Our MRes in Web Science and Big Data Analytics is highly flexible and specific, and is tailored to suit students' individual needs. It is problem-based learning and students will be encouraged to bring up their own technical problems for research, though not required. They will be able to choose their optional modules from a wide range of specialised options, including programming and analytical modules, and will research and write a dissertation based on a research project. It is a cost-effective way of addressing a specific technical problem the industry is facing. More specifically,
- students will start with a specific problem and choose modules based on the needed knowledge,
- then be liaised with their academic or industrial supervisor to choose a study area of mutual interest, and
- research and write a dissertation based on a 10-month research project.
It is intended for students who have a background in the Internet-based businesses (though not essential) and who have a specific technical question in mind for a substantial research project. We also offer the more Teaching orientated MSc Web Science and Big Data Analytics.
In our MRes Web Science and Big Data Analytics, students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web and essential computational and statistical skills; they will not only learn the state of the art (Web) search and information retrieval technologies and their underlying computational and statistical methods, but also study essential large-scale data analytics to discover and extract insights, patterns, and useful knowledge from vast amounts of unstructured data produced daily by (Web) users and systems in various fields.
If you've thought about taking the skills you'll learn into the media industry, then why not get a head start and apply for this Graduate Data Scientist role, a 2 year fixed term contract that will see you complete our MRes in year 1 and year 2 working full-time for Channel 4! You'll need to apply to Channel 4, by Midnight 15th March 2017, and UCL to be considered.
Students undertake courses to the value of 180 credits. The programme consists of two core modules (30 credits), four option modules (60 credits), or three option modules with one elective module, and the research dissertation (90 credits).
EDUCGE01 Investigating Research
This module is taught by UCL Centre for Advancing Learning and Teaching.
More information can be found here.
EDUCGE02 Researcher Profesional Development
This module is taught by UCL Centre for Advancing Learning and Teaching.
More information can be found here.
COMPGI08 Graphical Models
The module provides an entry into probabilistic modeling and reasoning, primarily of discrete variable systems. Very little continuous variable calculus is required, and students more familiar with discrete mathematics should find the course digestible. The emphasis is to demonstrate the potential applications of the techniques in plausible real-world scenarios related to information retrieval and analysis. Concrete challenges include questionnaire analysis, low-density parity check error correction, and collaborative filtering of Netflix data.
COMPGI14 Machine Vision
The course addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.
At the end of the course, students will be able to understand and apply a series of probabilistic models of images and objects in machine vision systems. To understand the principles behind face recognition, segmentation, image parsing, super-resolution, object recognition, tracking and 3D model building.
COMPGI15 Information Retrieval & Data Mining
The course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. Practical retrieval and data mining applications such as web search engines, personalisation and recommender systems, business intelligence, and fraud detection will also be covered.
Students are expected to master both the theoretical and practical aspects of information retrieval and data mining.
COMPGI17 Affective Computing and Human-Robot Interaction
The module targets students who have no previous knowledge in cognitive science and emotion theory. The aim of Part 1 is to give a basic introduction to the theory of emotion from psychology and neuroscience viewpoints and to understand its importance in human decision and communication processes. Part 2 will concentrate on the application of machine learning techniques to emotion recognition by looking at current applications in entertainment, education, and health. Part 3 will focus on the challenges in designing robots that are capable of socially interacting with humans.
COMPGI18 Probabilistic & Unsupervised Learning
This module provides students with an in-depth introduction to statistical modelling and unsupervised learning techniques. It presents probabilistic approaches to modelling and their relation to coding theory and Bayesian statistics. A variety of latent variable models will be covered including mixture models (used for clustering), dimensionality reduction methods, time series models such as hidden Markov models which are used in speech recognition and bioinformatics, independent components analysis, hierarchical models, and nonlinear models.
COMPGI19 Statistical Natural Language Processing
The course introduced the basics of statistical natural language processing (NLP) including both linguistics concepts such as morphology and syntax and machine learning techniques relevant for NLP.
Students successfully completing the module will understand relevant linguistic concepts; relevant ML techniques, what makes NLP challenging (and exciting), how to write programs that process language and how to rigorously formulate NLP tasks as learning and inference tasks, and address the computational challenges involved.
COMPGV10 Computer Graphics
The course aims to introduce to students the fundamental concepts of 3D computer graphics and give the students all the knowledge needed for creating an image of a virtual world from first principles.
