- Entry requirements: Related 2:1 degree (or equivalent)
- Full-time: 12 months
- Part-time: 24 months
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.
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.
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.
This course is for you if you want to understand and analyse the role of geography in everyday life through geographical and computational reasoning.
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
International students may be able to study this course on a part-time basis but this is dependent on visa regulations. Please visit the Government website for more information about student visas.
If you're able to study part-time, you'll study the same modules as the full-time master's degree over a longer period, usually 24 months. You can make studying work for you by arranging your personal schedule around lectures and seminars which take place during the day. After you complete all the taught modules, you will complete your final dissertation or project and will celebrate your achievements at graduation the following term.
Studying part-time means you can study alongside work or any other life commitments. You will study the same modules as the full-time master's degree over a longer period, usually 24 months. You can make studying work for you by arranging your personal schedule around lectures and seminars which take place during the day. After you complete all the taught modules, you will complete your final dissertation or project and will celebrate your achievements at graduation the following term.
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.
This module is intended to explore qualitative research methods in a holistic manner; moving from research philosophy, through design to individual research methods and analysis. The module covers a range of qualitative research methods through a mixture of lectures and workshops. In undertaking this module students will consider how research design and individual research methods need careful selection to suit the specific research problems or questions under investigation.
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.
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 specifically the programming language ‘Python’, which is the only scripting langauge officially supported by the industry-leading GIS packages ‘Arc/GIS’ and ‘QGIS’.
Core to the set of employable skills to be taught in this course is an introduction to programming tools for GDS.
The programme of lectures, guided practical classes and independent study illustrate how and why Geographic Data Science is useful for social science applications.
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 both lectures and supplemented by practical labs that develop familiarity and skills in the application of GIS.
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.
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.
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.
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.
This is the core theoretical human geography course for all students on 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 5000 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.
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.
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
The two enduring characteristics of many 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.
The module provides an introduction to fundamental techniques of population science theory. New forms of data in the way of digital traces are becoming more easily accessible offering a unique opportunity to undertand societal population issues at an unprecedented temporal and spatial granulity 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.
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; amd 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.
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.
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.
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.
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.
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.
You will work with academic staff within the Geographic Data Science Laboratory and the Centre for Spatial Demographic Research.
You can also access the Consumer Data Research Centre; created as part of a £6m investment in Big Data by the Economic and Social Research Council.
From arrival to alumni, we’re with you all the way:
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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.
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.
Your tuition fees, funding your studies, and other costs to consider.
|UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)|
|Full-time place, per year||£11,550|
|Part-time place, per year||£5,775|
|Full-time place, per year||£21,400|
|Part-time place, per year||£10,700|
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 tuition fees, funding and Postgraduate Loans.
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.
Find out more about the additional study costs that may apply to this course.
We offer a range of scholarships and bursaries to help cover tuition fees and help with living expenses while at university.
The qualifications and exam results you'll need to apply for this course.
My qualifications are from: United Kingdom.
|Postgraduate entry requirements||
You will normally need a 2:1 honours degree or above, or equivalent. This degree should be in a relevant subject.
Applications from those with significant relevant experience and professional qualifications will be assessed on an individual basis.
A Pre-Master's course, which prepares international students for postgraduate study with us, is offered by the University of Liverpool International College. If you hold a bachelor's degree or equivalent, but don't meet our entry requirements, successfully completing the relevant Pre-Master's pathway means you're guaranteed a place on your chosen course.
You'll need to demonstrate competence in the use of English language. 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|
View our IELTS academic requirements key.
Standard Level 5
|TOEFL iBT||88 or above with minimum scores in components as follows: Listening and Writing 17, Reading 17, Speaking 19.|
|INDIA Standard XII||70% or above from Central and Metro State Boards|
|Hong Kong use of English AS level||C|
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Last updated 29 March 2023 / / Programme terms and conditions /