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Masters Degrees (Data Visualization)

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This Joint Degree between HEC Paris and Ecole Polytechnique will equip students with both the technical skills and the strategic mindset to lead successfully any business career requiring a strong expertise in Big Data. Read more
This Joint Degree between HEC Paris and Ecole Polytechnique will equip students with both the technical skills and the strategic mindset to lead successfully any business career requiring a strong expertise in Big Data.

STUDY IN TWO GLOBALLY-RECOGNIZED INSTITUTIONS

Ecole Polytechnique (https://www.polytechnique.edu/en) and HEC Paris are both world leading academic institutions, renowned for the quality of their degrees, faculties and research (see HEC rankings http://www.hec.edu/Masters-programs/About/Rankings).

Their association within this Joint Degree represents the best Business/Engineering combination Europe could possibly offer, with extraordinary added value for the students who will follow this program in Big Data and Business.

LEAD THE DIGITAL TRANSFORMATION OF THE ECONOMY

Big data marks the beginning of a major transformation of the digital economy, which will significantly impact all industries. There are three main challenges to face:

> Technological: dealing with the explosion of data by managing the spread of vast amounts of information that is often very disorganized (IP addresses, fingerprinting, website logs, static web or warehouse data, social media, etc.)
> Scientific: replacing mass data with knowledge,i.e. developing the expertise that makes it possible to structure information, even out of tons of vague or corrupt data.
> Economic: managing data both to control risks and benefit from the new opportunities they offer. On the one hand, it is absolutely vital to be able to control the flow of information, anticipate data leaks, keep the information secure and ensure privacy. On the other hand, it is also essential to come up with solutions capable of transforming this flow of data into economic results and, at the same time, discover new sources of value from the data.

ACQUIRE THE SKILLS TO MAKE A DIFFERENCE IN TOMORROW’S DIGITAL WORLD

Exploiting this vast amount of data requires the following:

> A mastery of the sophisticated mathematical techniques needed to extract the relevant information.
> An advanced understanding of the fields where this knowledge can be applied in order to be in a position to interpret the analysis results and make strategic decisions.
> A strong business mindset and an even stronger strategic expertise, to be able to fully benefit from the new opportunities involved with Big Data problematics and develop business solutions accordingly.
> The ability to suggest and then decide on the choice of IT structures, the ability to follow major changes in IT systems, etc.

Therefore the program has three objectives:

> To train students in data sciences which combines mathematic modelling, statistics, IT and visualization to convert masses of information into knowledge.
> To give students the tools to understand the newest data distributing structures and large scale calculations to ease decision-making and guide them in their choices.
> To form data ‘managers’ capable of exploiting the results from analysis to make strategic decisions at the heart of our changeable businesses.

MAKE THE MOST OF WORLDWIDE NETWORKING AND ALUMNI POWER

Students will benefit not only from the close ties that HEC Paris has developed with the business world but also those of Ecole Polytechnique, through various networking events, conferences and career fairs.

The HEC Alumni network alone, consists of more than 52,300 members in 127 countries.

Program Details

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/Program-Details

Campuses

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/Campuses

CAREERS

As “Big Data” affects all kinds of companies and all sectors, students will have a very large range of career options upon graduation, from consulting firms to digital start-ups, not to mention very large multi-national companies.
In fact, as can be seen in all areas of cutting-edge innovation, there is a growing demand for high level managers who can combine strong technical skill with business know-how.

This is especially true when it comes to Big Data topics, and students graduating from data science and Big Data programs are therefore highly sought after on the job market.

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/Careers

FAQs

http://www.hec.edu/Masters-programs/Master-s-Programs/Dual-Degree-Programs-with-Partner-Institutions/MSc-Big-Data-for-Business-Joint-Degree-with-Ecole-Polytechnique/FAQ

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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.

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Today’s society operates on large amounts of data. Industry, governments and academia are asked to provide insight into these data. Read more
Today’s society operates on large amounts of data. Industry, governments and academia are asked to provide insight into these data.
•But how do we deal with such large amounts of data?
•What techniques do we use to mine the data?
•What are the legal and ethical aspects regarding these data sets?
•And what economic value can be found in big data?

