This course will enable you to develop a high level understanding of quantitative and computational geographical methods. This includes skills in GIS software and statistical programming languages, such as R or Python.
Introduction
Within an applied setting, you will develop skills in the visualisation, modelling and statistical analysis of conventional and novel sources of data, ranging from censuses and social surveys through to satellite imagery and social media using both web-based and traditional techniques.
More about Data Science and Analytics for Society
Human activity is increasingly associated with the generation of large volumes of data. For example, transactional data collated by retailers for marketing and store location purposes, administrative data assembled to help with the efficient running of public services, data shadows created through social media use, and an increased prevalence of smart-card linked transport systems record our travel behaviours.
Many grand human challenges concern problems of a geographical nature; be this how we can mitigate the human impact of climate change; ensure global food and water security; design energy systems that are resilient within the context of future population dynamics; or, how to design future cities where spatial inequities in health and wellbeing might be eradicated. The growing volumes of big data about the form, function and dynamics of human activities are providing new opportunities to advance such debates within a framework of Geographic Data Science.
Who is this course for?
This course is for you if you want to understand and analyse the role of geography in everyday life through geographical and computational reasoning.
What you'll learn
How the modern GIS toolkit can be integrated with Data Science tools to solve practical real-world problems
You’ll gain the practical ability to apply GIS in the handling and analysis of spatial data in a context of planning and social sciences
Research methodology, philosophy, strategy and design, and to the ethical and practical considerations associated with conducting research
How to analyse of social survey data, covering descriptive and inferential statistics, data visualisation, regression modelling and model diagnostics
A range of analytical techniques and approaches suitable for the analysis of spatial data
Some of the major debates of society, environment, space and place and the relationships between them.
Course content
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
Semester one
Our compulsory modules in Geographic Data Science (GDS) will give you a comprehensive introduction to the field where GIS and Data Science intersect. You’ll cover programming with Python, the industry-leading language for GIS packages like Arc/GIS and QGIS.
You’ll also learn about the importance of GDS for social science applications through a combination of lectures, practical classes, and independent study. Another compulsory module delves into using GIS tools to create digital representations of the world, with a focus on avoiding potential problems. Our qualitative research module covers a range of methods and emphasizes the importance of careful research design.
We’ll also introduce you to analysing social survey data through descriptive and inferential statistics, using the R programming language. As for optional modules, we offer a comprehensive overview of key algorithms and approaches for Big Data problems, as well as a focus on database systems and SQL.
Please see below for semester one modules. Select the next tab to view semester two modules.
Compulsory modules
Social Survey Analysis (ENVS450)
Credits: 15 /
Semester: semester 1
An introduction to analysis of social survey data, covering descriptive and inferential statistics, data visualisation, regression modelling and model diagnostics. The module is taught using ‘R’ – a statistical programming language – to which the module will also provide an introduction.
Geographic Data Science (ENVS563)
Credits: 15 /
Semester: semester 1
This module will introduce students to the nascent field of Geographic Data Science (GDS), a discipline established at the intersection between Geographic Information Science (GIS) and Data Science. The course covers how the modern GIS toolkit can be integrated with Data Science tools to solve practical real-world problems. Core to the set of employable skills to be taught in this course is an introduction to programming tools for GDS in R and Python. The programme of lectures, guided practical classes and independent study illustrate how and why GDS is useful for social science applications.
Applied Geographic Information Science (ENVS609)
Credits: 15 /
Semester: semester 1
This module introduces how the tools of GIS can be used to create digital representations of the world and, through a framework of Geographic Information Science, reviews the potential problems and pitfalls of doing so. The module is delivered through lectures supplemented by practical labs that develop familiarity and skills in the application of GIS.
Applied Data Management and Wrangling (ENVS615)
Credits: 15 /
Semester: semester 1
The digital turn in the social sciences has been marked by a huge increase in the types of data which are now available to analyse. The two enduring characteristics of many these new forms of data concerned with human dynamics are time and location; however, these attributes require special treatment by the social sciences. The content of this module reflects various spatial turns within the social sciences and concerns how techniques of modern spatio-temporal data analytics can be integrated with Data Science tools to solve practical real-world problems. Students will apply these methods and tools to a real-world dataset and are assessed through two pieces of coursework that demonstrates their ability to conduct an appropriate and rigorous analysis.
