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Spatio-temporal Analytics and Big Data Mining MSc

London, Bloomsbury

Unveil the potential of 'big' data with our Spatio-Temporal Analytics and Big Data Mining MSc programme. Embrace the age of smart sensors, smartphones, and social media as you master GIScience, databases, spatial analysis, data mining, and analytics. Students are equipped with the prowess to dissect, represent, and model vast spatio-temporal datasets, making impactful strides in diverse industries. Become a data-driven professional adept at navigating the dynamic landscape of information.

UK students International students
Study mode
UK tuition fees (2024/25)
£19,300
£9,650
Overseas tuition fees (2024/25)
£37,500
£18,750
Duration
1 calendar year
2 calendar years
Programme starts
September 2024
Applications accepted
Applicants who require a visa: 16 Oct 2023 – 05 Apr 2024

Applications closed

Applicants who do not require a visa: 16 Oct 2023 – 30 Aug 2024
Applications close at 5pm UK time

Applications open

Apply for this course

Entry requirements

A minimum of an upper second-class UK Bachelor's degree in a relevant discipline (such as engineering, mathematics, computer science, environmental science, human or physical geography, geology, forestry, oceanography, or physics) or an overseas qualification of an equivalent standard. Applicants with relevant professional experience are also considered.

The English language level for this programme is: Level 1

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree


The Spatio-Temporal Analytics and Big Data Mining MSc programme at UCL Civil, Environmental & Geomatic Engineering offers a comprehensive and dynamic educational experience. Students enrolled in this programme will gain a robust foundation in computational skills essential for navigating the realm of big data analytics. These skills encompass various facets, including visualization, prediction, clustering, and simulation through statistical and machine learning techniques. The curriculum also delves into the intricacies of retrieving and mining vast datasets from open sources, leveraging web services, cloud computing, and web and mobile applications. By engaging with actual case data and open-source software, students will develop a practical toolkit for their careers.

This programme primes graduates for roles across diverse sectors, such as consultancy, local government, public industries, and information supply. The skills acquired, particularly in Spatio-Temporal Analytics and Big Data Mining, make them valuable assets in fields ranging from data science in social media, finance, health, and telecoms to developing spatial tools and specialised software. Moreover, graduates can venture into research or entrepreneurial endeavours. The modern landscape characterised by smart sensors, smartphones, and pervasive social media has led to the proliferation of 'big' data. Through an interdisciplinary curriculum, the MSc imparts essential knowledge in GIScience, databases, spatial analysis, data mining, and analytics, empowering professionals to effectively dissect, represent, and model large and intricate spatio-temporal datasets.

Who this course is for

The Spatio-Temporal Analytics and Big Data Mining MSc programme at UCL is ideally suited for students and professionals seeking to harness the power of data in the digital age. If you have a keen interest in leveraging advanced computational tools to analyze, visualise, and model large and complex spatio-temporal datasets, this programme offers the perfect opportunity. Whether you come from a background in computer science, engineering, mathematics, or related fields, the multidisciplinary curriculum will equip you with the skills needed to excel in roles across diverse industries. If you're intrigued by the potential of 'big' data, smart sensors, and emerging technologies, and are eager to contribute to fields like data science, social media analysis, finance, health, telecoms, or urban planning, this programme will provide you with the expertise to thrive in the rapidly evolving data landscape.

Read about what it takes to study for a Master's at UCL

What this course will give you

Discover unparalleled educational opportunities at UCL Civil, Environmental & Geomatic Engineering. Located in the vibrant hub of London, our esteemed multidisciplinary department boasts a rich history of excellence in teaching and research. 

Students will be immersed in a world-class educational environment, driven by a rich history of excellence in teaching and research. With a curriculum designed to propel students towards a successful career in the dynamic field of big data analytics, this programme offers a comprehensive and dynamic educational experience. Through collaborative teamwork experiences and flexible module choices, students will gain the skills and knowledge necessary to thrive in roles spanning consultancy, local government, public industries, and the information supply sector. 