The MSc specialization Data Science: Business and Governance trains students to become Data Scientists that can address these questions. The Harvard Business Review calls the job of Data Scientist "the sexiest job of the 21st century"!

Why Data Science: Business and Governance in Tilburg?
•Tilburg University offers a wide range of complementary expertise, including techniques for data mining, pattern recognition, business analytics, visualization and process analytics; as well as knowledge on law, regulation, ethics and entrepreneurship.
•The MSc specialization consists of courses in methods of analysis, together with economic and management as well as legal, ethical and methodological perspectives on data, all of them taught by experts in these fields.
•The Master’s specialization Data Science: Business and Governance offers (constitutes/ consists of) a well-balanced mixture of theoretical and practical (elective) courses.

These elements combine to make this specialization unique in Europe and possibly even in the world: Four schools (Tilburg School of Economics and Management, Tilburg School of Law, Tilburg School of Social and Behavioral Sciences, and the Tilburg School of Humanities) work together in offering the best possible training for the job of the future, that of Data Scientist.

Career Prospects

Data Science: Business and Governance graduates will not only have knowledge and expertise in the area of data analysis and data mining, but also in economic, management and legal perspectives on big data.

Growing need for Data Scientists

There is a growing need in government organizations, in companies and in academia for employees with the analytical skills needed to analyze large datasets, recognize patterns, and visualize data, and combining these skills with interdisciplinary knowledge of perspectives on Data Science.

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* One-year masters studentships are available for this stream. Each studentship will be worth £5000 and can be taken either as a reduction in fees or as a bursary. Read more

Studentships

* One-year masters studentships are available for this stream. Each studentship will be worth £5000 and can be taken either as a reduction in fees or as a bursary. Studentships will be awarded based on academic merit and are open to all applicants, regardless of fee status (home/EU/overseas). Please indicate 'Data Science' in the first line of your personal statement.

* Two PhD Studentships targeted at successful graduates from this stream. Two 3-year PhD studentships will be on offer, targeted at students obtaining a minimum of a Pass with Merit on the Data Science stream. These studentships will cover the cost of tuition fees for home/EU applicants and a stipend at standard Research Council rates.

Stream overview

The Data Science stream provides an interdisciplinary training in analysis of ‘big data’ from modern high throughput biomolecular studies. This is achieved through a core training in multivariate statistics, chemometrics and machine learning methods, along with research experience in the development and application of these methods to real world biomedical studies. There is an emphasis on handling large-scale data from molecular phenotyping techniques such as metabolic profiling and related genomics approaches. Like the other MRes streams, this course exposes students to the latest developments in the field through two mini-research projects of 20 weeks each, supplemented by lectures, workshops and journal clubs. The stream is based in the Division of Computational and Systems Medicine and benefits from close links with large facilities such as the MRC-NIHR National Phenome Centre, the MRC Clinical Phenotyping Centre and the Centre for Systems Oncology. The Data Science stream is developed in collaboration with Imperial’s Data Science Institute.

Who is this course for?

Students with a degree in physical sciences, engineering, mathematics computer science (or related area) who wish to apply their numeric skills to solve biomedical problems with big data.

Stream Objectives

Students will gain experience in analysing and modelling big data from technologically advanced techniques applied to biomedical questions. Individuals who successfully complete the course will have developed the ability to:

• Perform novel computational informatics research and exercise critical scientific thought in the interpretation of results.
• Implement and apply sophisticated statistical and machine learning techniques in the interrogation of large and complex
biomedical data sets.
• Understand the cutting edge technologies used to conduct molecular phenotyping studies on a large scale.
• Interpret and present complex scientific data from multiple sources.
• Mine the scientific literature for relevant information and develop research plans.
• Write a grant application, through the taught grant-writing exercise common to all MRes streams.
• Write and defend research reports through writing, poster presentations and seminars.
• Exercise a range of transferable skills by taking short courses taught through the Graduate School and the core programme of the
MRes Biomedical Research degree.

