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.
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.