This module surveys the fundamental tools used in the theory and practice of data science. The module content will give the student a solid foundation in the fundamentals of statistics; statistical computing; data mining techniques and machine learning techniques used in data science.
Available courses
1.0 MODULE DESCRIPTION
In this module students will be introduced to the collection of data related to male and female social, economic and environmental issues and useful methods for their statistical analysis.
2.0 MODULE OBJECTIVES/LEARNING OUTCOMES
Upon completion of this module, students should:
1 Construct data frames for the collection of data related to both sexes together or
individually.
2 Classify and summarise data in relation to various social, economic and environmental
variables
3 Analyse variables and their relationship to the sexes using inferential methods.
1.0 MODULE DESCRIPTION
In this module students will be introduced to methods of sampling and statistical inference useful in environmental science and resource management.
The course addresses the application of statistical techniques to solve business problems and make recommendations for the optimal use of scarce resources.Use will be made of statistical packages where appropriate. The topics include probability distributions, sampling theory, estimation, hypothesis testing and correlation and regression. Tutorial questions will focus on business applications.