The digital landscape of sustainability is fast changing - apply your digital skill to solve global sustainability issues with the Data Science and Artificial Intelligence for Sustainability MSc

Sustainability depends on areas such as energy production and environmental management, making it a complex problem. Energy supply is fundamentally important to our homes and workplaces. Future energy supply has to be affordable, stable and secure. Ecosystem management needs to account for the food, water and energy nexus and socio-political context.
Digital transformation is an emerging discipline using powerful digital tools and various digital models to solve and manage the increasingly complex problems related to sustainability energy systems. Within this discipline, digital tools and models (such as Artificial Intelligence) are used to analyse data from different energy systems and sources and drive new control and operational strategies and business models, whilst supporting key objectives such as reaching Net Zero emissions.


  • Start dateFull-time: October. Part-time: October
  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%.
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course is suitable for engineering, computer science, mathematics, environmental, energy and information technology graduates wishing to pursue a technical management career in the rapidly growing area of digital transformation for sustainability. It develops professional engineers, scientists and practitioners with the multidisciplinary skills and ability to analyse current and future sustainability challenges across private and public sectors. 

Your career

The international nature of this growing field allows Cranfield graduates to develop diverse and rewarding global careers in industry, government or research.

Example careers:

  • Energy Analyst – data science,
  • Offshore Energy Analyst,
  • Energy and Sustainability Analyst,

Cranfield Careers and Employability Service

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

Why this course?

Countries transitioning to net-zero face a number of challenges in different sectors of their economies. In the core of this transition, affordable and secure energy supply, sustainable development, and the digital transitions aggravate these challenges for organisations to support the government towards net-zero. 

You will benefit from dedicated state-of-the-art facilities including unique engineering-scale facilities for the development of efficient technologies with low CO2 emissions. In addition to management, communication, teamwork and research skills, each student will attain at least the following learning outcomes from this degree course:

  • Design appropriate methods for data acquisition and processing
  • Develop AI algorithms to create data-driven solutions, driving sustainability within organisations.
  • Apply Data Science principles through simulation platforms and programming languages to sustainability problems.

This MSc is supported by our team of professorial thought leaders, including Professor Nazmiye Ozkan, who is influential in the field of net-zero transition and digital energy, and an integral part of this MSc.

My group project was actually with an industry, one of the leading industries in the renewable energy investment sector. So we worked with them as a consultant – so it was like working in industry, not just purely academic.
Data and data skills are really important to the energy sector as it's developing and transitioning. AI and blockchain technologies are really important to that transition but it's really important that we have the right skills and the technologies to process that data and use it accordingly.
AI blockchain in the energy sector is about that user experience. It's about taking that user centric design, place for place and understanding how we can shift that mass market and then how we can guarantee the experience that is a commercially viable for that organisation and doesn't degrade the experience that they currently have with a centralised generation system.
I think energy is one of our key challenges we face as a society. If we don't address the energy challenge, if we don't decarbonise our energy systems which make up a big chunk of our CO2 production, at the moment all else probably won't matter. Why AI and blockchain? Because I think they are two of the key technologies which are going to help us address those now there are lots of other technologies as well there are things like the Internet of Things. I think there's still a lot of work to be done on other social sciences and psychology of how to change people's behaviour because ultimately all of these technologies only make a difference if people decide to use them if people act on the advice – for example, given by an AI system – if people trust to hand over the controls to an automated system.

Informed by Industry

We have a world class reputation for our industrial-scale research and pilot-scale demonstration programmes in the energy sector. Close engagement with the energy and transport sectors over the last 20 years has produced long-standing strategic partnerships with the sectors’ most prominent players. The strategic links with industry ensures that all of the material taught on the course is relevant, timely and meets the needs of organisations operating within the energy sector. This industry-led education makes our graduates some of the most desirable in the world for energy companies to recruit.

Course details

The taught programme for the masters is generally delivered from October to February and is comprised of eight modules. Each of the first five modules are delivered over two weeks. Generally the first week involves intensive teaching while the second week has fewer teaching hours to allow time for more independent learning and completion of the assessment.

Water course structure diagram

Students on the part-time programme will complete all of the modules based on a flexible schedule that will be agreed with the course director.

Course delivery

Taught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%.

