Senior Mathematics and Statistics Handbook
Project Units
The School of Mathematics and Statistics offers majors in four subject areas, namely Data Science, Financial Mathematics and Statistics, Mathematics and Statistics. Correspondingly there are four project units available: DATA3888, FMAT3888, MATH3888 and STAT3888. To qualify for the major students must complete the corresponding project unit (or the Science Interdisciplinary Project Unit, SCPU3001).
DATA3888 Data Science Capstone
Prerequisite: one of DATA2001, DATA2901, DATA2002, DATA2902, STAT2912, STAT2012.
Assessment: a disciplinary component worth 50%, comprising an online quiz (10%) and a Student lecture (10% report, 20% presentations, 10% peer review), and an interdisciplinary component worth 50% (made up of 5% reflective task, 10% team work process, 35% report and product).
In our ever-changing world, we are facing a new data-driven era where the capability to efficiently combine and analyse large data collections is essential for informed decision making in business and government, and for scientific research. Data science is an emerging interdisciplinary field with its focus on high performance computation and quantitative expression of the confidence in conclusions, and the clear communication of those conclusions in different discipline context. This unit is our capstone project that presents the opportunity to create a public data product that can illustrate the concepts and skills you have learnt in this discipline. In this unit, you will have an opportunity to explore deeper disciplinary knowledge; while also meeting and collaborating through project-based learning. The capstone project in this unit will allow you to identify and place the data-driven problem into an analytical framework, solve the problem through computational means, interpret the results and communicate communicating your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, to translate problem between two or more disciplines and this is essential for both professional and research pathways in the future.
FMAT3888 Projects in Financial Mathematics
Prerequisite: either MATH2070 or MATH2970, and either STAT2011 or STAT2911.
Assessment: discipline content assignment (10%), discipline content quiz (20%), discipline project report (10%), discipline project presentation (10%), reflective task (10%), team work process (10%), interdisciplinary project report (20%), interdisciplinary project presentation (10%).
Mathematics and statistics are powerful tools in finance and more generally in the world at large. To really experience the power of mathematics and statistics at work, students need to identify and explore interdisciplinary links. Engagement with other disciplines also provides essential foundational skills for using mathematical and statistical ideas in financial contexts and in the world beyond. In this unit you will commence by working on a group project in an area of financial mathematics or statistics. From this project you will acquire skills of teamwork, research, wring and project management as well as disciplinary knowledge. You will then have the opportunity to apply your disciplinary knowledge in an interdisciplinary team to identify and solve problems and communicate your findings.
MATH3888 Projects in Mathematics
Prerequisite: one of MATH2921, MATH2021, MATH2065, MATH2965, MATH2061, MATH2961, MATH2923 or MATH2023, and one of MATH2922, MATH2022, MATH2061, MATH2961, MATH2088 or MATH2988.
Assessment: discipline content assignment (10%), discipline content quiz (20%), discipline project report (10%), discipline project presentation (10%), reflective task (10%), team work process (10%), interdisciplinary project report (20%), interdisciplinary project presentation (10%).
Mathematics is ubiquitous in the modern world. Mathematical ideas contribute to philosophy, art, music, economics, business, science, history, medicine and engineering. To really see the power and beauty of mathematics at work, students need to identify and explore interdisciplinary links. Engagement with other disciplines also provides essential foundational skills for using mathematics in the world beyond the lecture room. In this unit you will commence by working on a group project in an area of mathematics that interests you. From this you will acquire skills of teamwork, research, writing and project management as well as disciplinary knowledge. You will then have the opportunity to apply your disciplinary knowledge in an interdisciplinary team to indentify and solve problems and communicate your findings to a diverse audience.
STAT3888 Statistical Machine Learning
Prerequisite: Either STAT2011 or STAT2911, and one of DATA2002, DATA2902, STAT2012 or STAT2912.
Assessment: Written exam (40%), major project (50%), computer labs (10%).
Data Science is an emerging and inherently interdisciplinary field. A key set of skills in this area fall under the umbrella of Statistical Machine Learning methods. This unit presents the opportunity to bring together the concepts and skills you have learnt from a Statistics or Data Science major, and apply them to a joint project with NUTM3888 where Statistics and Data Science students will form teams with Nutrition students to solve a real world problem using Statistical Machine Learning methods. The unit will cover a wide breadth of cutting edge supervised and unsupervised learning methods will be covered including principal component analysis, multivariate tests, discrimination analysis, Gaussian graphical models, log-linear models, classification trees, k-nearest neighbors, k-means clustering, hierarchical clustering, and logistic regression. In this unit, you will continue to understand and explore disciplinary knowledge, while also meeting and collaborating through project-based learning; identifying and solving problems, analysing data and communicating your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, and this is essential for both professional and research pathways in the future.