Optional modules
Database and Information Systems (COMP518)
Credits: 15 /
Semester: semester 1
This module focuses on how databases are used in modern information systems. They are at the heart of almost all systems, such as supermarket checkouts, online banking, home rentals, and much more. One of the most successful data definition and manipulation languages is SQL, which will be covered in detail. The module will also introduce some of the fundamental concepts in computer science, as well as the mathematical underpinnings of relational databases and the techniques used to support concurrency and reliability in large information systems.
Big Data Analytics (COMP529)
Credits: 15 /
Semester: semester 1
This module provides an initial overview of key algorithms and algorithmic approaches and corresponding software environments used when developing solutions to Big Data problems and explains how to use these to analyse data. A significant portion of statistics, some advanced AI approaches as well as key deterministic and hybrid algorithms are included to support the development of future data analytics and to understand how to develop stochastic, machine learning and hybrid algorithms that can exploit Big Data and can be applied to solve real life problems.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
Semester two
You will learn about statistical techniques for handling spatial data and the latest debates in the discipline through seminars and essays. They will also gain knowledge of web-based map visualization and analysis, and an understanding of the importance of time and location in new forms of data.
Optional modules give you the chance to learn about biologically inspired optimization, population science theory, and digital trace data analysis. Another module explores social and spatial inequalities and their inter-relations through four themes, providing insight into government responses to these issues in the UK.
Compulsory modules
Spatial Modelling for Data Scientists (ENVS453)
Credits: 15 /
Semester: semester 2
An introduction to a range of statistical techniques specifically designed to handle spatial data, building upon the more generic social survey analysis skills and R programming skills covered and developed in ENVS450, the pre-requisite of Module ENVS453.
Theorising Human Geography (ENVS416)
Credits: 15 /
Semester: semester 2
This is the core theoretical course for all students on a Human Geography Masters introducing students to the latest debates in the discipline. It is taught through a series of staff-led seminars with pre-set readings and assessed through a 4000-word essay through which students identify the session closest to their individual research interests and agree a topic with that member of staff who provides formative guidance and feedback. This enables students to undertake an in-depth piece of work at master’s level on the topic that most interests them, and which they may go on to study in more detail later should they undertake doctoral studies. A formative essay plan submission helps support students in the development of their essay.
Web Mapping and Geovisualisation (ENVS456)
Credits: 15 /
Semester: semester 2
Through this module students will gain an understanding of how maps can be visualised online through a number of web platforms. Additionally, the internet will be presented both as a source of new data, and provide analytical functionality that can assist when solving geographic problems. Geographic data can be any dataset that can be visualised in a map. The module is taught through a mixture of lectures and practicals, and is assessed through two summative projects.
Optional modules
Computational Intelligence (COMP575)
Credits: 15 /
Semester: semester 2
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
Population Data Science (ENVS418)
Credits: 15 /
Semester: semester 2
The module provides an introduction to fundamental techniques of population data science theory. New forms of data in the way of digital traces are becoming more easily accessible offering a unique opportunity to understand societal population issues at an unprecedented temporal and spatial granularity and a global scale. These data, however, represent a challenge for traditional demographic approaches. This module provides an introduction to the use of novel methods to analyse digital trace data.
Social and Spatial Inequalities (ENVS357)
Credits: 15 /
Semester: semester 2
This module provides insight into social and spatial inequalities, and their inter-relations. The module will consider how and why inequalities might have persisted over time, how social inequalities have specific geographies, and the implications of this unevenness for those who are marginalised. The module is structured through four major themes: for example, inequalities and the labour market; ethnicity and inequalities; spatial understandings of poverty; and theories about inequality. The difficulties in defining and measuring social and spatial inequalities, and how such definitions may relate to broader theories, perspectives or frameworks of relevance are issues covered in the module, as well as how these terms are interpreted and (mis-)represented. The module draws on empirical evidence, theoretical approaches and policy responses. The module provides insight into government responses that aim to combat social and spatial inequalities and related issues in the UK, at the regional and sub-regional level.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
Final project
You’ll attend an introductory seminar early on to give you a better understanding of what’s expected in writing your dissertation and provide you with some guidance on forming your dissertation ideas and topic.
