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Master of Data Science (MC-DATASC) // Course structure
About this course
Coordinator
Howard Bondell
Coordinator
Trevor Cohn
Contact
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Contact Stop 1
Future students:
- Further information: http://science.unimelb.edu.au/
Course structure
This 200 point Master is based around
- 75 points of compulsory subjects: three core subjects in statistics (37.5 points), three core subjects in computer science (37.5 points);
- 25 points of a capstone project;
- 50 points of prerequisite subjects; and 50 points of electives.
Subject options
‘Prerequisite’ Subjects (up to 50 points depending on educational background)
It is expected that students admitted into the course will have either computer science or statistics background, though some students may have a mix of both.
- If a student has a computer science background, they will be required to complete statistics subjects in their first year as part of their elective component to satisfy the prerequisites for the Statistics core subjects
- If the student has Statistics background, they will need to complete computer science subjects in their first year as part of their elective component to satisfy the prerequisites for the Computer Science core subjects
- Students entering the program with the equivalent of both Statistics and Computer Science majors accelerate through to a 150 point program.
Dependent upon educational background students may need to take up to 50 points of the following:
Student entering with a Computer Science background may need to take
Code | Name | Study period | Credit Points |
---|---|---|---|
MAST90105 | Methods of Mathematical Statistics | Semester 1 (On Campus - Parkville) |
25 |
MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
Student entering with a Statistics background may need to take:
Code | Name | Study period | Credit Points |
---|---|---|---|
COMP90041 | Programming and Software Development |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90038 | Algorithms and Complexity |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP20008 | Elements of Data Processing |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
INFO90002 | Database Systems & Information Modelling |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Core (100 points):
Students must take:
Statistics core subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
MAST90139 | Statistical Modelling for Data Science | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90138 | Multivariate Statistics for Data Science | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90083 | Computational Statistics & Data Science | Semester 2 (On Campus - Parkville) |
12.5 |
Computer Science core subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
COMP90024 | Cluster and Cloud Computing | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90050 | Advanced Database Systems |
Semester 1 (On Campus - Parkville)
Winter Term (On Campus - Parkville)
|
12.5 |
COMP90051 | Statistical Machine Learning |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Capstone Project
Students are expected to have completed at least two semesters of their course before beginning the Data Science Project
Code | Name | Study period | Credit Points |
---|---|---|---|
MAST90106 | Data Science Project Pt1 | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90107 | Data Science Project Pt2 | Semester 2 (On Campus - Parkville) |
12.5 |
DATA SCIENCE RESEARCH PROJECT*
*Students who maintain a sufficiently high weighted average mark will be eligible to undertake a 25 point individual research project in Data Science as the capstone project, to replace MAST90106 and MAST90107. Students must first have agreement from a supervisor before enrolling in the research project.
Code | Name | Study period | Credit Points |
---|---|---|---|
MAST90108 | Data Science Research Project Pt1 |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
MAST90109 | Data Science Research Project Pt2 |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Electives: (50 points)
The remainder of the 200 points are made up subjects that may include Discipline Elective Subjects, Professional Skills Subjects, or elective subjects outside of the discipline electives with the approval of the course coordinator.
Discipline Elective Subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
GEOM90008 | Foundations of Spatial Information |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
GEOM90018 | Spatial Databases | Semester 1 (On Campus - Parkville) |
12.5 |
GEOM90006 | Spatial Analysis | Semester 2 (On Campus - Parkville) |
12.5 |
GEOM90007 | Information Visualisation | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90110 | Analysis of High-Dimensional Data | Not available in 2020 | 12.5 |
MAST90111 | Advanced Statistical Modelling | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90051 | Mathematics of Risk | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90014 | Optimisation for Industry | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90027 | Practice of Statistics & Data Science | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90059 | Stochastic Calculus with Applications | Not available in 2020 | 12.5 |
MAST90081 | Advanced Probability | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90019 | Random Processes | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90125 | Bayesian Statistical Learning | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90054 | AI Planning for Autonomy |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90057 | Advanced Theoretical Computer Science | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90014 | Algorithms for Bioinformatics | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90016 | Computational Genomics | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90046 | Constraint Programming | Not available in 2020 | 12.5 |
COMP90043 | Cryptography and Security | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90048 | Declarative Programming | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90020 | Distributed Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90015 | Distributed Systems |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90007 | Internet Technologies |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP90018 | Mobile Computing Systems Programming | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90025 | Parallel and Multicore Computing | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90045 | Programming Language Implementation | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90042 | Natural Language Processing | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90056 | Stream Computing and Applications | Semester 2 (On Campus - Parkville) |
12.5 |
ISYS90035 | Knowledge Management Systems | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90073 | Security Analytics | Semester 2 (On Campus - Parkville) |
12.5 |
Professional Skills Subjects
Students may take no more than 25 points of the following:
Code | Name | Study period | Credit Points |
---|---|---|---|
SCIE90012 | Science Communication | Semester 2 (On Campus - Parkville) |
12.5 |
SCIE90013 | Communication for Research Scientists |
Semester 1 (On Campus - Parkville)
Winter Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
EDUC90839 | Science in Schools | Not available in 2020 | 12.5 |
SCIE90017 | Science and Technology Internship |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Last updated: 18 December 2020