Projects

A wide range of research projects is made available to students twice a year. The projects available to each student are determined by their stream. Students may have access from other streams, but have priority only on projects offered by their own stream. Example projects for Data Science include (but are not limited to):

• Integration of Multi-Platform Metabolic Profiling Data With Application to Subclinical Atherosclerosis Detection
• What Makes a Biological Pathway Useful? Investigating Pathway Robustness
• Bioinformatics for mass spectrometry imaging in augmented systems histology
• Processing of 3D imaging hyperspectral datasets for explorative analysis of tumour heterogeneity
• Fusion of molecular and clinical phenotypes to predict patient mortality
• 4-dimensional visualization of high throughput molecular data for surgical diagnostics
• Modelling short but highly multivariate time series in metabolomics and genomics
• Searching for the needle in the haystack: statistically enhanced pattern detection in high resolution molecular spectra

Visit the MRes in Biomedical Research (Data Science) page on the Imperial College London web site for more details!

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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study High Performance and Scientific Computing at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study High Performance and Scientific Computing at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

The MSc in High Performance and Scientific Computing is for you if you are a graduate in a scientific or engineering discipline and want to specialise in applications of High Performance computing in your chosen scientific area. During your studies in High Performance and Scientific Computing you will develop your computational and scientific knowledge and skills in tandem helping emphasise their inter-dependence.

On the course in High Performance and Scientific Computing you will develop a solid knowledge base of high performance computing tools and concepts with a flexibility in terms of techniques and applications. As s student of the MSc High Performance and Scientific Computing you will take core computational modules in addition to specialising in high performance computing applications in a scientific discipline that defines the route you have chosen (Biosciences, Computer Science, Geography or Physics). You will also be encouraged to take at least one module in a related discipline.

Modules of High Performance and Scientific Computing MSc

The modules you study on the High Performance and Scientific Computing MSc depend on the route you choose and routes are as follows:

Biosciences route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Conservation of Aquatic Resources or Environmental Impact Assessment
Ecosystems
Research Project in Environmental Biology
+ 10 credits from optional modules

Computer Science route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Partial Differential Equations
Numerics of ODEs and PDEs
Software Engineering
Data Visualization
MSc Project
+ 30 credits from optional modules

Geography route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Partial Differential Equations
Numerics of ODEs and PDEs
Modelling Earth Systems or Satellite Remote Sensing or Climate Change – Past, Present and Future or Geographical Information Systems
Research Project
+ 10 credits from optional modules

Physics route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Partial Differential Equations
Numerics of ODEs and PDEs
Monte Carlo Methods
Quantum Information Processing
Phase Transitions and Critical Phenomena
Physics Project
+ 20 credits from optional modules

Optional Modules (High Performance and Scientific Computing MSc):

Software Engineering
Data Visualization
Monte Carlo Methods
Quantum Information Processing
Phase Transitions and Critical Phenomena
Modelling Earth Systems
Satellite Remote Sensing
Climate Change – Past, Present and Future
Geographical Information Systems
Conservation of Aquatic Resources
Environmental Impact Assessment
Ecosystems

Facilities

Students of the High Performance and Scientific Computing programme will benefit from the Department that is well-resourced to support research. Swansea physics graduates are more fortunate than most, gaining unique insights into exciting cutting-edge areas of physics due to the specialized research interests of all the teaching staff. This combined with a great staff-student ratio enables individual supervision in advanced final year research projects. Projects range from superconductivity and nano-technology to superstring theory and anti-matter. The success of this programme is apparent in the large proportion of our M.Phys. students who seek to continue with postgraduate programmes in research.

Specialist equipment includes:

a low-energy positron beam with a highfield superconducting magnet for the study of positronium
a number of CW and pulsed laser systems
scanning tunnelling electron and nearfield optical microscopes
a Raman microscope
a 72 CPU parallel cluster
access to the IBM-built ‘Blue C’ Supercomputer at Swansea University and is part of the shared use of the teraflop QCDOC facility based in Edinburgh

The Physics laboratories and teaching rooms were refurbished during 2012 and were officially opened by Professor Lyn Evans, Project Leader of the Large Hadron Collider at CERN. This major refurbishment was made possible through the University’s capital programme, the College of Science, and a generous bequest made to the Physics Department by Dr Gething Morgan Lewis FRSE, an eminent physicist who grew up in Ystalyfera in the Swansea Valley and was educated at Brecon College.

