EPM5005 · Data management and statistical computing

Overview

This unit will describe and demonstrate the complexity of data management and statistical computing methods. It will enable you to communicate effectively about the issues in storing and retrieving information, and in assessing the quality and limitations of data repositories. It uses examples from real data sets to give you practical skills in data management, assessment of data quality and handling and linking of large volumes of data.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Understanding of different sources and methods of data storage such as unit records, matrix files, longitudinal data, relational databases.
2. Understanding of relational database concepts and data retrieval methods.
3. Proficiency in the handling and analysis of large data sets.
4. Skills in data manipulation and management using the major statistical software packages.
5. Skills in linking files through unique and non-unique identifiers.
6. Understanding of data quality control and data entry methods and confidentiality issues, and experience in applying validation checks to data.
7. Skills in data cleaning, identification of outliers and data trimming using appropriate statistical methods.
8. Understanding of processes leading to finalisation of data sets prior to analysis.
9. Ability to communicate with researchers in data-related issues of design, conduct and analysis of studies.

Workload

No workload detail published.

Student experience, not official rules. Nothing here changes what the Handbook says.