Master of Data Science

For more information on units of study visit CUSP https://cusp.sydney.edu.au

Data Science

Master of Data Science

Students complete 48 credit points, comprising:
(a) 24 credit points of Core units of study: COMP5310, STAT5003, COMP5318, COMP5048
(b) 12 credit points of Project units
(c) a maximum of 12 credit points of non Data Science Elective units of study
-- Where a waiver is granted for a COMP core unit of study another COMP unit must be taken and where the waiver is granted for STAT5003 another STAT unit of study must be taken.

Graduate Certificate in Data Science:

Students complete 24 credit points, comprising of the following:
Core units of study: COMP5310, STAT5002, COMP9007, COMP9120
-- Where a waiver is granted for a COMP core unit of study, another COMP unit must be taken, and where the waiver is granted for STAT5002, another STAT unit of study must be taken.

Units of study

Master of Data Science

Core
COMP5048 Visual Analytics

Credit points: 6 Teacher/Coordinator: Prof Seok Hong Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: It is assumed that students will have experience with data structure and algorithms as covered in COMP9103 OR COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 (or equivalent UoS from different institutions). Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolmentin the following sessions:Semester 1
Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.
COMP5310 Principles of Data Science

Credit points: 6 Teacher/Coordinator: Ali Anaissi Session: Semester 1,Semester 2 Classes: Lectures, Laboratory Prohibitions: INFO3406 Assumed knowledge: It is assumed that students will have good understanding of relational data model and database technologies as covered in ISYS2120 or COMP9120 (or equivalent UoS from different institutions). Assessment: Through semester assessment (50%) Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
The focus of this unit is on understanding and applying relevant concepts, techniques, algorithms, and tools for the analysis, management and visualisation of data- with the goal of enabling discovery of information and knowledge to guide effective decision making and to gain new insights from large data sets.
To this end, this unit of study provides a broad introduction to data management, analysis, modelling and visualisation using the Python programming language. Development of custom software using the powerful, general-purpose Python scripting language; Data collection, cleaning, pre-processing, and storage using various databases; Exploratory data analysis to understand and profile complex data sets; Mining unlabelled data to identify relationships, patterns, and trends; Machine learning from labelled data to predict into the future; Communicate findings to varied audiences, including effective data visualisations.
Core data science content will be taught in normal lecture + tutorial delivery mode. Python programming will be taught through an online learning platform in addition to the weekly face-to-face lecture/tutorials. The unit of study will include hands-on exercises covering the range of data science skills above.
COMP5318 Machine Learning and Data Mining

Credit points: 6 Teacher/Coordinator: Nguyen Tran Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: INFO2110 OR ISYS2110 OR COMP9120 OR COMP5138 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.
STAT5003 Computational Statistical Methods

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: STAT5002 Assessment: Assignments (40%), quizzes (20%); 2-hour final examination (40%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolment
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.
Textbooks
(1) An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, (2014), Springer;
The prerequisite for STAT5003 is waived for Master of Data Science students. Please apply for special permission for this unit of study.

Project

The Project can be completed either as the two 6 credit point units, DATA5707 and DATA5708, over two semesters, or as the 12 credit point unit, DATA5703, in one semester.
DATA5703 Data Science Capstone Project

Credit points: 12 Teacher/Coordinator: Hamzah Osop Session: Semester 1,Semester 2 Classes: Project Work, Meeting Prerequisites: A candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit. Prohibitions: DATA5707 or DATA5708 or DATA5709 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
The Data Science Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in the data science domain (MDS). The results will be presented in a final project presentation and report. It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of data science skills in research or design.
DATA5707 Data Science Capstone A

Credit points: 6 Teacher/Coordinator: Hamzah Osop Session: Semester 1,Semester 2 Classes: Research/Project Work, Meeting Prerequisites: A part-time enrolled candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit. Prohibitions: DATA5703. Eligible students of the Data Science Capstone Project may choose either DATA5703 or DATA5707/DATA5708. Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
The Data Science Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in the data science domain. The results will be presented in a final project presentation and report. It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of data science skills in research or design. Eligible students for the Data Science Capstone project will be required to complete both DATA5707 (6 CPS) and DATA5708 (6 CPS), totalling 12 CPS.
DATA5708 Data Science Capstone B