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This programme provides students with the knowledge of cutting-edge methodologies, approaches and skills in the emerging field of data science and big data applications, including advanced software development, systems for big data analytics, statistical data analysis data mining, distributed systems, data privacy and security, and data visualization and exploration. Read more
This programme provides students with the knowledge of cutting-edge methodologies, approaches and skills in the emerging field of data science and big data applications, including advanced software development, systems for big data analytics, statistical data analysis data mining, distributed systems, data privacy and security, and data visualization and exploration.

The programme of study culminates in a dissertation, enabling you to bring what you have learnt together in a significant piece of project work.

In summary, the MSc Big Data Science and Technology offers you the opportunity to build your own path of study - from the advanced computing modules, the extended list of optional modules available, as well as the dissertation - so as to match your specific career aspirations in the area of big data and data science.

For more information on the part time version of this course, please view this web-page: http://www.brad.ac.uk/study/courses/info/big-data-science-and-technology-msc-part-time

Why Bradford?

This programme intends to equip graduates with the cutting-edge knowledge and skills to work in the industry as a Data Scientist, Big Data Architect, or Big Data Analyst.

MSc Big Data Science and Technology provides industry with graduates that are ready and able to develop solutions to address challenges for big data analytics and developing big data systems.

Modules

-Software Development
-Big Data Systems and Analytics
-Information Theory and Data Communication
-Security, Privacy and Data Protection
-Mobile Applications
-Statistical Data Analysis
-Data Mining
-Concurrent and Distributed Systems
-Data visualization
-Dissertation

Career support and prospects

The University is committed to helping students develop and enhance employability and this is an integral part of many programmes. Specialist support is available throughout the course from Career and Employability Services including help to find part-time work while studying, placements, vacation work and graduate vacancies. Students are encouraged to access this support at an early stage and to use the extensive resources on the Careers website.

Discussing options with specialist advisers helps to clarify plans through exploring options and refining skills of job-hunting. In most of our programmes there is direct input by Career Development Advisers into the curriculum or through specially arranged workshops.

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The growing availability of huge amounts of data in business and industry is generating a high demand for graduates able to handle and exploit big data. Read more
The growing availability of huge amounts of data in business and industry is generating a high demand for graduates able to handle and exploit big data. Using sophisticated analytics techniques, including social network visualization and sentiment analysis, and professional software packages, including R dplyr and ggplot2, you will gain the skills and knowledge to transform data into commercial value and boost your employability.

Key features

-Equipping applicants from almost all undergraduate degrees with broad professional competence in one of the world economy’s most sought-after postgraduate subject areas.
-Offering an equal number of taught credits in the three areas of data modelling, computing and business, with a wide choice of available business modules.
-Providing a flexible individual project in one or more of the three taught areas supervised by world-leading subject experts.
-Establishing high proficiency in the use and application of state-of-the-art programming languages including R.
-Developing modern analytics expertise for obtaining business, scientific and social insights from Big Data sources and social networks such as Facebook and Twitter.
-Consult eBooks to support your learning and receive a free Apple iPad mini to stay-up-to date, wherever you are.
-Enjoy teaching from leading academics: for example, in the UK 2014 Research Excellence Framework 68 per cent of our mathematics and statistics research papers were classified as World Leading or Internationally Excellent.

Course details

Year 1
Throughout the programme you will learn how to master sophisticated analytics techniques and professional software, including R, to handle and exploit big data, and to work as part of a project team. You will also develop practical and professional competence in data science and business analytics, and will be able to make strategic decisions in a broad range of business related practical situations.

In semester two you will select optional modules to suit your interests and career aspirations and have the opportunity to boost your employability with an optional industrial placement.

An optional placement year is available after Semester 2.

Core modules
-ISAD515 Computational Problem Solving and Computer Systems
-BPIE500 Masters Stage 1 Placement Preparation
-MATH500 Big Data and Social Network Visualization
-PROJ516 MSc Project
-SOFT562 Software Development and Databases
-MATH501 Modelling and Analytics for Data Science

Optional modules
-STO702 Global Supply Chain Management
-ACF717 Econometrics and Financial Modelling
-MKT704 Branding and Marketing Communications
-STO703 International Strategic Management
-MKT714 Social Media Practice
-MKT715 Relationship Marketing and CRM
-STO700B International Business Environment
-ACF719 Financial Management

Final year
Optional modules
-BPIE503 Mathematics Masters Industrial Placement

Every postgraduate taught course has a detailed programme specification document describing the programme aims, the programme structure, the teaching and learning methods, the learning outcomes and the rules of assessment.

