• Xi’an Jiaotong-Liverpool University Featured Masters Courses
  • Birmingham City University Featured Masters Courses
  • University of Bristol Featured Masters Courses
  • Northumbria University Featured Masters Courses
  • University of Northampton Featured Masters Courses
  • University of Surrey Featured Masters Courses
  • Cardiff University Featured Masters Courses
University of Nottingham in China Featured Masters Courses
Birmingham City University Featured Masters Courses
Buckinghamshire New University Featured Masters Courses
University of Warwick Featured Masters Courses
Ulster University Featured Masters Courses
"python"×
0 miles

Masters Degrees (Python)

We have 93 Masters Degrees (Python)

  • "python" ×
  • clear all
Showing 1 to 15 of 93
Order by 
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. Read more

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!

What happens on the course?

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.

Opportunities:

- 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.

Career path

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.

What skills will you gain?

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.



Read less
Ecologists and evolutionary biologists now routinely use next-generation DNA sequencing in their research, and graduates who are skilled in both genome analysis as well as ecology and evolution are rare. Read more

Ecologists and evolutionary biologists now routinely use next-generation DNA sequencing in their research, and graduates who are skilled in both genome analysis as well as ecology and evolution are rare. Genome-enabled approaches are helping rapidly to advance our understanding of the dynamic relationship between genotype, phenotype and the environment.

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.

Programme highlights

  • Work with leading researchers in environmental genomics - learn more on the Evolution and Genetics research group page 
  • Two-week tropical ecology field trip (currently to Borneo)
  • Strong foundation for careers in consultancy, environmental policy and management or research
  • Strong foundation for PhD training in any area of genomics, ecology or evolution

Research and teaching

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.

Structure

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.

Taught modules

  • Genome Bioinformatics: Covers the essential aspects of next generation sequence (NGS) analysis, including genome assembly, variant calling and transcriptomics. Also covers essential computer skills needed for bioinformatics, such as Linux and using our high performance computing cluster.
  • Coding for scientists: Assuming no prior programming knowledge, teaches you how to program in Python, using biological examples throughout. Python is one of the most popular languages in the bioinformatics community, and understanding Python provides the perfect foundation for learning other languages such as Perl, Ruby and Java.
  • Statistics and bioinformatics: Covers core statistics methods, within the R statistical computing environment. R has become the de facto environment for downstream data analysis and visualisation in biology, thanks to the hundreds of freely available R packages that allow biological data analysis solutions to be created quickly and reliably.
  • Post-genomics bioinformatics: Introduces techniques that have developed as a consequence of developments in genomics (i.e. transcriptomics, proteomics, metabolomics, structural biology and systems biology) with particular emphasis on the data analysis aspects. Practicals cover the popular Galaxy framework, advanced R, and machine learning.
  • Research frontiers in evolutionary biology: Exploring the frontiers of research in evolutionary biology. Topics covered will include: incongruence in phylogenetic trees, neutral versus selective forces in evolution, the origin of angiosperms, the origin of new genes, the evolution of sociality, the significance of whole genome duplication and hybridisation. Current methods being used to tackle these areas will be taught, with an emphasis on DNA sequence analysis and bioinformatics.

Research modules

  • Evolutionary/Ecological Analysis/Software Group Project module: Students are organised into small teams (3-4 members per team). Each team is given the same genomic or transcriptomic data set that must be analysed by the end of the module. Each team must design an appropriate analysis pipeline, with specific tasks assigned to individual team members. This module serves as a simulation of a real data analysis environment, providing invaluable experience for future employability.
  • Individual Research Project (50 per cent of the programme)


Read less
This MSc Computing offers students from diverse career and subject areas a balance of software engineering skills and technical abilities required for a career in Software Development. Read more
This MSc Computing offers students from diverse career and subject areas a balance of software engineering skills and technical abilities required for a career in Software Development.

