University Handbook 2017

Master of Data Science

Course code/s: SMDS

Course details
Location Bundoora
Course code SMDS
Course coordinator Dr Andriy Olenko
Available to international students Yes
CRICOS 092396B
Course duration 2 years full-time, or part-time equivalent
Credit points 240 credit points
Exchange opportunity No
Leave of absence available No
Course queries Current students: ASK La Trobe
Prospective students: Future Students
Notes  

Course description

The Master of Data Science is a two-year degree which enables students to develop the knowledge and skills to work as data scientists in a wide range of industries including: science, business, health, agriculture, sport, transport, or logistics.  There is increasingly high demand for an accurate extraction of insightful knowledge from big data collection, and for the ability to make predictions on future trends and performance.  The complexity and volume of big data collections (images, documents, social media data, streaming sensor data) drives the need for a unique set of skills in big data computing, analytical modelling and intelligence systems which underpins the Data Science degree. 

Specifically, this course has three major specialisation areas:

  • Big Data and Cloud Computing
  • Analytical Science
  • Bioinformatics

Each major includes a set of core advanced subjects from the computer science and mathematics/statistics disciplines and a number of specialised advanced subjects including big data management, computational intelligence for analytics, data exploration and visualisation, cloud systems development, analysis of repeated measures data, meta-analysis, and bioinformatics technologies.

Course intended learning outcomes

Course Intended Learning Outcomes (CILOs) are brief statements defining what students are expected to demonstrate they know and can do by the end of a course.

Communicate information and critical analysis in the fields of data science.
Critically analyse and solve problems in the fields of data science by using multi-disciplinary approaches.
Demonstrate the ability to assess whether the application of computing techniques and mathematical and statistical approaches are appropriate for a particular scientific problem in the fields of data science.
Demonstrate a highly developed, integrated understanding and ability to apply complex theoretical concepts that underpin the field of data science.

Course structure

The course requires the completion of 240 credit points over two years of full-time or equivalent part-time study with a minimum of 120 credit points completed at fifth year level.

Core subjects

Core subjects (60 credit points)
Teaching period Subject name Subject code Credit points
TE-SEM-1 Academic Integrity Module (online) * LTU0AIM 0
Students must choose 4 of the following fundamental subjects depending on their background:
Computer Science students must undertake MAT/STA coded fundamental subjects.  Mathematics and Statistics students must undertake CSE coded fundamental subjects.
TE-SEM-1 Database Fundamentals CSE4DBF 15
TE-SEM-1 or TE-SEM-2 Object-Oriented Programming Fundamentals CSE4OOF 15
TE-SEM-1 or TE-SEM-2 Intermediate Object-Orientated Programming CSE4IOO 15
TE-SEM-1 Number Systems and Linear Algebra MAT4NLA 15
TE-SEM-2 Calculus and Differential Equations MAT4CDE 15
TE-SEM-2 Statistical Science STA4SS 15
TE-SEM-2 Probability Models STM4PM 15

Key: * LTU0AIM (formerly SCI5AIM) is a not-for-credit subject that you are required to complete at the commencement of your first semester. The subject is designed to enhance your knowledge and awareness of issues concerning academic integrity.

Big data and cloud computing specialisation

Big Data and Cloud Computing Specialisation (180 credit points)
Teaching period Subject name Subject code Credit points
TE-SEM-1 Big Data Management on the Cloud CSE5BDC 15
TE-SEM-1 Computational Intelligence for Data Analytics CSE5CI 15
TE-SEM-2 Data Exploration and Visualisation CSE5DEV 15
TE-SEM-2 Data Mining CSE5DMI 15
TE-SEM-2 Web Development on the Cloud CSE5WDC 15
  Students select a STA5 subject from the following list:    
TE-SEM-1 Analysis of Repeated Measures STA5ARM 15
TE-SEM-2 Meta Analysis STA5MA 15
TE-SEM-2 Models for Bioinformatics STA5MB 15
  Students must choose between the Thesis or Industry Based Learning subjects from the following list:    
TE-SEM-1 or TE-SEM-2 Industry Project in Data Science CSE5ITP 30
TE-SEM-1 or TE-SEM-2 Industry Placement STM5IPL 30
TE-SEM-1 or TE-SEM-2 Industry Based Learning CSE5IBL 30
TE-SEM-1 or TE-SEM-2 Statistics Thesis STA5THA and STA5THB 45
TE-SEM-1 or TE-SEM-2 Computer Science Thesis CSE5TSA and CSE5TSB 45
Students must complete CSE4, CSE5, MAT4, MAT5, STA4, STA5 or other approved elective subjects from the list below to the value of 45-60 credit points.
If Thesis subjects undertaken 45 credit points of electives are required.
If Industry Based Learning subjects undertaken 60 credit points of electives are required.
45-60