You’ll also have several sessions to discuss your progress and help you focus your topic and define your areas of interest. Additionally, you’ll have supervision sessions or pastoral tutorials with your dissertation supervisor to support you in developing and completing your dissertation.
Compulsory modules
Dissertation - Geographic Data Science (ENVS492)
Credits: 60 /
Semester: summer
The module is designed around the goal of giving students the opportunity to fully and independently develop a theme in Geographic Data Science. Throughout a self-directed but staff-supported approach, students will identify a relevant topic in Geographic Data Science, will develop it into an academic project, and will complete the research required. By the end of the module, the student should achieve the level of a publishable academic paper.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
How you'll learn
You’ll learn across a variety of teaching methods, like lectures, seminars, and hands-on workshops in the computer lab. Each module usually starts with a brief lecture by the module leader, followed by some independent research or computer-based exercises.
You’ll either work on these assignments on your own or in a group project. After that, you’ll present your findings to the other students and the module leader, which will lead to a group discussion. This is a formative exercise, so you may even get feedback from your peers on your presentation.
How you're assessed
Assessments in this programme will provide you with the opportunity to pursue avenues of these fields that are relevant to your particular interests and career aspirations. The assessment is therefore designed flexibly to provide student centred, research-led learning.
Assessment takes the form of short (~3000 word) reports, computational essays, oral presentations, computer exercises, examinations and a 12,000 maximum word dissertation.
Liverpool Hallmarks
We have a distinctive approach to education, the Liverpool Curriculum Framework, which focuses on research-connected teaching, active learning, and authentic assessment to ensure our students graduate as digitally fluent and confident global citizens.
Support for students with differing needs from the Disability advice and guidance team. They can identify and recommend appropriate support provisions for you.
This program will give you the high-level skills you need to kickstart your career in applied geographical information science, data science with a geographic focus, or research with expertise in spatial analysis, GIS, and data science.
Career support from day one to graduation and beyond
Your career options after graduation are diverse and exciting. You might pursue a PhD or postdoctoral research, work as a GIS/Data Science consultant, be employed in the commercial or public sector, work in IT consulting, or teach in higher education. With the programming and web development skills you’ll gain, you’ll be in high demand in the job market for your newer spatial data handling skills, which are more in demand than traditional GIS skills.
Fees and funding
Your tuition fees, funding your studies, and other costs to consider.
Tuition fees
UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)
Part-time place, per year
£6,250
International fees
Part-time place, per year
£14,150
Fees are for academic year 2024/25.
Tuition fees cover the cost of your teaching and assessment, operating facilities such as libraries, IT equipment, and access to academic and personal support.
If you're a UK national, or have settled status in the UK, you may be eligible to apply for a Postgraduate Loan worth up to £12,167 to help with course fees and living costs. Learn more about fees and funding.
Additional costs
We understand that budgeting for your time at university is important, and we want to make sure you understand any course-related costs that are not covered by your tuition fee. This could include buying a laptop, books, or stationery.
If you hold a bachelor’s degree or equivalent, but don’t meet our entry requirements, you could be eligible for a Pre-Master’s course. This is offered on campus at the University of Liverpool International College, in partnership with Kaplan International Pathways. It’s a specialist preparation course for postgraduate study, and when you pass the Pre-Master’s at the required level with good attendance, you’re guaranteed entry to a University of Liverpool master’s degree.
International applicants who do not meet the minimum required standard of English language can complete one of our Pre-Sessional English courses to achieve the required level.
English language qualification
Requirements
INDIA Standard XII
National Curriculum (CBSE/ISC) - 75% and above in English. Accepted State Boards - 80% and above in English.
Liverpool bursts with diversity and creativity which makes it ideal for you to undertake your postgraduate studies and access various opportunities for you and your family.
To fully immerse yourself in the university experience living in halls will keep you close to campus where you can always meet new people. Find your home away from home.
Discover what expenses are covered by the cost of your tuition fees and other finance-related information you may need regarding your studies at Liverpool.
Contact us
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