Through this one-year programme you will get a first-hand understanding of the vital problem-solving role of software, the interdisciplinary opportunities available, and what computational systems can achieve.

Through a gentle introduction and intensive support, you will be introduced to programming skills using important languages such as Java and Python. Emphasis is placed on handling data and you will develop essential skills in SQL (Structured Query Language) for advanced database functionality using industry standard products such as Oracle™.

A choice of taught optional modules allows you to further develop skills in areas of your choice.

Graduates from these programmes will be ideally placed for employment in the computing industry or for careers requiring a combination of their graduate discipline with computing expertise.

Distinctive features:

• An opportunity to take a conversion course which is also an accredited course recognised by BCS, the Chartered Institute for IT.

• The opportunity to complement the discipline in which you graduated with the discipline of Computing.

• The facility to tailor the course to your interests by the selection of advanced option modules.

• Flexible choice of project topic, for example: associated with the research activity of the School fulfilling a business need reflecting your own interest.

Structure

You will study core modules to a total of 80 credits, with two optional modules worth a total of 40 credits. Students will also undertake an individual project and dissertation (worth 60 credits).

This course is a full-time programme undertaken over one calendar year. It is also available as a part-time programme over three years, and with placement.

Core modules:

Information Processing in Python
Web Application Development
Object-Oriented Development with Java
Software Engineering
Dissertation

Optional modules:

Computational Systems
Computer Science Topic 1: Web and Social Computing
Distributed and Cloud Computing
Human Centric Computing
Information Modelling & Database Systems
Visual Communication and Information Design
E-Commerce and Innovation

Teaching

The School of Computer Science and Informatics has a strong and active research culture which informs and directs our teaching. We are committed to providing teaching of the highest standard and received an excellent report in the most recent Quality Assurance Agency (QAA) review.

A diverse range of teaching and learning styles are used throughout the MSc in Computing and the MSc in Computing with Placement. Students will attend lectures, participate in seminars, workshops and tutorials, and carry out practical and laboratory work.

Students obtain support materials either via Learning Central (Cardiff University’s Virtual Learning Environment) or from study packs specially developed for selected modules.

Students will also undertake a project and independent study to enable them to complete their dissertation. Dissertation topics may be suggested by the student or chosen from a list of options proposed by academic staff reflecting their current interest.

Support

As a School, we pride ourselves on providing a supportive environment in which we are able to help and encourage our students.

All students are allocated a personal tutor who will monitor your progress throughout your time at university and will support you in your Personal Development Planning. You will see your Personal Tutor at least once each semester.

Outside of scheduled tutor sessions, our Senior Personal Tutor runs an open door policy, being on hand to advise and respond to any personal matters as they arise.

The School has a formal student-staff panel to discuss topics or issues of mutual interest, in addition we schedule fortnightly informal gatherings over coffee for all students and staff associated with MSc Programmes.

Feedback:

Feedback on coursework may be provided via written comments on work submitted, by provision of ‘model’ answers and/or through discussion in contact sessions.

Assessment

The taught modules within the programmes are assessed through examinations and a wide range of in-course assessments, such as written reports, extended essays, practical assignments and oral presentations.

The individual project and dissertation will enable students to demonstrate their ability to build upon and exploit knowledge and skills gained to exhibit critical and original thinking based on a period of independent study and learning.

Career prospects

Recent graduates have gained employment in roles such as software developers, systems analysts, business analysts, IT consultants, and support engineers.

MSc Computing graduates are employed by organisations of all sizes locally, nationally, and internationally. For example, recent graduates have taken up positions with local NHS Trusts, Logica, Sun Microsystems, BT, and the National Library of Medicine in the USA, as well as undertaking further doctoral study.

Read less
Who is it for?. 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. Read more

Who is it for?

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.

Objectives

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.