Group project

The group project is an applied, multidisciplinary, team-based activity. Often solving real-world, industry-based problems, you are provided with the opportunity to take responsibility for a consultancy-type project while working under academic supervision. Success is dependent on the integration of various activities and working within agreed objectives, deadlines and budgets. Transferable skills such as team work, self-reflection and clear communication are also developed.

Individual project

The individual project is the chance for you to focus on an area of particular interest to you and your future career. You will select the individual project in consultation with the Thesis Co-ordinator, your allocated supervisor and your Course Director. These projects provide you with the opportunity to demonstrate your ability to carry out independent research, think and work in an original way, contribute to knowledge, and overcome genuine challenges in the energy industry. Many of the projects are supported by external organisations.


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

GIS and Spatial Data Management

Module Leader
  • Dr Joanna Zawadzka
    Geographical information is increasingly prevalent in our daily life, affecting personal leisure activities as much as business services and the workplace. Geographical information represents a key theme in environmental management and it has been estimated that some 80% of the data used for environmental, business and policy-oriented decision-making is geographical in nature. Such spatial data requires a structured approach in its management if the maximum benefit is to be derived from analysis and dissemination. This module provides a solid introduction to the issues concerning the management of spatial information and the tools to do so, with a predominant focus on ESRI’s software solutions. 

    Spatial data models and database structures – vectors, rasters, topology, ordered and indexed lists, hierarchical, network, relational, object oriented, hybrid, metadata.
    Mapping fundamentals – geodesy, projections, cartography, abstraction of the real world into map form.
    Analysis approaches – database manipulation, reclassification, overlay, spatial modelling.
    Data specification and standards – use cases, interoperability, INSPIRE.
    Data visualisation.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Define the functional components of a GIS, projections, data and modelling processes for managing spatial data,
  • Organise and integrate, using appropriate data structures, spatial and aspatial data within a GIS,
  • Analyse, evaluate and prepare data within appropriate spatial databases structures for information dissemination,
  • Examine the role of the INSPIRE Directive and FAIR data principles for enabling interoperability between spatial data infrastructures,
  • Perform and disseminate the results of analysis and data manipulation via maps, tables and other appropriate media.

Decision Science

Module Leader
  • Dr Alice Johnston

    The module introduces the context of environmental decision making, providing both a conceptual overview and practical tools for addressing environmental management problems. It aims to promote an understanding of weight-of-evidence approaches used to inform real-world problems by research, industry, and government. Central to this is an understanding of the strengths and limitations of different approaches and aspects of decision science, the social, economic, and environmental trade-offs made during decision-making, and how real-world complexity and future uncertainty is accounted for. Students will learn how to apply systems thinking to evaluate different courses of action in response to environmental challenges related to land, water, and/or energy. 

    • Key environmental challenges and the need for decision support tools.
    • Different stakeholders in environmental decisions.
    • Weighing the evidence for different courses of action.
    • Big data; data analytical approaches; mathematical modelling; statistical inference; machine learning; information systems; spatial data science approaches. 
    • Environmental, economic, and social trade-offs in decisions. 
    • Accounting for real-world complexity and future uncertainty in decisions
Intended learning outcomes

On successful completion of this module you should be able to:

  • Evaluate the strengths and weaknesses of decision science approaches for practical environmental management problems.
  • Conceptualise complex environmental issues using systems thinking to weigh-up different courses of action.
  • Critically appraise the need for stakeholders to trade-off environmental, economic and social concerns, and the limitations of decision science approaches in accounting for real-world complexity and future uncertainty.

Scientific Python

Module Leader
  • Dr Daniel Simms
    Writing code opens-up new approaches to creative problem solving and allows us to move beyond the limitations of any particular software. This module aims to develop your skills and confidence to write your own code in the Python programming language and access a broad ecosystem of packages and tools that comprise the foundations of Data Science.

    Fundamentals of programming:
    Python syntax, types, calculations, variables, strings, and object-oriented programming concepts.
    Branching, iteration, and recursion.
    Abstraction, functions, and classes. 
    Files, packages, and imports.
    Testing, debugging, and exceptions.
    Version control.
    Writing efficient code.
    Python scientific packages for data analysis and visualisation.
    Geospatial data.
    Jupyter Notebooks and Colaboratory.
    Statistics for Data Science.

Intended learning outcomes On successful completion of this module you should be able to:
Explain how computer code is used in science and engineering.
Create Python code for solving set problems and develop confidence in writing code for your own projects.
Analyse data using Python libraries and visualise results as graphs, maps and interactive figures.

