Are you interested in working with cutting-edge technology at the forefront of language processing?
MA Computational Linguistics is a course run by a leading research group at the University of Wolverhampton. As a Masters student on this course, you will be part of our Research Institute of Information and Language Processing (RIILP), an independent, research-driven University unit specialising in Linguistics and Natural Language Processing.
As the name suggests, Computational Linguistics (sometimes called Natural Language Processing) is the use of computers to study language. On the course, you will be able to study:
• How to use Python and the well-established NLTK library to process natural language texts;
• How to analyse real language usage;
• How to automatically translate text using computer programs;
• The use of computers to study features of language;
• Translation tools such as translation memory systems;
• Computer techniques for automatically classifying natural language texts;
• Understand how Siri, Amazon Echo and Google Home etc. work;
• How to design an experiment that will thoroughly test your research questions.
You will be mentored through this programme by experienced and leading academics from the field. Join our research group today to become part of this team of leading researchers and academics and create your path to a career in computers and language!
MA Computational Linguistics, when studied full-time, comprises of three semesters worth 60 credits each. Three modules will be studied in both Semester One and Semester Two. During the third semester, students will undertake their research project and complete a 15,000 word dissertation on any aspect of Computational Linguistics.
The course covers all aspects of Computational Linguistics in-line with current and leading work in research and industry, and is divided into the following taught modules:
1. Computer programming in Python
The students will be taught the Python computer programming language, which is specially designed for dealing with natural language texts.
2. Corpus Linguistics in R
Corpus Linguistics involves storing large amounts of text on the computer for linguistic analysis. R is a programming language used to study the statistics of language.
3. Machine translation and other natural language processing applications
The automatic translation of text using statistics. The members of the Research Group will each speak on their own research areas throughout the module.
4. Computational Linguistics
The use of computers to study language at all levels, such as relations between words, part of speech tagging, syntactic parsing and anaphora resolution.
5. Translation tools for professional translators
Using computer tools to speed up many aspects of translation, such as product manuals, film scripts, medical texts, video games and simultaneous interpreting.
6. Machine learning for language processing
Computer techniques for automatically classifying natural language texts, for NLP tasks such as making summaries of text automatically.
7. Research methods and professional skills
You will learn how to design an experiment to thoroughly test your research questions.
Translation Tools for Professional Translators is an elective module that may be chosen in the Second Semester to replace another taught module for those students who are interested in pursuing careers in Translation.
You will be expected to dedicate 9 hours per week to lectures and a proportionate amount of time to self-study and tutorials with your supervisor.
- You will be taught by leading researchers in the field: our teaching staff at the Research Institute of Information and Language Processing (RIILP) are engaged in high-quality research, as evidenced by the latest RAE 2008 and REF 2014 results.
- We offer an exciting programme of invited lectures and research seminars, attended by both students and staff;
- The institute has a wide network of contacts in academia and in the industry from which you will be able to benefit.
The knowledge and skills developed in the course will be assessed in a variety of ways. Assessments will include writing assignments on given topics, reports on practical work carried out in the class, portfolios, projects, oral presentations, and tests.
The culmination of the study programme will be your 15,000-word dissertation, which will allow you to carry out an in-depth study of a chosen topic within the areas of corpus linguistics, language teaching, lexicography, or translation.
Graduates of this course will be well-placed to continue their academic/research careers by applying for PhD positions within RIILP or at other leading centres for language and information processing. This degree will also enable graduates to access research and development positions within the language processing and human language technology industries, as well as in related areas such as translation, software development and information and communication technologies, depending on their specific module choices and dissertation topic. It should be noted that computer programming is a skill that is increasingly sought after by many companies from technological backgrounds and skills gained from this course will place graduates in a good position to take up such posts. Past graduates from this course have also gone on to successful careers specifically within the computer programming industry.
The practical sessions include working with tools and software and developing programs based on the material taught in the lectures, allowing you to apply the technical skills you are learning. Some of the tasks are group based, feeding into the collaboration aspect of blended learning which enhances team-working skills, and some are done individually. Through portfolio building, you will be able to share your learning with other students. You will also be able to enhance your employability by sharing your online portfolio with prospective employers. Some assessments will require you to present your work to the rest of the class, enabling you to develop your presentation skills, which are useful in both academia and industry. Other transferrable skills are the abilities to structure your thoughts, present your ideas clearly in writing and prepare texts for a wider audience. You will acquire these skills through assessed report and essay writing, and most of all through writing your dissertation.