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Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business. Read more
Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business.

Whether it’s using Artificial Intelligence to improve a chess programme, or understanding the power of visualisation from a simple graph.

With input from industry experts in class and on-site, you will engage with real-world business problems.

PROGRAMME OVERVIEW

Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions.

This programme will take your career to the next level and develop your ability to confidently make high impact businesses decisions that are driven by data.

The programme focuses on two key areas: analysing business data, and solving business challenges analytically. Optional modules allow you to further specialise in areas such as the economic, managerial or finance or aspects of the subject.

Furthermore, you will benefit from hands-on experience of a wide range of analytics software such as simulators and mathematical tools.

PROGRAMME STRUCTURE

The programme is studied full-time over one academic year. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analytics
-Supply Chain Analytics
-Econometrics I
-Machine Learning and Visualisations
-Principles of Accounting
-Foundations of Finance: Finance and Investments
-Supply Chain and Logistics Management
-Information for Decision Making
-Managing Decisions Implementation
-Introduction to Marketing Analytics
-Econometrics II
-Business Process Management
-Innovation Management
-Investment Analysis
-Dissertation

CAREER PROSPECTS

Business analytics students often pursue careers as consultants, researchers, managers, and analysts.

SOFTWARE

You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:
-Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
-COGNOS and SQL Server for Business Intelligence for analytical processing
-Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
-SAP for Enterprise Resource Planning
-R, SPSS and EViews for coding, statistics and forecasting
-ILOG’s Optimisation Studio (Cplex) for optimisations
-Matlab for algorithms and programming and Simulink (SimEvents) for simulations
-Arena (or Simul8) for Discrete Event Simulations

EDUCATIONAL AIMS OF THE PROGRAMME

The programme’s aim is to provide a high quality education that is both intellectually rigorous and at the forefront of management science research, relevant for problem solving and decision making by managers.

It will respond to the emergent needs of corporations and academia for professionals who are able to work with analytical tools to generate value from available Information depots and take advantage of the vast amounts of data now provided by the modern ICT and ERP systems, which underlie the operations of modern corporations.

The program will implant understanding of the theoretical base around knowledge management and knowledge work, practical skills and experience in using analytical software tools.

It will allow future professional managers and consultants to cope with an increasingly complex and global operational environment of the modern corporation.

Completion of the programme will provide a sound foundation for those considering continuing their academic development towards a PhD degree in the management disciplines.

The programme is structured in a way that would provide students with a choice between a more quantitative intensive track of modules or a qualitative analytic (business development track) which would reflect students’ personal strengths and preferences and match future career aspirations.

The compulsory modules provide a sound foundation which builds an analytical skillset using relevant statistical and management theories, and supports the development of practical hands-on experience applying the theoretical aspects using real-world data to address corporate challenges and find solutions to actual problems.

The readings in the module will build a sound basis which would allow students to access and understand the academic literature and undertake empirical investigations in the areas of decision modelling and business development.

PROGRAMME LEARNING OUTCOMES

The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:

Knowledge and understanding
-A systematic, in-depth understanding of the development; issues and influences relevant to discipline of Management Decision Making, Management Science, and Data Science.
-Deep and thorough understanding of quantitative analytical methodologies and hands-on experience with decision-making software and data management tools.
-Knowledge about issues, application and analysis of Big Data
-An understanding of the academic research process.

