - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022