Module Code - Title:
MA5012
-
ADVANCED PREDICTIVE ANALYTICS
Year Last Offered:
2025/6
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
Rationale and Purpose of the Module:
This module will provide students with a grounding in the development and application of statistical models in a number of applied data problems. The module will focus on the application of linear and generalised linear models to real world data, with an emphasis on model fitting and selection. Students will learn how to appropriately interpret and communicate the results of their statistical models. Students will also learn how to implement their models in the statistical programming language R.
Syllabus:
1. Commonly used statistical distributions and their application in practical data analysis.
2. Statistical inference and approaches for estimating parameters of statistical models.
3. Linear models - regression modelling and evaluation. Variable selection.
4. Multivariate predictive modelling.
5. Generalised Linear Models including nonlinear regression.
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
1. Identify the appropriate statistical distribution for modelling certain types of data.
2. Describe and model the relationship between two or more quantitative variables in a number of different data settings.
3. Estimate the parameters of statistical models using appropriate statistical procedures.
4. Construct and interpret fitted models in the context of application to real world problems, drawing conclusions.
5. Implement each technique using the statistical software package R.
Affective (Attitudes and Values)
On successful completion of this module, students will be able to:
1. Demonstrate an ability to construct and fit appropriate statistical models and justify modelling choices.
2. Present modelling results accurately and succinctly, presenting technical information in a non-scientific format for a non-technical audience.
Psychomotor (Physical Skills)
N/A
How the Module will be Taught and what will be the Learning Experiences of the Students:
This module will be block taught online as part of the UL@Work Human Capital Initiative. This module will create graduates who are KNOWLEDGEABLE (students will develop and apply their analytics knowledge to real-world datasets), COLLABORATIVE (students will work in teams to analyse real-world data), CREATIVE (students will develop skills to build appropriate analytics models), ARTICULATE (students will develop the ability to communicate analytics results to key
stakeholders), RESPONSIBLE (students will understand the importance of using appropriate analytics tools and draw suitable conclusions).
Research Findings Incorporated in to the Syllabus (If Relevant):
Prime Texts:
Kleinbaum (2008)
Applied Regression Analysis and other Multivariate Methods
, Duxbury Press
James Gareth, Daniela Witten, Trevor Hastie and Robert Tibshirani. (2018)
An introduction to statistical learning
, Springer
John Fox and Sanford Weisberg. (2018)
An R companion to applied regression
, Sage
Other Relevant Texts:
Programme(s) in which this Module is Offered:
Semester(s) Module is Offered:
Spring
Module Leader:
Philippa.Wilkes@ul.ie