Intellectual / cognitive skills
-Demonstrate deep learning, understanding of the material and ability to apply the knowledge and demonstrate skills in problem solving in the topic space of the modules studied
-Carry out assessments of data in a repository, select the appropriate analysis tools, design and execute an analytical methodology (not required for PG Certificate), apply adequate visualization methodologies to present the results and interpret the findings and finally to communicate the results effectively to a select audience

Professional practical skills
-Demonstrate the ability to independently evaluate critical approaches and techniques relevant to Business Analytics
-Know and apply a range of techniques and tools to analyse data related to business operations
-Capability of selecting the right methodology and software to solve management and operational business issues
-Relate existing knowledge structures and methodologies to analytical business challenges

Key / transferable skills
-Conduct critical literature review; to select, define and focus upon an issue at an appropriate level
-Develop and apply relevant and sound methodology
-Apply the methodology to analyse the issue
-Develop logical conclusions and recommendations
-Be aware of the limitations of the research
-Identify modifications to existing knowledge structures and theoretical frameworks and therefore to prose new areas for investigation, new problems, new or alternative applications or methodological applications

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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This postgraduate Masters course is uniquely designed for both computing and non-computing graduates wishing to pursue a career in data science community. Read more
This postgraduate Masters course is uniquely designed for both computing and non-computing graduates wishing to pursue a career in data science community. MSc Data Science is ideally placed to provide you with technical knowledge and employer-focused skills required in a data scientist role. The course will develop your specialist knowledge of data acquisition, data cleansing, data analysis, information extraction, prediction, visualization, story-telling and explanation.

You'll gain hands-on experience using industry standard tools for data science, business intelligence and analytics including SAS, Tableau MS SQL Server, Oracle Database, and R Programming.

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The Master of Public Health (MPH) programme is designed to prepare students for leadership in scientific and management roles in Public Health. Read more
The Master of Public Health (MPH) programme is designed to prepare students for leadership in scientific and management roles in Public Health. It attracts students from clinical and health related disciplines from all over the world.

Why study Public Health at Dundee?

This course provides high quality training in the application of epidemiological, statistical and behavioural techniques used in public health practice and research.

You will have the opportunity to improve generic and transferable skills and will be ideally placed to pursue a career in research or public health practice. The MPH also provides an ideal springboard for further postgraduate study at PhD level.

What's so good about studying Public Health at Dundee?

"My experience with the MPH course was positive in every respect. From the quality of the teaching to the academic support by members of the department, it was a highly rewarding experience. I would strongly recommend the MPH course at the University of Dundee"
Dr Thaofiq Olatunde Ijaiya (MPH, 2007)

"The MPH provides an invaluable experience of being taught by inspiring academics with excellent research experience in the field"
Tony Barr, Programme Manager - Nursing (MPH, 1996)

"The design and execution of the MPH programme was very good. However, I was most fulfilled during the research dissertation stage. It was a wonderful experience and I am prepared for the future research challenges."
Dr Ekwem Divine (MPH, 2010)

Teaching & Assessment

This course is taught predominantly by staff from the School of Medicine and guest lecturers from further afield.

The full time MPH course lasts for one year. The taught component takes nine months and is followed by research leading to a dissertation. All students initially enrol on a diploma programme (DipPubH) which includes the taught component only. To progress to the Masters programme and undertake the Masters dissertation students must obtain an overall pass of 60% in the taught component. Both programmes may be undertaken on a part-time basis.

This course has one start date - September

How you will be taught

The course is focussed on the academic needs of its students but also provides emotional and pastoral care as required. The inclusion of optional modules creates flexibility, which allows students to tailor the MPH to their specific requirements.

A variety of teaching and assessment methods are used to give students the best possible learning opportunity
Students are given formative assessment on all assignments

Students have a spacious teaching room with networked computers at their disposal

Lecturers operate an open door policy and students are encouraged to seek advice/help at any time

Students are given the opportunity to choose a dissertation topic which reflects their interest, then advised on the most appropriate supervisor

The rich research environment within the Division gives students the opportunity to work on a range of topics

The Division has weekly research seminars during term time, to which students are invited to attend
What you will study

The programme consists of the following modules

Core:
Public Health
Epidemiology
Introduction to Clinical Statistics
Research Methods
Behavioural & Social Science
Applied Epidemiology
Statistics for Clinical Trials
Applied Statistics with Routine Health Datasets
Introduction to Systematic Reviews
Data Visualization
Spatial Epidemiology

Optional:
Students studying for a Master of Public Health then undertake research and write a dissertation.

How you will be assessed

The student's performance is monitored by continuous assessment throughout the programme. Written and oral examinations are held in December, March and May. Examinations are graded passes (A to D) or fail.

Students must obtain grade D or above in all written and oral examinations in order to be awarded the Diploma.