Structure

  • You will have gained a good understanding of the technical aspects used in financial markets, starting from the financial theory, looking at different financial instruments and showing various applications of the theoretical concepts.
  • You will gain a good understanding of stochastics, mathematical finance and econometrics as well as some programming.
  • You will also obtain a very good understanding of different financial assets, in particular derivatives, and how they can be used in different context, such as risk management, asset management or structuring
  • You will have three different possibilities to complete your degree in the third term, including writing a dissertation or an applied project. You can also opt to gain all the credits through taught electives.
  • Popular electives include Behavioural Finance, Trading and Hedging in the FOREX Market, Technical Analysis, Hedge Funds or Python.

Assessment

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:

  • nine core courses (Eight at 15 credits each, one at 10 credits)

and either

  • five electives (10 credits each)
  • three electives (10 credits each) and an Applied Research Project (20 credits)
  • one elective (10 credits) and a Business Research Project (40 credits)

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.

Induction weeks

The MSc in Quantitative Finance starts with two compulsory induction weeks, mainly dedicated to:

  • an introduction to careers in finance and the opportunity to speak to representatives from over 75 companies during a number of different industry specific fairs.
  • a refresher course of advanced financial mathematics, statistics, computing and electronic databases

Career pathways

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.



Read less
Who is it for?. 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. Read more

Who is it for?

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.

Objectives

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.

Structure

  • You will have gained a very good understanding of the technical aspects used in financial markets, including wide ranging financial theory and different financial assets.
  • You will gain a good understanding of stochastic and mathematical finance and gained some knowledge of econometrics and forecasting. You will also have obtained a good understanding of programming, in particular Matlab.
  • From the MSc Financial Mathematics you will also understand how the theory is being applied in the financial industry and what practical issues are.
  • In the third term you have three different options how you can complete your MSc, including a project or choosing only electives. Popular electives include Modelling and Data Analysis, Advanced Financial Engineering and Credit Derivatives, Credit Risk Management, Quantitative Risk Management. Introduction to Python.

Assessment

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:

  • nine core courses (Eight at 15 credits each, one at 10 credits)

and either

  • five electives (10 credits each)
  • three electives (10 credits each) and an Applied Research Project (20 credits)
  • one elective (10 credits) and a Business Research Project (40 credits)

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.

Two Induction Weeks

The Financial Mathematics course starts with two compulsory induction weeks, focused on:

  • an introduction to careers in finance and the opportunity to speak to representatives from over 75 companies during a number of different industry specific fairs.
  • a reminder course of advanced financial mathematics, statistics and basic computing which forms a prerequisite of the core modules in term 1.

Career pathways

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.



Read less
The GIS (Geographical Information Science) MSc provides an education in the theoretical, scientific and practical aspects of GIS. Read more
The GIS (Geographical Information Science) MSc provides an education in the theoretical, scientific and practical aspects of GIS. It prepares students for technical and analytical GIS roles and is in high demand; we have very close links with industry and the majority of our students find employment prior to contemplating their degree.

Degree information

Students gain a solid grounding in the scientific principles underpinning the computational and analytical foundations of GISc. Our staff are world-leading experts in the areas of programming location-enabled Apps, spatial and 3D databases, big spatio-temporal analytics, citizen science and and human computer interaction, and the MSc therefore is able to offer a wide range of options and specialisations.

Students undertake modules to the value of 180 credits. The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits). A Postgraduate Diploma, four core modules (60 credits), four optional modules (60 credits), full-time nine months is offered.

Core modules - core modules introduce the theory underpinning GIS, along with programming skills (python) and the basics of spatial analysis and statistcs. You'll learn to critically engage with GIS rather than just pushing buttons - how does the way data is captured and modelled influence the results of your analysis? Do you get the same results from two different GIS packages? Knowing what is inside the 'black box' means you understand analytical results and their limitations.
-GIS Principles and Technology
-Principles of Spatial Analysis
-Mapping Science
-Representations, Structures and Algorithms