Our programme will give you cross-disciplinary skills in a rare combination of areas of expertise, from bioinformatics and evolutionary inference to computational biology and fieldwork.
You will be taught by researchers who apply genomic methods to a wide range of issues in ecology and evolution, from bat food-webs and genome evolution to microbial biodiversity in natural and engineered ecosystems. For example, Professor Steve Rossiter carries out world-leading research on bat genome evolution; Dr Yannick Wurm has discovered a social chromosome in fire-ants; and Dr China Hanson is using genetic methods to study microbial biogeography. This means that teaching on our programme is informed by the latest developments in this field, and your individual research project can be at the forefront of current scientific discovery.
You will conduct your own substantive six-month research project, which may be jointly supervised by contacts from related institutes or within industry. You will also take part in a field course in Borneo - see photos from a recent trip on Flickr - giving you the opportunity to develop first hand experience of theory in action.
By choosing to study at a Russell Group university you will have access to excellent teaching and top class research. You can find out more about our research interests and view recent publications on the School of Biological and Chemical Science's Evolution and Genetics group page.
This MSc programme combines taught modules with individual and collaborative research projects. You will apply the knowledge and techniques from your taught modules in a practical setting and may be able to publish your project findings.
If you have any questions about the content or structure, contact the programme director Dr Christophe Eizaguirre.
To successfully complete this course, you must have a good understanding of mathematics. You may well have studied finance, economics, engineering or maths or physics as an undergraduate. Or you might have a bachelor’s degree in a science subject, in particular computer science.
You should have a general interest in mathematics and statistics, including the more technical and mathematical techniques used in financial markets; but you don’t need to have a background in finance.
You’ll study core modules focusing on asset pricing, risk management and introductions to key financial securities such as equities, fixed income securities and derivatives. From there you’ll progress to specialist learning in econometrics, and cover a large amount of stochastics and numerical methods.
You’ll cover basic and advanced topics in econometrics including ARCH and GARCH models, co-integration and dealing with high frequency data. You will also have the opportunity to work with a number of different estimation techniques, including OLS, Maximum Likelihood and GMM.
You’ll work extensively with the Matlab programming language in the core modules alongside other languages such as VBA, Python or C as optional modules. You’ll choose five from around 40 optional modules in your final term. You can also choose to complete a traditional dissertation, which counts for four optional modules, or a shorter ‘applied research project’, which is the equivalent of two optional modules.
We review all our courses regularly to keep them up-to-date on issues of both theory and practice.
To satisfy the requirements of the degree course students must complete:
Assessment of modules on the MSc in Quantitative Finance, in most cases, is by means of coursework and unseen examination. Coursework may consist of standard essays, individual and group presentations, group reports, classwork, unseen tests and problem sets. Please note that any group work may include an element of peer assessment.
The MSc in Quantitative Finance starts with two compulsory induction weeks, mainly dedicated to:
The job opportunities for students from the three quants masters programmes are very similar. similar. They usually find employment with large investment banks, but also some smaller boutique finance firms, hedge funds or other specialist companies.
Working as a general or technical analysts, risk management position, working on fixed income security desks and the asset management industry including hedge funds are typical jobs which students from the MSc Quantitative Finance go into. Energy companies, such as Npower, have also recruited quants students. Students from the MSc Quantitative Finance will have covered more topics relating to forecasting and regression analysis.
You will also have the skills to study for a PhD in the area of quantitative finance and financial markets.
To successfully complete this course, you must have a very good understanding of mathematics. You may well have studied maths, physics or engineering degrees as an undergraduate.
Or you might have a bachelor’s degree in economics or science and in particular computer science, which, coupled with your interest in stochastics, could also qualify you for this programme.
You should have a general interest in learning the more technical and mathematical techniques used in financial markets; but you don’t need to have a background in finance.
The MSc Financial Mathematics focuses on stochastics and simulation techniques, but also covers some econometrics. You’ll study core modules covering asset pricing, risk management and an introduction to key financial securities such as equities, fixed income and derivatives.