To be eligible to progress from the Diploma onto the Masters, students must pass the examination of each subject and achieve a minimum overall pass at B or above (60%).

Careers

The MPH prepares students for research and management careers in public health and also a variety of medical and health related disciplines.

Previous students graduating with the MPH from Dundee have taken up management positions in public health practice, nursing, professions allied to medicine, education, government, WHO and NGOs; and research positions within academic public health, general practice, acute medicine, nursing, professions allied to medicine, veterinary medicine, dentistry and environmental health.

The MPH also provides an ideal springboard for further postgraduate study at PhD and MD level.

"I had a wonderful time during my stay at Dundee. Scotland as a whole and Dundee in particular have very friendly and hospitable people. This course is a well-balanced general MPH course. It will provide the prospective students with a good foundation and skill set to advance their career in research, management or academics.
Syed Asif Shah, MD. MPH. MBA. FACC. FACP, Assistant professor of Cardiology (MPH 1990)

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This industry-focused course is for Computer Science graduates and experienced professional programmers interested in developing high-quality, complex software systems. Read more
This industry-focused course is for Computer Science graduates and experienced professional programmers interested in developing high-quality, complex software systems.

Who is it for?

This industry-focused course is for Computer Science graduates and experienced professional programmers interested in developing high-quality, complex software systems and aiming at a high-quality career in the industry, e.g. software houses, consultancies, and major software users across different sectors.

Students will have a keen interest in designing complex software systems, coding them in a programming language using the latest technologies (SOA, cloud, etc.), and ensuring that they are of high quality and that they actually meet the needs of their stakeholders.

Objectives

You will develop skills in analysing requirements and designing appropriate software solutions; designing and creating complex software systems to solve real-world problems, evaluating and using advanced software engineering environments, design methods and programming languages, and evaluating and responding to recent trends in interoperability and software development.

The course focuses on advanced engineering concepts and methods, as well as design issues for the systematic development of high-quality complex software systems. These are explored using industrial strength technologies, like the C++ and Java programming languages and the UML modelling language.

The course covers significant trends in systems development, including service-oriented architecture, cloud computing, and big data. The course is delivered by acknowledged experts and draws on City's world-class research in Systems and Software Engineering, which has one of the largest groups of academics working in this area in London, covering almost all aspects - from requirements, to designing reliable systems for the nuclear industry.

Placements

Postgraduate students on a Computing and Information Systems course are offered the opportunity to complete up to six months of professional experience as part of their degree.

Our longstanding internship scheme gives students the chance to apply the knowledge and skills gained from their taught modules within a real business environment. An internship also provides students with professional development opportunities that enhance their technical skills and business knowledge.

Internships delivered by City, University of London offer an exceptional opportunity to help students stand out in the competitive IT industry job market. The structure of the course extends the period for dissertation submission to January, allowing students to work full-time for up to six months. Students will be supported by our outstanding Professional Liaison Unit (PLU) should they wish to consider undertaking this route.

Teaching and learning

Software Engineering MSc is available full-time (12 months) as well as part-time (up to 28 months).

Students successfully completing eight taught modules and the dissertation for their individual project will be awarded 180 credits and a Master's level qualification. Alternatively, students who do not complete the dissertation but have successfully completed eight taught modules will be awarded 120 credits and a postgraduate diploma. Successful completion of four taught modules (60 credits) will lead to the award of a postgraduate certificate.

Assessment

Each module is assessed through a combination of coursework and examination.

Modules

You will develop skills in analysing requirements and designing appropriate software solutions; designing and creating complex software systems to solve real-world problems, evaluating and using advanced software engineering environments, design methods and programming languages and evaluating and responding to recent trends in interoperability and software development.

The focus of the course is on advanced engineering concepts and methods, as well as design issues for the systematic development of high-quality complex software systems. These are explored using industrial strength technologies, such as the C++ and Java object-oriented programming languages and the UML modelling language.

The course covers significant trends in systems development, including service-oriented architecture, mobile and pervasive computing, cloud computing, big data, and XML-enabled interoperable services. The course is delivered by acknowledged experts and draws on City's world-class research in Systems and Software Engineering. City has one of the largest groups of academics working in the area in London, working on almost all aspects of the area - from requirements, to designing reliable systems for the nuclear industry.