Big data and cloud computing specialisation elective subjects

Big data and cloud computing specialisation elective subjects
Subject name Subject code Credit points
Decision Support Systems CSE5DSS 15
Advanced Databases CSE5ADB 15
Bioinformatics Technologies CSE5BIO 15
Data Warehouse Concepts and Design CSE5DWD 15
Algorithms and Data Structures (PG) CSE5ALG 15
Epidemiology and Research Methods DTN4EPI 15
Epidemiology and Demography PHE5EPI 15
Methods in GIS ARC5GIM 15
Health Data Analysis A HIM4AHA 15
Predictive Analysis BUS5PA 15
Visual Analysis BUS5VA 15
Customer Analytics and Social Media BUS5CA 15
Advanced Time-Series Econometrics BUS5ATE 15

Bioinformatics Specialisation

Bioinformatics Specialisation (180 credit points)
Teaching period Subject name Subject code Credit points
TE-SEM-1 Big Data Management on the Cloud CSE5BDC 15
TE-SEM-1 Bioinformatics Technologies CSE5BIO 15
TE-SEM-2 Data Exploration and Visualisation CSE5DEV 15
TE-SEM-2 Data Mining CSE5DMI 15
TE-SEM-2 Bioinformatics BIO5INF 15
TE-SEM-1 or TE-SEM-2 Models for Bioinformatics STA5MB 15
  Students must choose between the Thesis or Industry Based Learning subjects from the following list:    
TE-SEM-1 or TE-SEM-2 Industry Project in Data Science CSE5ITP 30
TE-SEM-1 or TE-SEM-2 Industry Placement STM5IPL 30
TE-SEM-1 or TE-SEM-2 Industry Based Learning CSE5IBL 30
TE-SEM-1 or TE-SEM-2 Statistics Thesis STA5THA and STA5THB 45
TE-SEM-1 or TE-SEM-2 Computer Science Thesis CSE5TSA and CSE5TSB 45
Students must complete CSE4, CSE5, MAT4, MAT5, STA4, STA5 or other approved elective subjects from the list below to the value of 45-60 credit points.
If Thesis subjects undertaken 45 credit points of electives are required.
If Industry Based Learning subjects undertaken 60 credit points of electives are required.
45-60

Bioinformatics specialisation elective subjects

Bioinformatics specialisation elective subjects
Subject name Subject code Credit points
Decision Support Systems CSE5DSS 15
Advanced Databases CSE5ADB 15
Data Warehouse Concepts and Design CSE5DWD 15
Algorithms and Data Structures (PG) CSE5ALG 15
Computational Intelligence for Data Analytics CSE5CI 15
Web Development on the Cloud CSE5WDC 15
Epidemiology and Research Methods DTN4EPI 15
Epidemiology and Demography PHE5EPI 15
Methods in GIS ARC5GIM 15
Health Data Analysis A HIM4AHA 15
Visual Analysis BUS5VA 15

Analytical Science Specialisation

Analytical Science Specialisation (180 credit points)
Teaching period Subject name Subject code Credit points
TE-SEM-1 Big Data Management on the Cloud CSE5BDC 15
TE-SEM-2 Data Exploration and Visualisation CSE5DEV 15
TE-SEM-1 Analysis of Repeated Measures STA5ARM 15
TE-SEM-2 Meta Analysis STA5MA 15
TE-SEM-2 Models for Bioinformatics STA5MB 15
  Students must choose between the Thesis or Industry Based Learning subjects from the following list:    
TE-SEM-1 or TE-SEM-2 Industry Project in Data Science CSE5ITP 30
TE-SEM-1 or TE-SEM-2 Industry Placement STM5IPL 30
TE-SEM-1 or TE-SEM-2 Industry Based Learning CSE5IBL 30
TE-SEM-1 or TE-SEM-2 Statistics Thesis STA5THA and STA5THB 45
TE-SEM-1 or TE-SEM-2 Computer Science Thesis CSE5TSA and CSE5TSB 45
Students must complete CSE4, CSE5, MAT4, MAT5, STA4, STA5 or other approved elective subjects from the list below to the value of 60-75 credit points.
If Thesis subjects undertaken 60 credit points of electives are required.
If Industry Based Learning subjects undertaken 75 credit points of electives are required.
60-75

Analytical science specialisation elective subjects

Analytical science specialisation elective subjects
Subject name Subject code Credit points
Decision Support Systems CSE5DSS 15
Advanced Databases CSE5ADB 15
Bioinformatics Technologies CSE5BIO 15
Data Warehouse Concepts and Design CSE5DWD 15
Algorithms and Data Structures (PG) CSE5ALG 15
Computational Intelligence for Data Analytics CSE5CI 15
Web Development on the Cloud CSE5WDC 15
Bioinformatics BIO5INF 15
Epidemiology and Research Methods DTN4EPI 15
Epidemiology and Demography PHE5EPI 15
Methods in GIS ARC5GIM 15
Health Data Analysis A HIM4AHA 15
Predictive Analysis BUS5PA 15
Visual Analysis BUS5VA 15
Customer Analytics and Social Media BUS5CA 15
Advanced Time-Series Econometrics BUS5ATE 15

Please note: Some of these subjects may not be offered in the current year. For a full description of subjects, including the subject name, subject code, credit points, campus/location, teaching period and availability, subject coordinator, class requirements, assessment, prerequisites and readings, please click on the appropriate subject code.

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