Optional modules - term two is where you start to specialise, chosing modules that fit your interests, intended career choice and/or prepare you for your dissertation. At this point you can chose a heavilty technical route (e.g. databases, programming, human computer interaction) a more analytical route (spatio-temporal data mining, network and locational analysis, databases) or a mixture of the two routes. You will need to chose four modules in total. At least 30 credits of optional modules selected from :
-Geographical Information System Design
-Spatio-Temporal Analysis and Data Mining
-Web and Mobile GIS – Apps and Programming
-Spatial Databases and Data Management

Plus no more than 30 credits of optional modules (all term two) selected from :
-Airborne Data Acquisition
-Applied Building Information Modelling
-Network and Locational Analysis
-Image Understanding
-Ocean and Coastal Zone Management
-Positioning
-Research Methods
-Terrestrial Data Acquisition

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000–15,000 words. Where appropriate, this may be undertaken in conjunction with one of our many industrial partners, including Arup, Joint Research Centre, British Red Cross, Transport for London.

Teaching and learning
The programme is delivered through lectures, practical classes, demonstrations and tutorials, and is supported by a series of external speakers from industry and visits to industrial who give weekly seminars describing how GIS is used in their field as well as what they are looking for when recruiting graduate GIS students. Assessment is through unseen examinations, group and individual coursework, formal and oral presentations, and the dissertation.

Careers

There are excellent employment prospects for our graduates, with starting salaries of around £25,000. Recent GIS graduates have found openings with large engineering design firms (such as Arup or WSP), specialist consultancy firms such as Deloitte or Informed Solutions, in leading professional software companies (such as ESRI or Google), with local authorities, for organisations such as Shell, Tesco, the Environment Agency, Transport for London, NHS and the Ordnance Survey.

Employability
Students will develop specific skills including a fundamental understanding of GIS and its application to real-world problems, through theoretical lectures covering the foundations of the science – how data is captured, map creation, generalisation, spatial data management, spatial analysis, data quality and error, and spatial algorithms. Students will develop strong technical (python, R, Java, HTML, Javascript, SQL) and analytical skills (data mining, human computer interaction and usability), and in order to fully understand the principles behind GIS will make use of multiple GIS packages, both proprietary and free/open source (ArcGIS, QGIS).

Why study this degree at UCL?

This highly regarded MSc has been running for nearly 30 years and is taught by internationally recognised academics. Our specialist GIS laboratory offers the latest open source and proprietary software and our unique dual focus on the computer science and analytical aspects of GIS means that you will be able to develop your skills in multiple directions.

Our close links with industry (a strong alumni group and weekly industrial seminars) mean that you will be able to directly link your classroom learning with your future career as a GIS professional; you can also undertake your dissertation with an industrial partner.

As well as weekly industrial seminars, you will have the option to do an industry-linked project, and you will be able to attend our annual GIS careers event, which is co-organized with the UK Assocation of Geographic Infrormation.

Read less
This MSc Computing offers students from diverse career and subject areas a balance of software engineering skills and technical abilities required for a career in Software Development. Read more
This MSc Computing offers students from diverse career and subject areas a balance of software engineering skills and technical abilities required for a career in Software Development.

Through this two-year programme you will get a first-hand understanding of the vital problem-solving role of software, the interdisciplinary opportunities available, and what computational systems can achieve.

Through a gentle introduction and intensive support, you will be introduced to programming skills using important languages such as Java and Python. Emphasis is placed on handling data and you will develop essential skills in SQL (Structured Query Language) for advanced database functionality using industry standard products such as Oracle™.

A choice of taught optional modules allows you to further develop skills in areas of your choice.

Students may choose to apply for a paid 7-12 month professional work placement to be undertaken on completion of Spring semester and before completing the MSc course with a 60-credit dissertation. This provides valuable work experience to develop your IT Professional skills.

Graduates from these programmes will be ideally placed for employment in the computing industry or for careers requiring a combination of their graduate discipline with computing expertise.

Distinctive features

• A conversion course as well as an accredited course recognised by BCS, the Chartered Institute for IT.

