Geographic Data Science MSc
- Programme duration: Full-time: 12 months
- Programme start: September 2019
- Entry requirements: You will need a 2:1 Honours degree or better in an appropriate field of study. Individual consideration is given to mature students with significant and relevant experience and with professional qualifications.

Module details
Compulsory modules
Semester One
- ENVS441 Qualitative Research Methodologies (15 credits)
- ENVS450 Social Survey Analysis (15 credits)
- ENVS563 Geographic Data Science (15 credits)*
- ENVS609 Geographic Information Science (15 credits)*
Semester Two
- ENVS416 Theorising Human Geography (15 credits)
- ENVS453 Spatial Analysis (15 credits)
- ENVS456 Web Mapping and Analysis (15 credits)
Summer
- ENVS492 Dissertation (60 credits)
Optional Modules
Choose 1x15 credit module if taking all the compulsory modules. If exempt from ENVS609 or (and) ENVS563, add an additional 1(2)x15 credit module.
Semester 1
- COMP518 Database and Information Systems (15 credits)
- COMP519 Web Programming (15 credits)
- COMP529 Big Data Analysis (15 credits)
Semester 2
- ENVS418 Population and Health Analysis and Projection (15 credits)
- COMP575 Computational Intelligence (15 credits)
- ENVS557 Social and Spatial Inequalities (15 credits)
* If an equivalent to ENVS609 or ENVS563 has been taken as part of a first degree, then they may be replaced by an additional optional module, subject to the Programme Directors approval.
Notes
- No more than 75 credits can be taken in one semester.
- ENVS418 is a pre-requisite for ENVS462
- Part-time students must take ENVS609 (if required) in year 1, and ENVS492 in year 2. Other modules may be taken in either year, provided the total credits per year (excluding ENVS492) does not exceed 75.
Compulsory modules
Qualitative Research Methods (ENVS441)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 0:100 |
Aims | This module aims to introduce students to research methodology, philosophy, strategy and design, and to the ethical and practical considerations associatedwith conducting research. This module aims to provide students with an understandingof various qualitative research methods. This module aims to enable students to develop the skillsnecessary to analyse qualitative data. |
Learning Outcomes | Studentswill gain knowledge of ontological and epistemological positions and theirimplications for research Students will gain an understanding of ethical issues in research Students will develop the skills needed to select appropriate qualitative research methods Students will develop the skills to plan and conduct research using qualitative methods Students will develop the skills to analyse qualitative data |
Social Survey Analysis (ENVS450)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 0:100 |
Aims |
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Learning Outcomes | The ability to produce descriptive statistics and graphical representations of quantitative variables The ability to address survey bias through the use of survey reweightingThe ability to use statistical inference techniques to address the impacts of sampling uncertainty on survey estimatesAn understanding of the impact of survey design and sample size on the precision of survey estimates The ability to conduct data analysis using correlation and multivariate regression (OLS and logistic) Competence in the use of a statistical programming language (R) to undertake basic social survey analysis |
Geographic Data Science (ENVS563)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 0:100 |
Aims |
|
Learning Outcomes | Demonstrate advanced GIS/GDS concepts and be able to use the tools programmaticallyto import, manipulate and analyse data in different formats.Understand the motivation and inner workings of the main methodological approcahes ofGDS, both analytical and visual.Critically evaluate the suitability of a specific technique, what it can offer and how it canhelp answer questions of interest.Apply a number of spatial analysis techniques and how to interpret the results, in theprocess of turning data into information. When faced with a new data-set, work independently using GIS/GDS tools programmatically. |
Geographic Information Science (ENVS609)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 0:100 |
Aims | Understand how digital representations of the real world can be created within a GIS including the referencing of geographic features To gain familiarity with the unique properties of geographic data including spatial autocorrelation and modifiable areal units Appreciate that there are uncertainties in the creation of geographic representations Develop skills in the basic use of GIS to create digital representations and underatnd their constraints within a framework of GIScience |
Learning Outcomes | Gain a sound understanding of the function, concepts and features of a Geographic Information System Understand those constraints and considerations that are required when implementing a GIS to build geographic representations Develop practical skills in the application of a GIS to those data types often associated with a student''s disciplinary area |
Theorising Human Geography (ENVS416)
Level | M |
---|---|
Credit level | 15 |
Semester | Second Semester |
Exam:Coursework weighting | 0:100 |
Aims |
|
Learning Outcomes | Gain a thorough understanding of the major themes that define human geography; Gain a thorough understanding of the epistemological underpinnings of different approaches to researching human geography. |
Spatial Analysis (ENVS453)
Level | M |
---|---|
Credit level | 15 |
Semester | Second Semester |
Exam:Coursework weighting | 0:100 |
Aims | · Build upon the more general researchtraining delivered via companion modules on Data Collection and Data Analysis,both of which have an aspatial focus · Highlight a number of key socialissues that have a spatial dimension · Explain the specific challenges facedwhen attempting to analyse spatial data · Introduce a range of analyticaltechniques and approaches suitable for the analysis of spatial data · Enhancepractical skills inusing software packages to implement a wide range of spatial analytical tools. |
Learning Outcomes | Identify some key sources of spatial data andresources of spatial analysis and modelling tools Explain the advantages of taking spatial structure intoaccount when analysing spatial data Apply a range of computer-based techniques for theanalysis of spatial data, including mapping, correlation, kernel densityestimation, regression, multi-level models, geographically-weighted regression,spatial interaction models and spatial econometricsSelect appropriate analytical tools for analysingspecific spatial data sets to address emerging social issues facing the society |
Web Mapping and Analysis (ENVS456)
Level | M |
---|---|
Credit level | 15 |
Semester | Second Semester |
Exam:Coursework weighting | 0:100 |
Aims | The module has two main aims. First, it seeks to provide hands-on experience and training in the design and generation of web-based mapping and geographical information tools. Second, it seeks to provide hands-on experience and training in the use of software to access, analyse and visualize web-based geographical information. |
Learning Outcomes | (LO1) Experience using tile rendering tools to generate content for map-based web sites. (LO2) Web-based data collection techniques (LO3) Programming skills to enable basic online data manipulation and web mapping (LO4) Knowledge of web based mapping infrastructure (S1) Communication skills (S2) Problem solving skills (S3) IT skills (S4) Organisational skills (S5) Numeracy |
Dissertation - Geographic Data Science (ENVS492)
Level | M |
---|---|
Credit level | 60 |
Semester | Summer (June-September) |
Exam:Coursework weighting | 0:100 |
Aims | To give students the opportunity to design and deliver an academic research project around Geographic Data Science |
Learning Outcomes | A complete study, written to publishable standard. Self managed and planned programme. |
Optional modules
Database and Information Systems (COMP518)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 25:75 |
Aims | To provide a deep, systematic and critical understanding of the nature of information systems and particularly database systems, including web technology for databases.