Data Analytics for Energy Systems

    This module will introduce you to data analytics, overview challenges and solutions in using data analytical tools in energy systems, present approaches to predictive and descriptive data mining, classification, statistical methods, regression models and explain unsupervised learning techniques suitable for new information discovery. Students may benefit from knowledge of basic concepts of statistics methods for performance assessment and evaluation, regression models (linear, non-linear, Gaussian, Bayesian Logistic), and classification methods.
    • Introduction to Data Analytics,
    • Statistics refresher and data pre-processing,
    • Predictive analytics: regression refresher and classification methods,
    • Clustering and dimensionality reduction,
    • Graph analysis and visualisation,
    • Software and tools for data analytic,
    • Case study: application of data pre-processing and data analytical tools for a specific dataset.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically analyse stages of the data analytics workflow; and establish a data analytics workflow based on the available data and formulated requirements,
  • Critically evaluate data analysis and visualisation techniques with respect to data analytics stages, using graph analysis and visualisation techniques,
  • Analyse and apply algorithms for discovery of new information from the large data sets, using statistics, regression, classification methods,
  • Evaluate performance of the algorithms and quality of the data analysis outcomes.

Artificial Intelligence for Energy Systems

Module Leader
  • Dr Da Huo
    With more and more measurement and control devices installed in energy systems, data analytics using AI technology to support planning and operation of energy systems has shown significant advantages. The scientific and technical concepts of machine learning and AI methods/tools and their potential advantages in the energy sector will be taught in this module. One example of this is to use smart metering data to analyse a network’s hosting capacity of solar photovoltaic systems, and to analyse a power system’s technical and non-technical energy losses.  The module aims to provide you with data analytical skills from machine learning and AI technology, and evaluate the advantages/disadvantages of their applications in the energy industry. The module also aims to provide you with essential skills (e.g. computer programming and coding in Python) for applying machine learning and AI in the energy industry.
    • Design of an appropriate analysis toolkit specific to analyse the examples of applications of machine learning and AI technology in energy industry,
    • Analysing the development and scaling/design of the AI technologies by evaluating the advantages/disadvantages of the available examples in the areas of applications in energy systems,
    • AI techniques (e.g. Artificial Neural Networks, RNN, Reinforcement Learning), and skills to manage, process and use data to support network operation and planning,
    • Techniques to evaluate where AI can be used and the potential benefits to the energy industry. 
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Critically analyse the state-of-the-art of the applications of machine learning (ML) and AI technology in the energy industry,
  2. Identify and assess the requirements of different AI/ML techniques and their contributions to improve the planning and operation of energy systems,
  3. Implement AI/ML algorithms, estimate their performance in a simulation environment and assess their performance for a realistic case study,
  4. Evaluate the advantages and disadvantages of particular AI techniques within the context of the energy industry.

Sustainability and Environmental Assessment

Module Leader
  • Dr Gill Drew

    Environmental impact assessment and life cycle analysis are important tools for evaluating the sustainability of complex renewable energy technologies and industrial processes or products. The tools and concepts taught in this module will enable you to assess the sustainability of a case study from an environmental standpoint. Analysis of relevant case studies to demonstrate the assessment process, including how to account for uncertainty and sensitivity analysis.

    This module is 10 credits.

    • Environmental impact assessment and sustainability,
    • Environmental Impact Assessment,
    • Indicator selection and analysis,
    • Life cycle analysis, and carbon footprinting,
    • Social impact assessment,
    • The technique of demand forecasting.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically assess the emissions and waste production throughout the lifecycle of a technology or process,
  • Design a framework to ensure compliance of a  process, product or service to support the transition to Net Zero that is  compliant with regulatory and voluntary requirements,
  • Evaluate forecasting techniques as one of the fundamental aspects of environmental assessment,
  • Critically evaluate different environmental and social appraisal metrics,
  • Design and implement a strategy to assess the environmental sustainability of a process or technology, and evaluate the associated uncertainties.

Elective modules
One of the modules from the following list needs to be taken as part of this course

Computational Fluid Dynamics for Renewable Energy

Module Leader
  • Dr Patrick Verdin

    To appraise existing Computational Fluid Dynamics (CFD) techniques and tools for modelling, simulating and analysing practical engineering problems related to renewable energy, with hands on experience using commercial software packages used in industry.