• The opportunity to complement the discipline in which you graduated with the discipline of Computing.

• The facility to tailor the course to your interests by the selection of advanced option modules.

• Flexible choice of project topic, for example: associated with the research activity of the School; fulfilling a business need; reflecting your own interest.

• 7-12 month experience as an IT Professional for students who successfully find a suitable placement.

Structure

You will undertake a placement following the taught stage of the course and prior to undertaking your individual project and dissertation. Most students start their placement in the summer of Year 1. The breakdown is as follows:

Year 1: 80 credits core modules, 40 credit optional modules.

Year 2: 120 credits placement, dissertation.
This is a full-time course undertaken over two calendar years. It is also available as a full-time course over one year or a part-time course over three years, both without placement.

Year ONE core modules:

Information Processing in Python
Web Application Development
Object-Oriented Development with Java
Software Engineering

Year ONE optional modules:

Computational Systems
Computer Science Topic 1: Web and Social Computing
Distributed and Cloud Computing
Human Centric Computing
Information Modelling & Database Systems
Visual Communication and Information Design
E-Commerce and Innovation

Year TWO core modules:

Placement
Dissertation

Teaching

The School of Computer Science and Informatics has a strong and active research culture which informs and directs our teaching. We are committed to providing teaching of the highest standard and received an excellent report in the most recent Quality Assurance Agency (QAA) review.

A diverse range of teaching and learning styles are used throughout the MSc in Computing and the MSc in Computing with Placement. Students will attend lectures, participate in seminars, workshops and tutorials, and carry out practical and laboratory work.

Students obtain support materials either via Learning Central (Cardiff University’s Virtual Learning Environment) or from study packs specially developed for selected modules.

You will also undertake a project and independent study to enable you to complete a dissertation. Dissertation topics may be suggested by you or chosen from a list of options proposed by academic staff reflecting their current interest.

Support

As a School, we pride ourselves on providing a supportive environment in which we are able to help and encourage our students.

All students are allocated a personal tutor who will monitor your progress throughout your time at university and will support you in your Personal Development Planning. You will see your Personal Tutor at least once each semester.

Outside of scheduled tutor sessions, our Senior Personal Tutor runs an open door policy, being on hand to advise and respond to any personal matters as they arise.

The School has a formal student-staff panel to discuss topics or issues of mutual interest, in addition we schedule fortnightly informal gatherings over coffee for all students and staff associated with MSc Programmes.

Students are responsible for obtaining their placement. The School actively assists students on “with Placement” courses in finding a suitable placement.

Feedback:

Feedback on coursework may be provided via written comments on work submitted, by provision of ‘model’ answers and/or through discussion in contact sessions.

Assessment

The taught modules within the programmes are assessed through examinations and a wide range of in-course assessments, such as written reports, extended essays, practical assignments and oral presentations.

The placement is assessed through a reflective report that demonstrates that the student has developed skills as an IT Professional.

The individual project and dissertation will enable you to demonstrate your ability to build upon and exploit knowledge and skills gained to exhibit critical and original thinking based on a period of independent study and learning.

Career prospects

Recent graduates have gained employment in roles such as software developers, systems analysts, business analysts, IT consultants, and support engineers.

MSc Computing graduates are employed by organisations of all sizes locally, nationally, and internationally. For example, recent graduates have taken up positions with local NHS Trusts, Logica, Sun Microsystems, BT, and the National Library of Medicine in the USA, as well as undertaking further doctoral study.

Read less
The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. Read more
The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop skills in database management, programming, summarisation, modelling and interpretation of data. The programme provides graduates with an opportunity, through development of a research project, to investigate the more applied elements of the disciplines.

Visit the website: http://www.ucc.ie/en/ckr49/

Course Details

The MSc in Data Science and Analytics is a significant collaboration between the Departments of Computer Science and Statistics; designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever increasing and complex data. The programme emphasises the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making.

