|Year of offer||2018|
|Subject level||Undergraduate Level 3|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
This capstone subject for the Data Science major combines statistical reasoning and practical computing skills to solve challenging problems with big data.
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
INTENDED LEARNING OUTCOMES (ILOs)
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
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.
Eligibility and requirements
|Code||Name||Teaching period||Credit Points|
|MAST30025||Linear Statistical Models||
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
- Two individual assignments (up to 15 pages each), requiring approximately 30 hours of work, addressing Learning Outcomes (LOs) 1 to 4, due in weeks 3-4 and 6-7 40% (20% each)
- Group assignment (written component, up to 50 pages),requiring approximately 50 hours of work addressing LOs 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 (50%)
- Individual self-reflection report that also outlines the individual's contribution to the group assignment (requiring 10 hours work per student). The individual's contribution to the project will be measured using change logs from document and source-code repositories, due in the examination period (10%)
Dates & times
- Semester 2
Principal coordinator Chris Culnane Mode of delivery On Campus — Parkville Contact hours Total time commitment 170 hours Teaching period 23 July 2018 to 21 October 2018 Last self-enrol date 3 August 2018 Census date 31 August 2018 Last date to withdraw without fail 21 September 2018 Assessment period ends 16 November 2018
Time commitment details
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. Major Data Science
- 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