Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

6126COMP-JAN-CTY

Aims

To consolidate and extend prior learning and experience of data science by exploring predictive analytics through the application of machine learning to data sets. To build experience in the process of an analytical exercise.

Learning Outcomes

1.
Formulate and construct an appropriate descriptive analytical modelling task
2.
Formulate and construct an appropriate predictive analytical modelling task.

Module Content

Outline Syllabus:Overview of Predictive Analytics Supervise vs Unsupervised Learning Parametric vs Non-parametric Models Review CRISP-DM Data Understanding Data preparation Association Rules e.g. Market basket Analysis Descriptive Modelling Principal Component Analysis Clustering Algorithms e.g. K-Means Algorithm Interpreting Descriptive Models Predictive Modelling Decision tress Logistic regression K-nearest neighbours Naïve Bayes Linear Regression Assessing Predictive models Consideration of Ensemble Models
Module Overview:
To consolidate and extend prior learning and experience of data science by exploring predictive analytics through the application of machine learning to data sets. To build experience in the process of an analytical exercise.This is a practical module that generates effective analytical modelling experience, thus developing real hands-on experience of data science applications.
Additional Information:This is a practical module that generates effective analytical modelling experience, thus developing real hands-on experience of data science applications.

Assessments

Report

Practice