# Master of Data Science
# First Semester - 15 Points
COMPSCI 762-15 Points
# Advanced Machine Learning
Machine learning is a branch of artificial intelligence concerned with making accurate, interpretable, computationally efficient, and robust inferences from data to solve a given problem. Students should understand the foundations of machine learning, and introduce practical skills to solve different problems. Students will explore research frontiers in machine learning. Recommended preparation: COMPSCI 220, 225 and STATS 101
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 361
# Second Semester - 45 Points
COMPSCI 751-15 Points
# Advanced Topics in Database Systems
Database principles. Relational model, relational algebra, relational calculus, SQL, SQL and programming languages, entity-relationship model, normalisation, query processing and query optimisation, ACID transactions, transaction isolation levels, database recovery, database security, databases and XML. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
STATS 762-15 Points
# Regression for Data Science
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
STATS 765-15 Points
# Statistical Learning for Data Science
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
60 Points closed
# Third Semester - 45 points
COMPSCI 752-15 Points
# Big Data Management
Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-view), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.
Prerequisite: Approval of the Academic Head or nominee
COMPSCI 760-15 Points
# Datamining and Machine Learning
An overview of the learning problem and the view of learning by search. Techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
STATS 763-15 Points
# Advanced Regression Methodology
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Prerequisite: STATS 210 and 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
# Fourth Semester - 45 points
STATS 769 - 15 Points
# Advanced Data Science Practice
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
STATS 78715 Points
# Data Visualisation
Effective visual presentations of data. Topics may include: how to present different types of data; human perception; graphics formats; statistical graphics in R; interactive graphics; visualising high-dimensional data; visualising large data.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
or
STATS 78315 Points
# Simulation and Monte Carlo Methods
A practical introduction to modern simulation and Monte Carlo techniques and their use to simulate real situations and to solve difficult statistical inferential problems whose mathematical analysis is intractable.
COMPSCI 734-15 Points
# Web, Mobile and Enterprise Computing
Examines advanced and emerging software architectures at the confluence of XML, web services, distributed systems, and databases. Includes advanced topics in areas such as: mobile computing, remoting, web services for enterprise integration, workflow orchestrations for the enterprise, peer-to-peer computing, grid computing. Recommended preparation: COMPSCI 335.
Prerequisite: Approval of the Academic Head or nominee
150 Points closed
# Fifth Semester - 45 points
COMPSCI 761-15 Points
# Advanced Topics in Artificial Intelligence
The cornerstones of AI: representation, utilisation, and acquisition of knowledge. Taking a real world problem and representing it in a computer so that the computer can do inference. Utilising this knowledge and acquiring new knowledge is done by search which is the main technique behind planning and machine learning. Research frontiers in artificial intelligence. Recommended preparation: COMPSCI 220, 225.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 367
INFOSYS 722-15 Points
# Data Mining and Big Data
Data mining and big data involves storing, processing, analysing and making sense of huge volumes of data extracted in many formats and from many sources. Using information systems frameworks and knowledge discovery concepts, this project-based course uses cutting-edge business intelligence tools for data analytics.
ENGSCI 763-15 Points
# Advanced Simulation and Stochastic Optimisation
Advanced simulation topics with an emphasis on optimisation under uncertainty. Uniform and non-uniform random variate generation, input distribution selection, output analysis, variance reduction. Simulation-based optimisation and stochastic programming. Two-stage and multi-stage programs with recourse. Modelling risk. Decomposition algorithms. Scenario construction and solution validation.
Prerequisite: ENGSCI 391 or 765
or
STATS 779-15 Points
# Professional Skills for Statisticians - Level 9
Statistical software, data management, data integrity, data transfer, file processing, symbolic manipulation, document design and presentation, oral presentation, professional ethics.
# Final Semester - 45 points
STATS 792-45 Points
STATS 792A-22.5 Points
STATS 792B-22.5 Points
# Dissertation in Statistics Education - Level 9
To complete this course students must enrol in STATS 792 A and B
STATS 792 Enrolment Req