The multidisciplinary nature of the programme equips students with computational prowess, enabling our students to master big data analytics, visualisation, prediction, clustering, and simulation using statistical and machine learning approaches. By practicing with real-case data and open-source software, students will be well-prepared to navigate the complexities of modern data-driven industries. This transformative experience positions students at the forefront of civil engineering, empowering them to make a lasting impact on the ever-evolving landscape of data analytics.

Read about what you'll get out of a graduate programme

The foundation of your career

Upon completing our programme, graduates unlock a multitude of promising pathways. They seamlessly integrate into the workforce, finding placements in esteemed consultancies, construction giants, and influential government bodies. The programme lays a robust foundation for their journey towards becoming Chartered Engineers, a highly regarded professional achievement that solidifies their expertise. 

With a comprehensive skill set and in-depth knowledge, graduates are poised to excel across various sectors, spearheading projects, influencing decisions, and contributing to the advancement of the engineering landscape. This degree not only opens doors to an array of prestigious employment opportunities but also sets a well-defined trajectory for a fulfilling and impactful career.

Employability

Graduates are poised to seize opportunities in a diverse array of sectors, including consultancy, local government, public industry, and the information supply industry, while also embarking on continued research endeavours. A myriad of career pathways awaits, spanning roles as data scientists within social media, finance, health, telecoms, retail, or construction and planning industries. Graduates could also carve a niche as developers of spatial tools and specialised spatial software, researchers, or even entrepreneurs. This programme moulds data-driven professionals who thrive in the dynamic landscape of information. Armed with proficiency in space-time data analysis, statistical and machine learning techniques, parallel computing, social media analysis, and programming languages such as R and Python, graduates exhibit precision in data interpretation and scientific writing, underpinned by critical thinking skills. The programme also cultivates practical competencies in managing, modelling, analysing, and visualizing 'big' spatio-temporal data, while honing crucial attributes in teamwork, presentation, communication, and time management.

Networking

UCL offers an enriching environment for networking and professional growth. Engage with peers, industry experts, and faculty members who share your passion for engineering excellence. Through collaborative group projects, seminars, workshops, and industry partnerships, you'll forge connections that extend beyond the classroom. 

These networking opportunities provide insights into real-world challenges, offer chances to learn from industry leaders, and pave the way for potential internships and job placements. Your interactions within this vibrant community will not only enrich your learning experience but also establish a valuable network that can shape your future in the civil engineering arena.

Teaching and learning

Throughout the programme, a diverse range of teaching and learning strategies foster comprehensive skill development and intellectual growth. Graduates emerge with an array of capabilities:

Advanced knowledge and contextual understanding: Through a structured curriculum, students will acquire specialised subject knowledge. This includes grasping the characteristics of space-time data, their collection, representation, and management. Principles of spatial and spatio-temporal analysis, guided by statistical and machine learning approaches, will provide a comprehensive contextual understanding. Additionally, the programme delves into parallel computing, system design, social media analysis, text mining, and programming with R and Python, enhancing students' capabilities in these critical domains.

Practical proficiency and collaboration: The programme cultivates practical proficiency through hands-on experiences. Students will engage in managing, modelling, analysing, visualizing, and effectively communicating 'big' spatio-temporal data. Practical skills will extend to programming with R and Python, as well as utilizing spatial databases. Collaborative teamwork will be emphasised, honing presentation, communication, and time management skills crucial for success in professional environments.

Intellectual, academic, and research excellence: A strong focus on intellectual rigour and academic excellence is integral to the programme. Graduates will develop precision in data representation, analysis, and interpretation, along with the critical thinking skills needed to discern the insights and limitations of data. Scientific writing skills will be honed, enabling effective communication of findings. Moreover, students will gain foundational programming knowledge, fostering a research-oriented mindset.

Practical and innovative aptitude: The curriculum encourages innovation by equipping students with practical tools. Students will master programming with R and Python, positioning them to apply these languages innovatively in addressing complex challenges. Moreover, the programme nurtures teamwork and effective communication, essential qualities for translating ideas into tangible solutions. The programme's delivery combines lectures, seminars, and laboratory practicals, ensuring a well-rounded and engaging learning experience. Through this comprehensive approach, students will acquire both theoretical knowledge and practical skills, preparing them for successful careers in the field of spatio-temporal analytics and big data mining.