    • Introduction to CFD: Introduction to the physics and understanding of governing equations (continuity, momentum, energy and species conservation) and state of the art Computational Fluid Dynamics including modelling, grid generation, simulation, and high-performance computing. Case study of industrial problems and the physical processes where CFD can be used,
    • Computational engineering exercise: specification for a CFD simulation. Requirements for accurate analysis and validation. Introduction to turbulence and practical applications of turbulence models, introduction to turbulence and turbulent flows, traditional turbulence modelling,
    • Advanced turbulence modelling: introduction to Reynolds-averaged Navier Stokes (RANS) simulations and large-eddy simulation (LES),
    • Practical sessions: offshore renewable energy problems (flow around wind and tidal turbines) will be solved employing the widely-used industrial flow solver software FLUENT. These practical sessions will cover the entire CFD process including grid generation, flow solver, analysis, validation and visualisation.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Assemble and evaluate the different components of the CFD process,
  • Explain the governing equations for fluid flows and how to solve them computationally,
  • Compare and contrast various methods for simulating turbulent flows applicable to civil and mechanical engineering, especially offshore renewable energy applications such as wind turbines and tidal turbines,
  • Set up simulations and evaluate a practical problem using a commercial CFD package,
  • Design CFD modelling studies of renewable energy devices.

Energy Entrepreneurship


    In this world of downsizing, restructuring and technological change, notions of traditional careers and ways of creating value have all been challenged. People are depending more upon their own initiative to realise success. Never, it seems, have more people been starting their own companies than now, particularly to exploit the World Wide Web. There’s no single Government (in either the developed or the developing world), which is not paying at least lip service to enterprise development. The aim of this module is to provide you with knowledge and skills relevant for starting and managing new ventures across the entrepreneurial life cycle. Moreover, it will prepare you on how to prepare a business pitch to an investor.

    • Entrepreneurial risk, performance and environment,
    • Business planning techniques and their application in entrepreneurial ventures,
    • Venture strategy in dynamic markets,
    • Start-up and resources to exploit a profit opportunity,
    • The evolution of the venture and managing growth,
    • Protecting and securing intellectual capital: IPR and antitrust law,
    • Financial management for new ventures: financing a start-up,
    • The entrepreneurial financing process: buying and selling a venture.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Assess the impact of the business environment on entrepreneurial opportunity identification and exploitation.
  • Critically apply the theoretical underpinning of entrepreneurship to the process of managing risk in new ventures and supporting their development.
  • Compare and contrast how managerial challenges vary across the life cycle of an entrepreneurial venture.
  • Assess the likely financial needs of a new venture and pitch for finance.
  • Develop and write a credible business plan for a new venture.

Elective modules
One of the modules from the following list needs to be taken as part of this course

Energy Systems Case Studies

    The module aims to provide you with a deep understanding of the truly multidisciplinary nature of a real industrial project.  Using a relevant case study, the scientific and technical concepts learned during the previous modules will be brought together and used to execute the analysis of the case study.
    • Work flow definition: setting up the single aspects to be considered, the logical order, and the interfaces.
    • Design of an appropriate analysis toolkit specific to the case study
    • Development of a management or maintenance framework for the case study
    • Multi-criteria decision analysis [MDCA] applied to energy technologies to identify the best available technology. 
    • Energy technologies and systems: understanding the development and scaling/design of the technologies by applying an understanding of the available resources in the assigned location;
    • Public engagement strategies and the planning process involved in developing energy technologies.
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Critically evaluate available technological options, and select the most appropriate method for determining the most preferred technology for the specific case study.
  • Demonstrate the ability to work as part of a group to achieve the stated requirements of the module brief.
  • Organise the single-discipline activities in a logical workflow, and to define the interfaces between them, designing an overall multidisciplinary approach for the specific case study.

Short Research Project


    The purpose of this module is to provide you with experience of scoping, designing and delivering of a short research project. This requires an understanding of the background literature, as well as relevant analysis techniques. You will need to agree the project scope early on and deliver the project within the two weeks of the module. The module will allow you to draw on the experience and learning from the previous modules. 

    Technical requirements specific to project brief and relevant to the MSc course and route.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Deliver a research project, including identification of research methods,
  • Design a data analysis method appropriate to their chosen research topic,
  • Execute a short project, analyse the outcomes and provide sound recommendations.

How to apply

Click on the ‘Apply now’ button below to start your online application.

See our Application guide for information on our application process and entry requirements.