90483 - Modern Statistics And Big Data Analytics

Course Unit Page

Academic Year 2020/2021

Learning outcomes

By the end of the course, the student gains an understanding of theory and computing of modern statistical methods, with particular emphasis on methods for analysing large amounts of data (big data). More specifically, the student acquires knowledge on the most important methods of statistical learning and prediction and the skills required to solve real-world and decision-making problems.

Course contents

Cluster analysis: k-means, construction of distances, hierarchical clustering, partitioning around medoids, average silhouette width, mixture models, with algorithms, R-coding, theory, applications and in-depth discussion

Dimension reduction: Variable and feature selection in regression, cross-validation, model selection criteria, Lasso, with algorithms, R-coding, theory, applications and in-depth discussion

Robust statistics: Influence function, breakdown point, robust estimation of univariate and multivariate location and scale and regression,  with algorithms, R-coding, theory, applications and in-depth discussion

Readings/Bibliography

Everitt, B. S., Landau, S., Leese, M., Cluster Analysis (fourth edition), E. Arnold 2001

Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning (second edition), Springer 2009.

Hennig, C., Meila, M., Murtagh, F., and Rocci, R., Handbook of Cluster Analysis, Taylor & Francis 2016.

Maronna, R. A.,  Martin, R. D., Yohai, V. J., Salibián-Barrera, M., Robust Statistics: Theory and Methods (with R), 2nd Edition, Wiley 2019.

Teaching methods

Classroom lessons, tutorials, computer workshop

Assessment methods

The assessment will have four components. 5/30 marks are assigned to regular homework activity. 5/30 marks are assigned to a literature question to be done at home. 9/30 marks are assigned to a data analysis project to be done at home. 11/30 marks are assigned to a 2 1/2 hours exam comprising of a theoretical question and another data analysis project.

Teaching tools

Lecture Notes, supporting material provided on the web

Office hours

See the website of Christian Martin Hennig