You’ll cover a wide range of elementary and advanced topics in stochastics, including Levy processes and different simulation techniques. You’ll be taught Matlab and VBA and you have the opportunity to learn other programming languages as part of our electives offering, such as Python or C++.
There are three ways to complete the third term. Either you’ll choose five electives from around 40 optional modules in your final term. Or you can choose to complete a traditional dissertation, known as a ‘business research project’, which counts for four electives, or a shorter ‘applied research project’, which is the equivalent of two elective modules.
We review all our courses regularly to keep them up-to-date on issues of both theory and practice.
To satisfy the requirements of the degree course students must complete:
Assessment of modules on the MSc in Financial Mathematics, in most cases, is by means of coursework and unseen examination. Coursework may consist of standard essays, individual and group presentations, group reports, classwork, unseen tests and problem sets. Please note that any group work may include an element of peer assessment.
The Financial Mathematics course starts with two compulsory induction weeks, focused on:
The job opportunities for students from the three quants Masters programmes are very similar and students usually find employment with either large investment banks, or smaller specialist companies or financial boutique firms. Working as a quantitative analysts using stochastic, technical risk management position, pricing fixed income securities and structuring are some of the positions Financial Mathematics students are well qualified for. You will also have the skills to study for a PhD in the area of quantitative finance and financial markets.
The Spatial Data Science and Visualisation MSc teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Students carry out their own research project, supported by academics, researchers and other students in one of the most exciting, interdisciplinary research teams in the field. The programme takes place within The Bartlett, UCL's Faculty of the Built Environment.
The programme consists of four core modules (60 credits), a group mini-project (30 credits), two elective modules (30 credits), and a dissertation (60 credits).
The core modules focus on technical skills, leading to applications in mapping, visualising and analysing spatial data.
Students select two elective modules from a wide range available at UCL, subject to approval.
All students submit a dissertation of 10-12,000 words.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials and practical-based workshops and classes. The interlinked laboratory research-based mini project with data collection focuses on ‘remote data mining’ rather than fieldwork in the traditional planning/geographical/architectural sense. Assessment is through group and individual projects and the dissertation.
Further information on modules and degree structure is available on the department website: Spatial Data Science and Visualisation MSc
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Recent graduates of our related Spatial Data Science and Visualisation MRes have gone on to work as developers, in spatial analysis, and a number have continued to PhDs. Through our PhD partners, Knowledge Transfer Partnerships and substantial outreach, graduates will be able to take advantage of CASA's links to the world outside academia.
The Spatial Data Science and Visualisation MSc provides a unique skill set in computation mapping, visualisation and spatial research. Research-led skills are increasingly a key element in our understanding of complex spatial functions, particularly as vast amounts of previously unused data are becoming available either from changes in accessibility regulation or more widely as a result of new mass data collection methodologies.
The Centre for Advanced Spatial Analysis (CASA) is a research centre specialising in computational and mathematical approaches, with cutting-edge research in GIS, urban simulation, mapping, data visualisation, and 3D environments in cities and space.
Students on this programme will be exposed to a range of programming languages (Java/Processing, R, Python and MySQL), 3D visualisation packages, and be given a substantive grounding in GIS, programming structure, mathematical methods and data design.
The combination of skills involved in this programme is unique – graduates will be able to lead institutions and companies in new directions and be involved in changing cultures across the sector.
This course is a conversion course that will give you a grounding in computer programming. There's a particular focus on in-demand mobile and web applications, software engineering, and computer security. You'll learn through classes and extensive lab based work. You'll undertake a practical software development dissertation project. All classes within the course are tailored to the needs of students from non-computing backgrounds. They'll equip you with the key skills you'll need to begin a successful career as a software developer.
This course will prepare you for a professional and rewarding career in software development not only within the IT sector, but within many other sectors including education, engineering, health, finance, government, manufacturing, retail, and transport.
You'll also develop other valuable transferable skills including effective presentation, team working, and report writing, which will further enhance your skills as a future leader.
The Department of Computer & Information Sciences is based on levels 11 to 14 of the Livingstone Tower. We have a large quiet study zone and a large social zone for working on group activities.
There are three large computer laboratories within the department, each containing state-of-the-art equipment with over 175 dual boot Windows and Linux machines available. Each lab machine is equipped with up-to-date software.
