Day 2 – Foundations of Data Science I (PM)

14.00-15.00 Statistical Inference and Modelling

Modern statistics exists at the interface between theory, computation and applications and this session will visit a little of each. The aim of the session is to offer an introduction to statistical inference and modelling. The material will be targeted at a numerate audience who have taken an introductory statistics and probability course. Learning outcomes include an understanding of maximum likelihood estimation and how to construct and estimate a statistical model. The R statistical computing environment will be used.

The R project for statistical computing, freely available for download at www.r-project.org. Code will be provided in advance to participants so that they can follow it on their own machine during the session.

Speaker biography: Dr Keefe Murphy is a lecturer in statistics in Maynooth University. His primary research interests lie in developing methodologies for both supervised and unsupervised classification tasks involving complex, high-dimensional, mixed-type data, often in the presence of outliers. Publications to date include work on Bayesian nonparametric mixtures of factor analysers, covariate-dependent parsimonious model-based clustering, risk-stratification for multi-omic prostate cancer data and novel distance-based clustering approaches for longitudinal categorical sequences. Dr Murphy has a keen interest in the computational implementation of statistical models and has authored a number of R packages (e.g., IMIFA, MoEClust, MEDseq). Dr Murphy was awarded the 2020 Classification Society Distinguished Dissertation Award, an award bestowed for an outstanding Ph.D. dissertation on the theme of clustering, classification, or related areas of data analysis.

15.00-17.00 Mathematical Modelling: An overview

The use of mathematics has many historical successes, particularly in the realm of physics and engineering, where mathematical concepts are regularly employed to address challenges far beyond the context in which they were originally developed. More recently, mathematics has been employed to further our understanding of biological systems. This lecture introduces the mathematical tools needed for mathematically modelling complex sytems, such as the brain. We will review concepts from linear algebra, vector calculus and differential equations. We will learn how to describe neural systems using differential equations, how to analyse these equations and how to simulate the system computationally.

Python will be required, ideally Jupyter Notebook

Speaker biography:

Áine Byrne is an Assistant Professor in the School of Mathematics and Statistics, at University College Dublin. She obtained her PhD from the University of Nottingham in 2017, before receiving a Swartz Fellowship for her postdoctoral studies at the Center for Neural Sciences, New York University. Her research involves using mathematics to understand how the brain works. Broadly, she is interested in synchronisation. How do individual neurons (brain cells) synchronise to create coherent network oscillations? How do brain area synchronise their activity to communicate and exchange information? How do neural oscillations synchronise to an external stimulus? In her work, she utilises dynamical systems theory and statistical physics to begin to answer these types of questions

Participants are welcome to join in person (S206 Schumann Building, University of Limerick) or remotely. A Zoom link will circulate to remote participants on Wednesday 24th of August.

There is a 0.5 deduction from the training unit allocation per Enterprise Alliance member joining this session.