Credit points: 6 Teacher/Coordinator: Hamzah Osop Session: Semester 1,Semester 2 Classes: Research/Project Work, Meeting Prerequisites: A part-time enrolled candidate for the MDS who has completed 24 credit points from Core or Elective units of study may take this unit. Corequisites: DATA5707 Prohibitions: DATA5703. Eligible students of the Data Science Capstone Project may choose either DATA5703 or DATA5707/DATA5708. Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
The Data Science Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in the data science domain. The results will be presented in a final project presentation and report. It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of IT skills in research or design. Eligible students for the Data Science Capstone project will be required to complete both DATA5707 (6 CPS) and DATA5708 (6 CPS), totalling 12 CPS.
DATA5709 Data Science Capstone Project - Individual

Credit points: 12 Teacher/Coordinator: Hamzah Osop Session: Semester 1,Semester 2 Classes: Meetings Prerequisites: A candidate for the MDS who has completed 24 credit points from Core or Elective units of study, and has a WAM of 75+ may take this unit. Prohibitions: DATA5703 or DATA5707 or DATA5708 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: Students are required to source for a project and an academic supervisor prior to enrolment.
The Data Science Capstone project unit provides an opportunity for high-achieving students (WAM of 75+) to carry out an individual defined piece of work with academics of our school. The students will acquire skills including the capacity to define a project, show how it relates to existing work, and carry out the project in a systematic manner. Students will apply their gained knowledge of units of study in the data science domain (MDS). The results will be presented in a final project presentation and report. The unit aims to provide students with the opportunity to carry out an advanced project work in a setting and manner that fosters the development of data science skills in research or design.

Electives

Complete a maximum of 12 credit points from the following:
COMP5046 Natural Language Processing

Credit points: 6 Teacher/Coordinator: Soyeon Han Session: Semester 1 Classes: Lectures, Laboratory Assumed knowledge: Knowledge of an OO programming language Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages (such as English or Chinese). It will review the core statistics and information theory, and the basic linguistics, required to understand statistical natural language processing (NLP). Statistical NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state of the art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction, text categorisation, phrase structure parsing and dependency parsing. You will implement many of these sub-tasks in labs and assignments. The unit will also investigate the annotation process that is central to creating training data for statistical NLP systems. You will annotate data as part of completing a real-world NLP task.
COMP5328 Advanced Machine Learning

Credit points: 6 Teacher/Coordinator: Tongliang Liu Session: Semester 2 Classes: Lectures, tutorials Corequisites: COMP5318 OR COMP3308 OR COMP3608 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Machine learning models explain and generalise data. This course introduces some fundamental machine learning concepts, learning problems and algorithms to provide understanding and simple answers to many questions arising from data explanation and generalisation. For example, why do different machine learning models work? How to further improve them? How to adapt them to different purposes?
The fundamental concepts, learning problems and algorithms are carefully selected. Many of them are closely related to practical questions of the day, such as transfer learning, learning with label noise and multi-view learning.
COMP5329 Deep Learning

Credit points: 6 Teacher/Coordinator: Chang Xu Session: Semester 1 Classes: Tutorials, Lectures Assumed knowledge: COMP5318 Assessment: through semester assessment (50%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.
COMP5338 Advanced Data Models

Credit points: 6 Teacher/Coordinator: Dr Ying Zhou Session: Semester 2 Classes: Tutorials, Lectures Assumed knowledge: This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1). Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This unit of study gives a comprehensive overview of post-relational data models and of latest developments in data storage technology.
Particular emphasis is put on spatial, temporal, and NoSQL data storage. This unit extensively covers the advanced features of SQL:2003, as well as a few dominant NoSQL storage technologies. Besides in lectures, the advanced topics will be also studied with prescribed readings of database research publications.
COMP5349 Cloud Computing

Credit points: 6 Teacher/Coordinator: Dr Ying Zhou Session: Semester 1 Classes: Lectures, Practical Labs, Project Work Assumed knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'.
Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud.
In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The unit will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.
COMP5425 Multimedia Retrieval