All departmental machines are linked by a high-speed local area network and operate under a single network file system so you can access your files from any of our machines. High-speed wireless access is also available throughout the department.
The University has a large modern library which contains all of the materials that you need for your course. Many of the books are also available online electronically meaning they are available to all students at all times.
Software Development graduates are highly employable and can look forward to well-paid professional careers. You could end up designing and building the digital technologies that underpin the global economy and, indeed, every aspect of human activity from recreation through healthcare to business and the natural environment.
Example roles include:
Strathclyde University provides a range of professional development services, including career support services introduced during induction and offered throughout the period of study and even after graduation, to help our students achieve their career ambitions.
The internet and advances in digitalisation and social networking are transforming how companies interact with customers and partners. The specialization in Digital Marketing and Data Science provides participants with the strategic and analytical skills to successfully guide companies’ strategies in a digital world that is overflowing with data on customers, products and interactions.
The MSc in Digital Marketing & Data Science is designed to grow a new generation of leading marketing specialists – digital savvy professionals that can benefit from an explosive growth of online technologies to develop business.
The program pedagogy uniquely combines a strong academic background in business studies, marketing, data analysis and strategy with an in‐depth and specific digital knowledge in online video, mobile, viral, social media, and data driven marketing. As a student, you will also have the possibility to learn the latest innovations from major players like Google, Facebook, Amazon, Twitter, Netflix, and other greatest technological companies or discover the benefits and challenges faced by the main companies.
This program focuses on digital marketing and data analytics, which includes business analytics (with advanced Excel and Tableau Software), coding (R and Python), database access (SQL), data science and machine learning with Python. You will be able to manage the coming technological and algorithmic disruptions in marketing, instead of being made redundant by them.
During the first semester the focus is on the fundamentals of digital marketing and about becoming proficient in data analytics. The second semester covers strategy, value intensive processes in digital marketing and data analytics and the third semester opens specializations and training in a professional and international context.
Upon completing the MSc in Digital Marketing & Data Science, you will become accomplished digital marketing professionals, able to manage and innovate in a data‐rich business environment. You will be well‐prepared to work in a sales or marketing department of startups and major brands in business to consumer or business to business environments. You will also fit the requirements of advertising agencies looking for marketing professionals knowledgeable about multichannel communication, consulting firms managing the digital transformation of their clients, as well as digital media and technology companies looking for managers with a strong business background who are also familiar with their trade.
The skills developed with the program open large horizons as all companies are now challenged by data and digital disruptions.
The MSc in Digital Marketing & Data Science will prepare you for specific positions and job titles related to the program’s fields, with target positions in marketing and business updated to the digital era such as :
Selecting the right student is about more than just test scores. At emlyon business school we take an applicant's entire potential into account. Elements like motivation to pursue the MSc in Digital Marketing & Data Science, your background and career aspirations weigh just as heavily in our selection procedure.
If you would like to have the contact details of the right person to help you with any questions regarding the program or the selection procedure, you can create your personal space and obtain full contact details on your emlyon business school dashboard.
The first step of the admission process is your online application, which you can access through your personal space(programme dashboard).
Should your application be complete, you will receive the admission board’s final decision within 15 working days. Please send back your enrolment form within 10 working days.
The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.
Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.
This MSc programme delivers:
You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.
There are two streams: the Financial stream and the Computational stream.
Compulsory courses previously offered include (both streams):
Additional compulsory courses for Computational Stream previously offered include:
Additional compulsory courses for Financial stream previously offered include:
Optional courses previously offered include:
We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.
At the end of this programme you will have:
Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.
One year enterprise-led funded Masters by Research, Ref. No. 80
· Get paid £15,000 tax-free
· Have your tuition fees reduced. Your partner company pays £2,000 towards your fees, meaning UK/EU students pay £2,260, and international students pay £15,945.
· Be part of the multi award winning Centre for Global Eco-Innovation with a cohort of 50 talented graduates working on exciting business-led R&D.
· Finish in a strong position to enter a competitive job market in the UK and overseas.
