# Master of Professional Studies in Data Science
# COMPSCI 732 - Software Tools and Techniques
An advanced course examining research issues related to tools and techniques for software design and development. Topics include: techniques for data mapping and data integration, software architectures for developing software tools, issues in advanced database systems.
Recommended preparation: COMPSCI 331
Prerequisite: Approval of the Academic Head or nominee
# COMPSCI 760 - 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 769 - 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 762 - 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
# COMPSCI 762 - 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
# COMPSCI 752 - 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 751 - 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 779 - 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.