Credit points: 6 Teacher/Coordinator: Dr Xiu Wang Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: It is assumed that students will have experience with programming skills, as learned in COMP9103 OR COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 (or equivalent UoS from different institutions). Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) evening
The explosive growth of multimedia data, including text, audio, images and video has imposed unprecedented challenges for search engines to meet various information needs of users. This unit provides students with the necessary and updated knowledge of this field in the context of big data, from the information retrieval basics of a search engine, to many advanced techniques towards next generation search engines, such as content based image and video retrieval, large scale visual information retrieval, and social media.
INFO5060 Data Analytics and Business Intelligence

Credit points: 6 Teacher/Coordinator: Dr Simon Poon Session: Intensive January,Intensive July Classes: Lectures, Tutorials, Laboratories, Presentation, Project Work - own time Assumed knowledge: It is assumed that students will have the basic knowledge of information systems, which are covered in COMP5206 or ISYS2160 (or equivalent UoS from different institutions). Assessment: Through semester assessment (65%) and Final Exam (35%) Mode of delivery: Block mode
Note: Department permission required for enrolment
The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.
INFO5301 Information Security Management

Credit points: 6 Teacher/Coordinator: Dinesh Thilakarathna Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: This unit of study assumes foundational knowledge of Information systems management. Two year IT industry exposure and a breadth of IT experience will be preferable. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This unit of study gives a broad view of the management aspects of information security. We emphasise corporate governance for information security, organisational structures within which information security is managed, risk assessment, and control structures. Planning for security, and regulatory issues, are also addressed.
QBUS6810 Statistical Learning and Data Mining

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 2hr lecture and 1x 1hr tutorial per week Prerequisites: BUSS6002 Assessment: group project (30%); online quizzes (20%); final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.
QBUS6840 Predictive Analytics

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1 x 2hr lecture and 1 x 1hr tutorial Prerequisites: (QBUS5001 or ECMT5001) and BUSS6002 Assessment: group assignment (30%); homework assignment (15%); mid-semester test (20%); final exam (35%) Mode of delivery: Normal (lecture/lab/tutorial) day
To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.
The prerequisites for QBUS6810 and QBUS6840 are waived for Master of Data Science students. Please apply for special permission for these units.

Non-Data Science Electives

Complete a maximum of 12 credit points from the following:.
CSYS5010 Introduction to Complex Systems

Credit points: 6 Teacher/Coordinator: Dr Michael Harre Session: Semester 1,Semester 2 Classes: Lectures, Laboratories Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Globalisation, rapid technological advances, the development of integrated and distributed systems, cross-disciplinary technical collaboration, and the emergence of "evolved" (as opposed to designed) systems are some of the reasons why many systems have begun to be described as complex systems in recent times. Complex technological, biological, socio-economic and socio-ecological systems (power grids, communication and transport systems, food webs, megaprojects, and interdependent civil infrastructure) are composed of large numbers of diverse interacting parts and exhibit self-organisation and/or emergent behaviour. This unit will introduce the basic concepts of "complex systems theory", and focus on methods for the quantitative analysis and modelling of collective emergent phenomena, using diverse computational approaches such as agent-based modelling and simulation, cellular automata, bio-inspired algorithms, and game theory. Students will gain theoretical knowledge of complex adaptive systems, coupled with practical skills in computational simulation and forecasting using a range of modern toolkits.
DATA5207 Data Analysis in the Social Sciences

Credit points: 6 Teacher/Coordinator: Shaun Ratcliff; Shaun Ratcliff Session: Intensive December,Semester 1 Classes: lectures, laboratories Assumed knowledge: COMP5310 Assessment: through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolmentin the following sessions:Intensive December
Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.
EDPC5012 Evaluating Learning Tech. Innovation

Credit points: 6 Session: Semester 1 Classes: 1x2hr seminar/week evenings Assessment: 2x1500wd short assignment (2x25%) and 1x3000wd final paper (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This unit is intended to help students acquire the knowledge and skills needed to evaluate ICT-enhanced learning innovations. It provides an introduction to the theory and practice of evaluations, drawing principles and methods from best practice in program evaluation and the areas of ICT-enhanced learning. Attention is paid to a holistic approach to evaluation, stressing the need to plan, design and implement evaluation in context. It is suitable for those with an interest in formal education, corporate training and professional development.
EDPC5025 Learning Technology Research Frontiers