Format

A typical 5 credit module:
• 2 lecture hours per week
• 1–2 hours of practicals per week
• Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.

Structure

Students must attain 90 credits through a combination of:

- Core Modules (30 credits)
- Elective Modules (30 credits)
- Dissertation (30 credits)

Part 1 (60 credits)

- Core Modules (30 credits) -

CS6405 Data Mining (5 credits) - Dr. Marc Van Dongen
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (5 credits)

- Database Modules -

Students who have adequate database experience take:

CS6408 Database Technology (5 credits) - Mr. Humphrey Sorensen
CS6409 Information Storage and Retrieval (5 credits) - Mr. Humphrey Sorensen

- Students who have not studied databases take:

CS6503 Introduction to Relational Databases (5 credits)
CS6505 Database Design and Administration (5 credits)

Elective Modules (30 credits)

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

CS6322 Optimisation (5 credits) - Dr. Steve Prestwich
CS6323 Analysis of Networks and Complex Systems (5 credits) - Prof. Gregory Provan
CS6509 Internet Computing for Data Science (5 credits)
ST6032 Stochastic Modelling Techniques (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)

- Programming Modules -

Students who have adequate programming experience take:

CS6406 Large-Scale Application Development and Integration l (5 credits) - Professor Gregory Provan
CS4607 Large-Scale Application Development and Integration ll (5 credits) - Professor Gregory Provan

- Students who have not studied programming take:

CS6506 Programming in Python (5 credits)
CS6507 Programme in Python with Data Science and Applications (5 credits) - Dr. Kieran Herley

Part 2 (30 credits)

Students select one of the following modules:

CS6500 Dissertation in Data Analytics (30 credits)
ST6090 Dissertation in Data Analytics (30 credits)

Assessment

Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2015 Book and for each module in the Book of Modules 2015/2016 - http://www.ucc.ie/modules/

Postgraduate Diploma in Data Science and Analytics

Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.

Careers

This programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting Computer Science graduates in 2014-15 include:

Accenture, Aer Lingus, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Cisco, CiTi-Technology, Cloudreach, Dell, Digital Turbine Asia Pacific, EMC, Enterprise Ireland, Ericsson, First Derivatives, Guidewire, IBM, Intel, Open Text, Paddy Power, Pilz, PWC, SAP Galway Transverse Technologies, Trend Micro, Uniwink, Version 1 (Software).

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

Read less
Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Read more
Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Study on a course which has been developed with direct input from industry experts who will bring real life business case scenarios to you.

More about this course

This specialist advanced course will equip students with the theoretical, technical and practical data analytics competencies required in an area of economic growth. The course curriculum content has been developed with direct input from industry experts and utilises specialist software tools and techniques. Students’ experience of the course will be enriched with exposure to real life business case scenarios brought to them by skilled professionals in industry.

The specialist nature of the course will allow students to explore and experience advanced techniques in data science. Students will acquire practical skills, often first-hand from an external practitioners, preparing them for employment as data analysts. Students will also be trained in the use of software tools and environments currently used by the industry sector. For example, students on this course will have exposure to R and Python programming, IBM SPSS, SAS®, Tableau, Oracle and Hadoop.

A range of assessment methods are used on the course, including written reports, practical and research assignments, demonstrations, presentations, group work and examinations.

Modular structure

The modules listed below are for the academic year 2016/17 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

Year 1 modules include:
-Data Analysis and Visualization (core, 20 credits)
-Data Mining for Business Intelligence (core, 20 credits)
-Data Modelling and OLAP Techniques for Data Analytics (core, 20 credits)
-MSc Project (core, 60 credits)
-Programming for Data Analytics (core, 20 credits)
-Statistical Modelling and Forecasting (core, 20 credits)
-Financial Mathematics (option, 20 credits)
-Work Related Learning (option, 20 credits)

After the course

On completion of the course graduates will be well equipped to work in some of the fastest growing sectors of the data science and big data industries. The course offers wide-ranging career opportunities in the commercial industry, public and financial services, especially in areas requiring big data analysis such as consumer, healthcare, scientific, financial, security intelligence, business and social sciences.

