MSc Data Science (Earth and Environmental Analytics)

Year of entry: 2025

Course unit details:
Understanding Data and their Environment

Course unit fact file
Unit code DATA71011
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

This module is a combination of technical and non-technical topics all related to critical  

externalities to the data analytics process. The course covers a suite of topics rated to the representation and processing of data:  metadata, paradata and data provenance. Issues about data quality and the impact on inference; accessing and finding data. Pre-Processing: Understanding data quality and divergence and the impact on inference; Cleaning data; Editing and imputation models; Combining and enhancing data: Basics of data linkage/integration; Data Visualisation.

 

Aims

The unit aims to:  

• Develop an awareness of the issues around the use of data in research.  

• Develop fundamental skills in data pre-processing.  

Learning outcomes

Students should be able to:  

• Demonstrate a basic understanding of metadata, paradata and data provenance  

• Be able to prepare a dataset for analysis  

• Make informed decisions about linkage/integration of data and carry out a basic data linkage.  

• Be able to produce basic data visualisations. 

Teaching and learning methods

Lectures will introduce specific ideas in relation to data management, the ethics and disclosure of data and linkage in relation to research. Interactive exercises will involve a mixture of solo and group work. Laptop based practicals will allow the students to apply those ideas and to manage data and be able to make informed decisions about linkage/integration of data and to apply anonymisation processes to data. 

Assessment methods

Group provenance exercise (600 words and code) 20%

Online test on information about data and reproducibility (1 hour) 20%

Group presentation of analysis plan (video) (5 minutes) 10%

Pre-processing and analysis report (1,500 words) 50% 

Recommended reading

Christen, P. (2012). Data matching: concepts and techniques for record linkage,  
entity resolution, and duplicate detection. Springer Science & Business Media  
García S., Luengo, J., & Herrera F. (2015). Data preprocessing in data mining.  
Springer 
Moreau, L., & Groth, P. (2013) Provenance: An Introduction to PROV. Available at https://tinyurl.com/PROV-BOOK [accessed 25/9/2019]  

 

Teaching staff

Staff member Role
Mark Elliot Unit coordinator

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