Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

5123COMP-SEP-CTY

Aims

To develop a theoretical knowledge of statistical skills to solve data science problems To develop and display solutions to data science problems by applying statistical theory using appropriate software applications

Learning Outcomes

1.
Applying appropriate statistical theory data science problem to derive meaningful solutions.
2.
Apply appropriate statistical theory and derive meaningful solutions in a suitable programming language

Module Content

Outline Syllabus:Review summary statistics Assumption testing for statistical test Normality Multivariate normality Homoscedasticity etc Correlation and Covariance Non-parametric test – Chi Square T-Tests One sample T-test Two sample T-test Paired Two-sample T-Test ANOVA Linear Models Simple Linear Regression Multiple Regression Discussion of Generalized Linear Models Logistic Regression Poisson Regression Model Diagnostics Residuals – ANOVA – Akaike Information Criteria (AIC) Cross-Validation Bootstrap Nonlinear Models Nonlinear Least Squares Generalized Additive Models Decision trees Random Forests (Ensemble)
Module Overview:
To develop a theoretical knowledge of statistical skills to solve data science problems To develop and display solutions to data science problems by applying statistical theory using appropriate software applications Although an apparently heavy theoretical treatment of the area, this is intended to be a practical, hands-on exploration of the area.
Additional Information:Although an apparently heavy theoretical treatment of the area, this is intended to be a practical, hands-on exploration of the area.

Assessments

Centralised Exam

Report