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Learning Outcomes | At the end of the module students should have a critical understanding of the nature of relational databases. At the end of the modulestudents should be able to develop the ability touse SQL as a data definition and data manipulation language, and to develop acritical understanding of querying a relational database withSQL. At the end of the module students should be able to develop a systematic understanding of transaction management and concurrency control in database systems. |
Web Programming (COMP519)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 0:100 |
Aims |
|
Learning Outcomes | be able to demonstrate an understanding of the range of technologies and programming languages available to organisations and businesses and be able to choose an appropriate architecture for a web application. be able to make informed and critical decisions, design and implement reasonably sophisticated client-side web applications using HTML and JavaScript. be able to make informed and critical decisions, design and implement reasonably sophisticated server-side web applications using one or more suitable technologies. |
Big Data Analysis (COMP529)
Level | M |
---|---|
Credit level | 15 |
Semester | First Semester |
Exam:Coursework weighting | 60:40 |
Aims | To introduce the student to middleware often used in Big Data analytics. To introduce the student to implementing algorithms using such middleware. |
Learning Outcomes | Understanding of algorithmic approaches for handling batch and streaming analysis. Understanding of middleware that can be used to enable algorithms to scale up to analysis of large datasets. Understanding of the impact of the middleware on how algorithms are articulated. |
Population and Health Analysis and Projection (ENVS418)
Level | M |
---|---|
Credit level | 15 |
Semester | Second Semester |
Exam:Coursework weighting | 0:100 |
Aims | To provide an introduction to basic techniques of population and health analysis and projection. |
Learning Outcomes | An understanding of the basic techniques of population and health analysis and projection. The ability to use spreadsheets for data analysis and presentation. Appreciation of the value of population and health analysis and projection for understanding society. |
Computational Intelligence (COMP575)
Level | M |
---|---|
Credit level | 15 |
Semester | Second Semester |
Exam:Coursework weighting | 100:0 |
Aims | Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks. Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised mapping. Understand the benefit of adopting naturally inspired techniques to implement optimisation of complex systems and acquire the fundamental knowledge in various evolutionary techniques. Become familiar with the basic concepts of systems optimisation and its role in natural and biological systems and entities. |
Learning Outcomes | Learning the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms. Appreciation of the advantages of evolutionary-related approaches for optimisation problems and their advantages compared to traditional methodologies. Also, understanding the different techniques of evolutionary optimisation for discrete and continuous configurations Understanding of the needs for genetic encoding and modelling for solving optimisation problems and familiarisation with the evolutionary operators and their performance. Understanding of the neural network learning processes and their most popular types, as well as appreciation of how neural networks can be applied to artificial intelligence problems. |
Social and Spatial Inequalities (ENVS557)
Level | M |
---|---|
Credit level | 15 |
Semester | Second Semester |
Exam:Coursework weighting | 0:100 |
Aims | · Gain anunderstanding of several core areas of social and spatial inequalities and howthese inter-relate, and to engage with academic debates about these issues · Explore evidencefor, and interpretations of, social and spatial inequalities (eg labour market,ethnic, spatial aspects of poverty) · Gain anunderstanding of the geographies of social inequalities, including whyinequalities are not equal between places, and what the implications of thisunevenness are for individuals and communities · Consider howand why social inequalities have persisted and/or changed over time, withreference to allied theories and empirical evidence · Gain acritical understanding of the meaning and measurement of inequalities, povertyand deprivation · Identify andreview the types of data sources that can be used to explore social and spatialinequalities · Explore thewider UK context for the development of social and spatial inequalities,including economic restructuring and welfare reform · Considerrepresentations of inequalities in the media, policy and political debate · Consider anumber of policy developments/responses to problems of social and spatialinequalities, and to highlight their impact |
Learning Outcomes | Develop anunderstanding of social and spatial inequalities, how these inter-relate, andhow the terms have been (mis-)used in academic, political, policy and public discourses Develop anunderstanding of how and why social and spatial inequalities might havepersisted over time, and review the empirical evidence for this Gain insightinto a range of government responses that have been developed to combat socialinequalities and related issues in the UK, at the regional and sub-regionallevel |
If an equivalent to ENVS563 has been taken as part of a first degree, then ENVS563 may be replaced by an additional optional module, subject to the Programme Directors approval.
No more than 75 credits can be taken in one semester.
ENVS418 is a pre-requisite for ENVS462.
Part-time students must take SOCI501 and ENVS563 (if required) in year 1, and ENVS492 in year 2. Other modules may be taken in either year, provided the total credits per year (excluding ENVS492) does not exceed 75.