Job roles include data scientist, data analyst, digital analyst, big data consultant, statistical analyst and data modeller. Graduates will be eligible to work as data analysts or data scientists in a multitude of areas where skills such as R or Python programming, machine learning and statistical modelling, SAS® and SPSS experience, data visualisation and data-driven decision-making are required.

The course also provides an excellent basis for further study for those wishing to pursue a higher-level research degree or embark on an industry-based research career.

Read less
The Spatial Data Science and Visualisation MSc teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Read more

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.

About this degree

Students gain a grounding in the principles and skills of spatial research, data analysis and visualisation, agent-based models and virtual environments, and develop an understanding of research methodology for data collection and analysis. Subject-specific modules provide students with the opportunity to develop skills in spatial analysis and to contribute to current debates in the field. They will learn programming skills in Java/Processing, Python, R, JavaScript and SQL, and the ability to use a range of interactive geospatial and visualisation tools (ArcGIS, Unity, Mapbox and CityEngine).

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).

Core modules

The core modules focus on technical skills, leading to applications in mapping, visualising and analysing spatial data.

  • Data Science for Spatial Systems
  • Geographic Information Systems and Science
  • Introduction to Programming
  • Quantitative Methods
  • Group Mini Project: Digital Visualisation

Elective modules

Students select two elective modules from a wide range available at UCL, subject to approval.

Dissertation/report

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

Funding

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

Careers

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.

Employability

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.

Why study this degree at UCL?

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.



Read less
Why this course?. 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. Read more

Why this course?

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.

What you’ll study

  • programming in Python, JavaScript and Java, three of the most popular programming languages. For example, Spotify, Twitter and Open Office are built using these languages.
  • mobile app development using HTML5/JavaScript providing you with the skills to develop graphical user interfaces for mobile applications.
  • database design & development using Oracle SQL providing you with the fundamental skills that underpin the global explosion and demand for Big Data.
  • state-of-the-art software engineering methodologies providing you with professional software development skills, including widely use rapid development Agile methods.
  • cyber security tools and technologies including experience of the tools and techniques used in security exploits. 

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.

Facilities

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.

Careers

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:

  • Software Developer (Java/Javascript/Python): As software developer you'll be playing a key role in the design, installation, testing and maintenance of software systems. Your programs will be the key driver for the success of a business and will enhance research.
  • Software Engineer (Agile/Scrum): As software engineer you'll apply engineering principles to the creation of software. You will oversee the design process and connect the client’s needs with applicable technology solutions.
  • Web Developer (Javascript/HTML5): Similar to the software developer role, but with a focus for web sites and services developed using Web-specific languages such as Javascript, HTML5 or PHP.
  • Database Developer (SQL/Oracle): You will develop databases that satisfy the information needs of your organization and which underpin Big Data. This will support decision-making within a business and knowledge-discovery in research.
  • Business Analyst: as a business analyst you will identify improvements that can be made to organisational systems, write specifications for their modification and enhancement, and be involved in the design of new IT solutions to improve business efficiency.

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.



Read less
The internet and advances in digitalisation and social networking are transforming how companies interact with customers and partners. . Read more

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.

Careers

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 :

  • Digital marketing manager
  • Marketing product manager
  • Head of digital solutions
  • Digital project manager
  • Data analyst
  • Business intelligence analyst
  • Digital analyst
  • Consultant in digital strategy
  • Consultant or project manager in digital transformation
  • Social media manager
  • Online media planner
  • Web project leader

Admissions

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.

Admission Process

The first step of the admission process is your online application, which you can access through your personal space(programme dashboard).

  • Selection sessions for the 2018 intake run from November 2017 to July 2018.

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.



