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Master of Data Science (MC-DATASC) // Course structure
About this course
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/
Coordinator
Howard Bondell
Course structure
This 200 point Master is based around
- 125 points of compulsory subjects: four core subjects in statistics (50 points) , four core subjects in computer science (50 points) and a 25 point capstone project;
- and 25 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 |
COMP90049 | Knowledge Technologies |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
INFO90002 | Database Systems & Information Modelling |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
Core (125 points):
Students must take:
Statistics core subjects
Code | Name | Study period | Credit Points |
---|---|---|---|
MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90084 | Statistical Modelling | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90083 | Computational Statistics & Data Science | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90085 | Multivariate Statistical Techniques | 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 |
COMP90042 | Web Search and Text Analysis | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90051 | Statistical Machine Learning | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90050 | Advanced Database Systems | Semester 1 (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 |
Electives: (25 - 62.5)
The remainder of the 200 points are made up subjects that may include Discipline Elective Subjects, Professional Skills Subjects or an optional Data Science Research Project.
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 | Spatial Visualisation | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90110 | Analysis of High-Dimensional Data | Not available in 2019 | 12.5 |
MAST90111 | Advanced Statistical Modelling | Not available in 2019 | 12.5 |
MAST90051 | Mathematics of Risk | Not available in 2019 | 12.5 |
MAST90014 | Optimisation for Industry | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90027 | Practice of Statistics & Data Science | Not available in 2019 | 12.5 |
MAST90059 | Stochastic Calculus with Applications | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90081 | Advanced Probability | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90019 | Random Processes | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90054 | AI Planning for Autonomy | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90057 | Advanced Theoretical Computer Science | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90014 | Algorithms for Functional Genomics | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90016 | Computational Genomics | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90046 | Constraint Programming | Not available in 2019 | 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 1 (On Campus - Parkville) |
12.5 |
COMP90042 | Web Search and Text Analysis | Semester 1 (On Campus - Parkville) |
12.5 |
COMP90056 | Stream Computing and Applications | Semester 2 (On Campus - Parkville) |
12.5 |
ISYS90035 | Knowledge Management Systems | Not available in 2019 | 12.5 |
ISYS90086 | Data Warehousing | Summer Term (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)
Semester 2 (On Campus - Parkville)
|
12.5 |
EDUC90839 | Science in Schools | Not available in 2019 | 12.5 |
SCIE90017 | Science and Technology Internship |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
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:
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 |
Students must first have agreement from a supervisor before enrolling in the research project.
Last updated: 18 December 2020