The students will be able to define a virtual world and create images of it. They will know how to write a basic ray tracer, and use a graphics library such as OpenGl (or equivalent).
COMPGW01 Complex Networks and Web
This module introduces the fundamental concepts, principles and methods in the interdisciplinary academic field of network science, with a particular focus on the Internet, the World Wide Web and online social media networks. Topics covered include graphic structures of networks, mathematical models of networks, the Internet topology, structure of the Web, community structures, epidemic spreading, PageRank, temporal networks and spatial networks.
On successful completion of this module the students will be able to define and calculate basic network graphic metrics, describe structural features of the Internet and the Web, relate graphic properties to network functions and evolution, explore new angles to understand network collective behaviours, design and conduct analysis on large network datasets.
COMPGW02 Web Economics
The course is intended to provide an introduction of the computing systems and their economics for the production, distribution, and consumption of (digital) goods and services over the Internet and web. While the basic economic principles are covered to understand the business aspects of web-based services, the course is primarily focused on the computational and statistical methods for implementing, improving and optimizing the internet-based businesses, including algorithmic mechanism design, online auctions, user behavior targeting, yield management, dynamic pricing, cloud-sourcing, social media mining and attention economics. Practical applications such as Google’s online advertising, Ebay’s online auction, and Amazon’s cloud computing will also be covered and discussed.
Students will be expected to master both the theoretical and practical aspects of web economics.
You will need to choose a minimum of 45 and a maximum of 60 credits from the optional modules.
Up to 15 credits as an elective from the Postgraduate Syllabus Index.
The modules that make up a programme are either core, optional or elective, which reflects whether they must be taken or can optionally be taken. The programme’s curriculum (also called a programme diet) will prescribe in what combinations modules can be taken, any restrictions on doing so, and how much credit can and must be taken.
Core/compulsory modules are fundamental to the programme’s curriculum and students must take these. You will be automatically allocated a place on any core modules for your programme and will not need to select these during the module selection process. There will be no timetable clashes between your programme’s core modules.
Optional modules are strongly related to the programme and students can choose which of these they wish to take, usually from within specific groups (for example, a student may be asked to choose two optional modules from one group and three from another, etc.) Places of optional modules are strictly limited (due to spatial, resource and timetable constraints) and will be allocated on a first come first serve basis. Some optional modules have pre-requisites which students will need to meet in order to be eligible for a place.
Elective modules are not programme specific, but allow students the opportunity to explore their interests more widely. Students are usually restricted to taking one or two elective modules. There is no guarantee of being accepted onto an elective module. These modules are core or optional on other programme diets, consequently students on these programmes will be given priority. Any remaining places will then be allocated on a first come first served basis. Some elective modules have pre-requisites which students will need to meet in order to be eligible for a place.
Please note: timetable clashes between optional and elective modules from different specialisations are inevitable and this can result in limiting the available choices. It is the student’s responsibility to select modules that do not clash in order to meet UCLs minimum attendance requirements. Please speak to your Programme Director and/or Programme Administrator if you have any queries.
Non-Computer Science students should note that priority on COMP* modules will always be given to Computer Science students in the first instance.
- The MRes is designed for people who have a first or upper second class honours degree (or equivalent) in a highly quantitative subject such as Computer Science, Mathematics, Electrical Engineering or the Physical Sciences.
- Industrial experience may compensate for lesser degrees or lack of technical qualification.
- Successful candidates will have proven experience with programming languages such as C/ C++, Java or Python.
- Applicants should have a strong foundation in mathematics including vector and matrix algebra, calculus, and probability and statistics.
English Language Requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency. The English language level for this programme is: Good. Further information can be found on our English language requirements page.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
UK/EU fees (FT): £11,800 for 2017/18
Overseas fees (FT): £25,130 for 2017/18
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarship and Funding website.
Tuition Fee Deposit
This programme requires that applicants firmly accepting their offer pay a deposit. This allows UCL to effectively plan student numbers, as students are more demonstrably committed towards commencing their studies with us.
For full details about the UCL tuition fee deposit, please see the central UCL pages.
Tuition fee deposits within the Department of Computer Science are currently listed as:
|*where part-time is an available mode of study|
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Students are advised to apply as early as possible due to competition for places, later applications may be less successful. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Deadline 17 June 2017.