This project aims to develop a cutting edge video analytics system that will be capable of detecting birds landing within the field of view of a CCTV camera. The system will identify the types and number of birds that have landed using deep learning, neural network techniques and provide a trigger to notify operators that birds have landed in the vicinity of the cameras. The information along with a snapshot of the birds will be displayed on a user interface that is web-based and can be accessed via a device functioning on the Android operating system.
Applicants should have, or expect to achieve, at least a 2:1 degree (or equivalent overseas qualification) in related Computer Science, Signal Processing, Engineering or Mathematics degree with strong programming skills (e.g. C++, Python, Matlab). Previous experience in computer vision, image processing, machine/deep learning would be advantageous.
Enterprise and collaborative partners
To apply for this opportunity please email [email protected] with:
· A CV (2 pages maximum)
This project is part funded by the European Regional Development Fund and is subject to confirmation of funding. For further information about the Centre for Global Eco-Innovation, please see our website.
Deadline: Midnight Sunday 15th July 2018
Start: October 2018
The benefits of GIS are increasingly recognised within government, business, education and the voluntary sector, and the applications of geospatial data technologies are steadily growing. Using GIS, it is possible to combine data from a broad range of sources and in a variety of formats, such as paper and digital maps, routinely collected administrative data, censuses and population surveys, satellite imagery, aerial photography, GPS tracking and surveys, LiDAR and crowd-sourcing. The uses of GIS are very diverse, and include mapping, spatial analysis, planning and decision-making within a wide range of disciplines and sectors – common examples include environmental management and conservation, resource management, emergency service planning and humanitarian assistance, health care provision, land use planning and urban development, the utilities, transport, geo-demographics, mineral extraction and retail analysis. Increasing uptake of GIS and associated techniques and technologies means that there is a growing demand for qualified personnel who have the skills to manage spatial data effectively. Strong industry links help ensure that our course is relevant to the needs of employers.
The course is designed to help people gain understanding and experience of GIS concepts, functionality and applications. Content focuses on the representation, acquisition, management, manipulation and analysis of spatial data. It also includes modules on remote sensing, spatial databases, web-GIS and GIS in the commercial environment. Additional optional modules include GIS work experience, spatial analysis and modelling, GIS for environmental management, and Customising GIS.
After successfully completing the PgDip modules, you may transfer to the Masters part of the programme. This requires the completion of a substantial independent research project, written in the form of a research journal article (which may, with agreement of your supervisor, be submitted for publication).
As part of the course resources, you will be provided with a free copy of ArcGIS, the remote sensing package ERDAS Imagine, and the data analysis package SPSS.
Gaining experience in the workplace and being able to apply academic learning within that context is very beneficial for students preparing to enter the workplace, so we offer the option of undertaking a GIS Work Experience module to full-time students. This entails working within an organisation for 2.5 days per week over a six-week period. Placements (which are unpaid) may be in the public sector, private companies, charities or education. Students who take this module find it extremely helpful for both their professional and personal development and refer particularly to benefits such as broadening their technical skills, gaining experience of team-working and of independent problem-solving, improved confidence and of learning about the geospatial industry and employment through exposure to real-world applications of GIS.
Part-time students who are in GI-related employment may opt to undertake the GIS Workplace Project.
GIS and geospatial technologies underpin a rapidly growing, multi-billion dollar industry, and are becoming increasingly mainstream within both the public and private sectors, resulting in a need for graduates who have a combination of theoretical knowledge and practical skills.
Graduates of this course have secured employment in a variety of GIS-related roles worldwide, in GIS positions including technicians, analysts, scientists, surveyors, data specialists, mapping officers, consultants, project managers, development, sales and marketing, customer support, GIS training, lecturing and research (including funded PhD projects). The breadth of potential uses of GIS ensures a great diversity of job opportunities; for example, our graduates have found employment with mapping agencies, GIS and SatNav companies, environmental consultancies, ecological and marine resource management and environmental agencies, renewable energy companies, forestry, fisheries, town planning departments, heritage agencies, health and emergency services, housing authorities, local government, aid agencies, countryside recreation, rural development, retail analysis, utilities and infrastructure, Further and Higher Education, mining and mineral exploitation and the oil industry, among others. Knowledge and understanding of geo-spatial data is also increasingly required in a variety of jobs outside of the GI profession, making a GIS qualification a valuable asset enhancing employability in a range of fields.