Credit points: 6 Session: Semester 2 Classes: 1x2hr seminar/week - evening Assessment: 1x3000wd weekly contributions to debates and learning technology forecasts (50%) and 1x3000wd final paper (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This unit is designed for students interested in the newest research developments in the area of learning technology, and those who want to gain a deeper understanding of research methods and techniques, appropriate to the fields of the learning sciences and technologies. It is ideal for those students who want to explore the newest topics of their interest and simultaneously learn about research design in a collaborative peer-supported learning environment. Students will learn to assess critically emergingdomains of learning technology innovation, understand different kinds of research methods and choose appropriate research methods for carrying out empirical studies. Students will participate in debates, research projects. The unit is student-led and involves proactive individual and collaborative exploration of topics.
ITLS6107 Applied GIS and Spatial Data Analytics

Credit points: 6 Session: Semester 2 Classes: 7 x 2 hr lectures, 7 x 4 hr computer labs Prohibitions: TPTM6180 Assessment: individual projects (40%); group project (20%); group presentation (10%); final exam (30%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: This unit assumes no prior knowledge of GIS; the unit is hands-on involving the use of software, which students will be trained in using.
The world is increasingly filled with systems, devices and sensors collecting large amounts of data on a continual basis. Most of these data are associated with locations that represent everything from the movement of individuals travelling between activities to the flow of goods or transactions along a supply chain and from the location of companies to those of their current and future customers. Taking this spatial context into account transforms analyses, problem-solving and provides a powerful method of visualising the world. This is the essence of Geographic Information Systems (GIS) and this unit. This unit starts by introducing students to the 'building blocks' of GIS systems, including data structures, relational databases, spatial queries and analysis. The focus then moves on to sources of spatial data including Global Positioning System (GPS), operational systems such as smartcard ticketing and transaction data along with web-based sources highlighting both the potential and challenges associated with integrating each data source within a GIS environment. The unit is hands-on involving learning how to use the latest GIS software to analyse several problems of interest using real 'big data' sources and to communicate the results in a powerful and effective way. These include identifying potential demand for new services or infrastructure, creating a delivery and scheduling plan for a delivery firm or examining the behaviour of travellers or consumers over time and locations. This unit is aimed at students interested in the spatial impact of decision-making and on the potential for using large spatial datasets for in-depth multi-faceted analytics.
PHYS5033 Environmental Footprints and IO Analysis

Credit points: 6 Teacher/Coordinator: Dr Arunima Malik and Prof Manfred Lenzen Session: Semester 1,Semester 2 Classes: 2-hour lecture interspersed with hands-on exercises per week Assessment: Comprehensive diary/notes from lectures, including a quantitative example, and in class tests (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Minimum class size of 5 students.
This unit of study will provide students with practical skills for carrying out environmental footprinting calculations: for individuals, companies, organisations or nations. In particular, this unit will provide a comprehensive introduction to input-output analysis for identifying impacts embodied in regional, national and global supply chains. This unit focuses on contemporary environmental applications such as emissions, energy-use, water, land, loss of animal & plant species; and also social applications such as employment, poverty and child labour. The unit first explores national and global economic and environmental accounting systems and their relationships to organisational accounting. Second, it presents cutting-edge techniques enabling the global analysis of environmental and social impacts of international trade. Third, it offers hands-on practical activities for mastering the input-output techniques conceived by Nobel Prize Laureate Wassily Leontief, and provides a step-by-step recipe for undertaking boundary-free environmental and social footprinting for sectors and organisations. Students will walk away from this unit equipped with useful skills needed to calculate footprints, and prepare sustainability reports for any organisation, city, region, or nation, using organisational data, economic input-output tables and environmental accounts. Students will also benefit from enrolling in PHYS5034 for a sound understanding of the role of input-output analysis within the field of Life-Cycle Assessment.