Read less
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. Read more

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:

  • a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
  • a solid knowledge in financial derivative pricing, risk management and portfolio management
  • the transferable computational skills required by the modern quantitative finance world

Programme structure

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):

  • Stochastic Analysis in Finance (20 credits, semester 1)
  • Discrete-Time Finance (10 credits, semester 1)
  • Finance, Risk and Uncertainty (10 credits, semester 1)
  • Object-Oriented Programming with Applications (10 credits, semester 1)
  • Risk-Neutral Asset Pricing (10 credits, semester 2)
  • Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
  • Monte Carlo Methods (5 credits, semester 2)
  • Numerical Methods for Stochastic Differential Equations (5 credits, semester 2)
  • Research-Linked Topics (10 credits, semesters 1 and 2)

Additional compulsory courses for Computational Stream previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)

Additional compulsory courses for Financial stream previously offered include:

  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)

Optional courses previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)
  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)
  • Integer and Combinatorial Optimization (10 credits, semester 2)
  • Bayesian Theory (10 credits, semester 1)
  • Credit Scoring (10 credits, semester 2)
  • Python Programming (10 credits, semester 1)
  • Scientific Computing (10 credits, semester 1)
  • Programming Skills - HPC MSc (10 credits, semester 1)
  • Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)
  • Applied Databases (10 credits)

Work placements/internships

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.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.



Read less
Get paid to do a Masters with the. Centre for Global Eco-Innovation. at. Lancaster University. , The Sunday Times University of the Year 2018, and. Read more

Get paid to do a Masters with the Centre for Global Eco-Innovation at Lancaster University, The Sunday Times University of the Year 2018, and Remvox Ltd.

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.

·        The Centre is based at Lancaster University, so you will gain your Masters from a Top Ten University, recognised as The Sunday Times University of the Year 2018.

·        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

This Masters by Research is a collaborative research project between Lancaster University with supervision by Dr David Cheneler, Dr Jungong Han and Steve Pearson of Remvox Limited. Remvox manufacture and install class-leading and patented specialist electronics, audio intervention and crime prevention products.

Apply Here

To apply for this opportunity please email with:

·    A CV (2 pages maximum)

·    Application Form

·    Application Criteria Document

·    Reference Form

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



Read less
Why Study with Us?. Join an established course with excellent reputation and feedback. Get support and advice from experienced lecturers, tutors, librarians, e-learning and IT staff. Read more

Why Study with Us?

  • Join an established course with excellent reputation and feedback.
  • Get support and advice from experienced lecturers, tutors, librarians, e-learning and IT staff.
  • Access a wide range of online resources such as e-books, digital lectures and podcasts, discussion boards and video-conference tools all within a dedicated e-learning platform.
  • Develop and improve your employability, professional and academic skills and gain extensive hands-on practice with key software.
  • Obtain free student copies of GIS, remote sensing and statistical software.
  • Be assessed entirely by coursework – there are no formal examinations.
  • Pay your fees by instalments.
  • With a fully online course, you can study from almost anywhere and there is no need to travel to classes.
  • You can enrol to study part-time or full-time and choose the times you study each week to suit yourself.
  • Substantial relevant work experience may be accepted in place of standard entry requirements.
  • Study for a Master’s degree, a PG Diploma, PG Certificate or enrol for individual modules.

Why Study GIS?

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.

Course Summary

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.

In addition to acquiring substantial theoretical knowledge of the subject, you will gain extensive practical experience using a variety of software, focusing primarily on ArcGIS but also including ERDAS Imagine, PostgreSQL, PostGIS, MySQL, OpenLayers, Geoserver, QGIS, Excel, SPSS and a number of GIS extensions and plug-ins. One of the core modules provides experience of web-based programming languages such as HTML, CSS and JavaScript, whilst an optional module in customising GIS applications introduces Esri’s ModelBuilder and the Python programming language.

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.

Work placement / study abroad

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.

Career options

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.



Read less

Show 10 15 30 per page



Cookie Policy    X