Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

7502BDSA-SEP-PAR

Aims

The course covers the concept, the underlying assumptions, and the applications of generalized linear model (GLM) and growth curves. It considers detecting and handling influential observations. The course provides students with a comprehensive knowledge about matrix representation of a model, model fit, model validation, interpretation and prediction for future observations. The course covers the experimental design and analysis, the analysis of variance (ANOVA), logistic regression, and the analysis of multidimensional contingency tables. The students will have hands-on practice using R/SPSS (or alternative recent software) as analytic tools to analyse real-world data by implementing the course topics.

Learning Outcomes

1.
Model relationships between variables using regression analysis, classification and non-parametric methods.
2.
Implement different resampling methods for model assessment, model selection, and uncertainty measurement.
3.
Control prediction variance using subset selection, shrinkage, and dimension reduction methods.
4.
Implement tree-based methods for decision-making problems.
5.
Analyse real data using advanced statistical programming software.

Module Content

Outline Syllabus:Statistical Learning Linear Regression Classification. Resampling Methods Linear Model Selection and Regularization Moving Beyond Linearity Tree-Based Methods
Additional Information:The module contributes to the master’s aim to equip the student with the required abilities and skills to perform data science on real-world applications.

Assessments

Report

Exam

Portfolio