Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Module Offerings
5224COMP-JAN-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.
Apply appropriate statistical theory to data science problems to derive meaningful solutions.
2.
Apply appropriate data analysis techniques in a suitable software application.
Module Content
Outline Syllabus:Purpose of statistics
Assumption testing e.g. Normality Multivariate normality, Homoscedasticity etc.
Correlations
Cluster analysis
Non-parametric tests – Chi Square, Two-way Chi Square
ANOVA and T-tests
Linear Modelling - Simple Linear Regression, Multiple Linear Regression, Logistic Regression, Poisson Regression
Decision trees, Random Forests
Nonlinear Models, Generalized Linear Models
Akaike Information Criteria (AIC)
Module Overview:
This module allows you to explore statistical techniques through practical, hands-on data analysis. You will develop a theoretical knowledge of statistical skills to solve data science problems and display solutions to data science problems by applying statistical theory using appropriate software applications.
This module allows you to explore statistical techniques through practical, hands-on data analysis. You will develop a theoretical knowledge of statistical skills to solve data science problems and display solutions to data science problems by applying statistical theory using appropriate software applications.
Additional Information:This module explores statistical techniques through practical, hands-on data analysis.