Graduate Certificate in Data Science

Core
COMP5310 Principles of Data Science

Credit points: 6 Teacher/Coordinator: Ali Anaissi Session: Semester 1,Semester 2 Classes: Lectures, Laboratory Prohibitions: INFO3406 Assumed knowledge: It is assumed that students will have good understanding of relational data model and database technologies as covered in ISYS2120 or COMP9120 (or equivalent UoS from different institutions). Assessment: Through semester assessment (50%) Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
The focus of this unit is on understanding and applying relevant concepts, techniques, algorithms, and tools for the analysis, management and visualisation of data- with the goal of enabling discovery of information and knowledge to guide effective decision making and to gain new insights from large data sets.
To this end, this unit of study provides a broad introduction to data management, analysis, modelling and visualisation using the Python programming language. Development of custom software using the powerful, general-purpose Python scripting language; Data collection, cleaning, pre-processing, and storage using various databases; Exploratory data analysis to understand and profile complex data sets; Mining unlabelled data to identify relationships, patterns, and trends; Machine learning from labelled data to predict into the future; Communicate findings to varied audiences, including effective data visualisations.
Core data science content will be taught in normal lecture + tutorial delivery mode. Python programming will be taught through an online learning platform in addition to the weekly face-to-face lecture/tutorials. The unit of study will include hands-on exercises covering the range of data science skills above.
COMP9007 Algorithms

Credit points: 6 Teacher/Coordinator: Andreas Van Renssen; Mohammad Polash Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: COMP5211 Assumed knowledge: This unit of study assumes that students have general knowledge of mathematics (especially Discrete Math) and problem solving. Having moderate knowledge about Data structures can also help students to better understand the concepts of Algorithms taught in this course. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) evening
The study of algorithms is a fundamental aspect of computing. This unit of study covers data structures, algorithms, and gives an overview of the main ways of computational thinking from simple list manipulation and data format conversion, up to shortest paths and cycle detection in graphs. Students will gain essential knowledge in computer science, including basic concepts in data structures, algorithms, and intractability, using paradigms such as dynamic programming, divide and conquer, greed, local search, and randomisation, as well NP-hardness.
COMP9120 Database Management Systems

Credit points: 6 Teacher/Coordinator: Ali Anaissi; Mohammad Polash Session: Semester 1,Semester 2 Classes: Lectures, Tutorials, Project work Prohibitions: INFO2120 OR INFO2820 OR INFO2005 OR INFO2905 OR COMP5138 OR ISYS2120. Students who have previously studied an introductory database subject as part of their undergraduate degree should not enrol in this foundational unit, as it covers the same foundational content. Assumed knowledge: Some exposure to programming and some familiarity with data model concepts Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This unit of study provides a conceptual and practical introduction to the use of common platforms that manage large relational databases. Students will understand the foundations of database management and enhance their theoretical and practical knowledge of the widespread relational database systems, as these are used for both operational (OLTP) and decision-support (OLAP) purposes. The unit covers the main aspects of SQL, the industry-standard database query language. Students will further develop the ability to create robust relational database designs by studying conceptual modelling, relational design and normalization theory. This unit also covers aspects of relational database management systems which are important for database administration. Topics covered include storage structures, indexing and its impact on query plans, transaction management and data warehousing.
In this unit students will develop the ability to: Understand the foundations of database management; Strengthen their theoretical knowledge of database systems in general and relational data model and systems in particular; Create robust relational database designs; Understand the theory and applications of relational query processing and optimisation; Study the critical issues in data and database administration; Explore the key emerging topics in database management.
STAT5002 Introduction to Statistics

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Assumed knowledge: HSC Mathematics Assessment: 2 hour examination (60%), assignments (20%), quizzes (20%) Mode of delivery: Normal (lecture/lab/tutorial) evening
The aim of the unit is to introduce students to basic statistical concepts and methods for further studies. Particular attention will be paid to the development of methodologies related to statistical data analysis and Data Mining. A number of useful statistical models will be discussed and computer oriented estimation procedures will be developed. Smoothing and nonparametric concepts for the analysis of large data sets will also be discussed. Students will be exposed to the R computing language to handle all relevant computational aspects in the course.
Textbooks
All of Statistics, Larry Wasserman, Springer (2004)