These multifaceted teaching and learning methods imbue graduates with the prowess to thrive in research, industry, and various professional domains.

 Assessment in the programme encompasses a diverse range of methods, including examinations, coursework assignments, practical exercises, a research dissertation, and a poster presentation. This holistic approach ensures that students' skills and understanding are rigorously evaluated across various dimensions of the subject matter.

Students engaging in the programme can expect a workload of approximately 40 hours per week. This allocation encompasses a mix of structured learning and teaching activities, including lectures, seminars, and tutorials. Additionally, students are encouraged to dedicate time to self-directed study, a crucial aspect of their educational journey that complements formal instruction and enhances comprehension. This balanced approach ensures a comprehensive understanding of the material and fosters the development of independent learning skills.

A Postgraduate Diploma, consisting of five core modules (75 credits) and three optional modules (45 credits), taken full-time over nine months is also offered.

Modules

You will be equipped with computational foundations and skills needed for big data analytics including visualisation, prediction, clustering and simulation with statistical and machine learning approaches, as well as retrieving and mining big (open) data, web services and cloud computing, web and mobile applications, by practising with real case data and open software.

You undertake modules to the value of 180 credits. The programme consists of three compulsory modules, five optional modules and a dissertation/report. 

The programme structure for part-time students encompasses a total of 180 credits over the course of two years. This flexible structure allows both full-time and part-time students to tailor their learning journey, ensuring a comprehensive education that suits their pace and aspirations.

The programme structure for modular/flexible students encompasses a total of 180 credits over the course of their studies. 

Compulsory modules








Optional modules









Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability are subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Spatio-temporal Analytics and Big Data Mining. Upon successful completion of 120 credits, you will be awarded a PG Dip in Spatio-temporal Analytics and Big Data Mining.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the UCL Student Support and Wellbeing team.

Fees and funding

Fees for this course

UK students International students
Fee description Full-time Part-time
Tuition fees (2024/25) £19,300 £9,650
Tuition fees (2024/25) £37,500 £18,750

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees.

Additional costs

Potential additional costs within the programme encompass various aspects. Expenses related to course materials, such as books, are contingent on the selected modules. Some modules may entail charges for field trips. In laboratory settings, specialised protective gear like lab coats and safety boots might be necessary, contributing to potential costs. Project-specific necessities can also incur additional expenses. 

While not an exhaustive list, it underscores the potential financial considerations. Material and project costs depend on individual choices. Moreover, short local visits, integral to the program, could involve public transport costs. Being cognizant of these potential costs aids in prudent financial planning throughout the programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.

Funding your studies

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

See all the ways you can fund your studies

Brown Family Bursary

Deadline: 20 June 2024
Value: £15,000 (1 year)
Criteria Based on both academic merit and financial need
Eligibility: UK

UCL East London Scholarship

Deadline: 20 June 2024
Value: Tuition fees plus £15,700 stipend ()
Criteria Based on financial need
Eligibility: UK

Next steps

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

There is an application processing fee for this programme of £90 for online applications and £115 for paper applications. Further information can be found at Application fees.

In addition to fulfilling the necessary academic prerequisites, the personal statement serves as a pivotal aspect of your application. It offers a platform to align your motivations for selecting this program with its offerings. When evaluating your application, we are interested in understanding:

  • Your rationale for pursuing graduate-level studies.
  • What specifically draws you to study this programme at UCL.
  • The unique aspects of this program that resonate with you.
  • How your personal, academic, and professional background aligns with the programme's challenging requirements.
  • Your envisioned professional trajectory upon attaining your degree.

While applications are accepted until the deadline, it is advisable to submit your application early due to the competitive nature of placements. The process typically takes around four weeks from submission to receiving an offer letter. For international applicants seeking visas, we recommend applying in advance to secure the necessary CAS number within the required timeframe. Your application journey is a significant step towards a rewarding educational experience at UCL.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

Apply for this course

Got questions? Get in touch

Civil, Environmental and Geomatic Engineering
Civil, Environmental and Geomatic Engineering

cege-pg-admissions@ucl.ac.uk

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