MIT9305 Statistical Methods for Data Science
Unit Code
MIT9305
Level
Postgraduate
Campus
Sydney
Prerequisites
None
Credit Points
10
Subject Description
MIT9305 Statistical Methods for Data Science introduces students to a comprehensive foundation in the mathematical and statistical techniques that are fundamental for to provide students with decision making skills and serving as a basis for advanced studies in data science. Students will explore various mathematical concepts such as algebra, calculus, probability theory and statistical inferences. Students are also offered a brief introduction into fundamental machine learning algorithms and their applications. Students will develop skills in data manipulation, model building, and computational techniques through practical exercises and real-world data projects, preparing them for advanced data science tasks and applications.
Course Level
Level 2
Subject Type
☒ Specialisation (MIT Data Science)
☒ Elective (MIT)
Study Mode: Face to face on site
Study Load: Full-time
Learning Topics
- Data preparation, summarisation and visualisation
- Bayesian learning and Monte Carlo methods
- Regression, classification and regularisation
- Clustering and principal component analysis
- Ensemble learning, optimisation and deep learning
Subject Learning Outcomes
- Apply data analysis solutions that demonstrate proficiency in linear algebra, calculus, and probability theory.
- Evaluate statistical inference procedures, the underlying statistical frameworks identifying the limitations of these.
- Develop and evaluate mathematical and statistical models to analyse and interpret data.
- Present data-driven solutions to complex problems, demonstrating the application of mathematical and statistical principles.
Assessment
AIIHE uses a variety of assessment tools to guide and assess each student’s achievement of the subject learning outcomes. In this subject, students undertake portfolio students will demonstrate their knowledge and skills in the use of mathematical and statistical methods through different problem settings, practical test (in-class) real life problem exercise, and students will solve a real-world case study, apply various supervised learning and/or ensemble learning methods, and present the findings. These tasks assess the knowledge, applied skills, critical analysis, communication and collaborative capabilities relevant to the subject.
Summary of Assessment
- Students must attempt all assessment tasks, and achieve an overall mark of at least 50%, to pass this subject.
- Assessment in this subject is consistent with and informed by the AIIHE Assessment Policy and Procedure.
| Assessment Type | Due Date | Weighting |
|---|---|---|
| Assessment 1: Portfolio | Week 2, 4, 5, 6, 8, 9 & 11 | 35% |
| Assessment 2: Practical Test (In-Class) Real Life Problem Exercise | Week 10 | 30% |
| Assessment 3: Individual Report and Presentation | Week 12, 13 | 35% |
Learning Resources
AIIHE will update the topic coverage of the subject and the available relevant learning resources at the time of subject delivery. In the interim, AIIHE provides the following indicative list of relevant reference material. AIIHE also subscribes to eLearning Resources through the Canvas portal, and where relevant material is available, AIIHE will seek to source reference material from this system for staff and student access.
Prescribed Reading
- Kroese, D.P., Botev, Z., Taimre, T. (2019). Data science and machine learning: mathematical and statistical methods. Chapman and Hall/CRC. Available on May 10, 2024, from https://people.smp.uq.edu.au/DirkKroese/DSML/DSML.pdf.
Texts and References
- James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. (2023). An introduction to statistical learning: With applications in python. Springer Nature.
- Deisenroth, M.P., Faisal, A.A., Ong, C.S. (2020). Mathematics for machine learning. Cambridge University Press.
- Ramachandran, K.M., Tsokos, C.P. (2020). Mathematical statistics with applications in R. Academic Press.
Recommended Reading
- James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J. (2023). Statistical learning. In An Introduction to Statistical Learning: with Applications in Python. pp. 15-67. Cham: Springer International Publishing.
- Suzuki, J., Suzuki, J. (2021). Statistical Learning with Math and Python. Springer Singapore.
- Matloff, N. (2019). Probability and statistics for data science: Math+ R+ data. Chapman and Hall/CRC.