Core modules - there are five core modules:
-Advanced Database Technologies (15 credits)
-Research Methods and Professional Issues (15 credits)
-Service Oriented Architectures (15 credits)
-Software Systems Design (15 credits)
-Advanced Programming: Concurrency (15 credits)

Elective modules - you will be required to take three elective modules, choosing from the following:
-Advanced Algorithms and Data Structures (15 credits)
-Big Data (15 credits)
-Programming in C++ (15 credits)
-Business Engineering with ERP Solutions (15 credits)
-Mobile and Pervasive Computing (15 credits)
-Data Visualization (15 credits)
-Cloud Computing (15 credits)

Career prospects

The MSc in Software Engineering aims to meet the significant demand for graduates with a good knowledge of computing. This demand arises from consultancies, software houses, major software users such as banks, large manufacturers, retailers, and the public services, defence, aerospace and telecommunications companies.

Typical entrants to the course have a degree in an engineering or scientific discipline, and wish to either move into the software engineering field or to the development of software for their current field. Entrants must have previous exposure to computing, especially to programming (particularly in Java or C#) and relational databases (from either academic or professional experience).

From this base, the course provides solid technical coverage of advanced software development, including such widely used languages as C++, Java, UML and XML for which demand is particularly high. The course is therefore quite demanding; its success in providing advanced academic education along these lines is evident from the fact that recent graduates of the course are currently employed in a wide spectrum of organisations.

Of course, the employment value of a master's degree is not just short term. Although on-the-job training and experience as well as technology specific skills are valuable, they can be rather narrow and difficult to validate, and to transfer. The structure of this course ensures that there is a strong balance between the development of particular skills and a solid education in the enduring principles and concepts that underlie complex software system development.

SAP Certification - in parallel to your degree you will be able to register for a SAP TERP10 Certification course at a substantial discount, thus obtaining an additional, much sought-after qualification

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With City’s MSc in Information systems and technology you will develop the skills to manage an organisation's IT infrastructure. This postgraduate Information Systems and Technology course is for students who have a keen interest in both information system development and information management. Read more
With City’s MSc in Information systems and technology you will develop the skills to manage an organisation's IT infrastructure.

Who is it for?

This postgraduate Information Systems and Technology course is for students who have a keen interest in both information system development and information management. Students are either in the early stages of their career or have significant work experience in the area and wish to formalise their knowledge.

Students will have curiosity about information and knowledge and will want to learn about managing them in organisations, together with the requisite design and technical skills to meet business requirements.

Objectives

Information systems are a key part of an organisation's IT infrastructure. IT professionals who can manage a business's information resources, and understand the technologies and systems that enable this are key to a modern enterprise's success.

Our postgraduate Information Systems and Technology degree will equip you with the skills to develop and maintain information systems that align with the strategic needs of any organisation.

Rather than focusing on technical issues only, the course combines technological fundamentals with a systematic understanding of IT's broader business contexts, including human and organisational factors. The course exploits City's research expertise in both computing and information management to produce effective professionals with a broad understanding of IT underpinned by a firm grasp of key technical concerns.

Placements

The School of Mathematics, Computer Science& Engineering has been delivering placements in the IT industry for over 20 years.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, Microsoft Dynamics, SAP etc you will have access to an array of tools to support your learning.

Teaching and learning

The teaching and learning methods we use mean that your specialist knowledge and autonomy increase as you progress through each module. Active researchers and professionals guide your progress in the areas of information systems and management, project management and business processes.

Taught modules are delivered through a series of lectures together with either tutorials or laboratory sessions. Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts or case studies. Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

Assessment

We expect you to study independently and complete coursework for each module. Modules are assessed through a combination of written examinations, coursework, group work and presentations.

The individual project is a substantial task. It is your opportunity to develop an autonomous research-related topic under the supervision of an academic member of staff. This is the moment when you can apply your learning to solve a real-world information system or information management problem. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.

Students successfully completing eight modules and the dissertation will be awarded 180 credits and a masters level qualification. Alternatively, students who do not complete the dissertation but have successfully completed eight modules will be awarded 120 credits and a postgraduate diploma. Successful completion of four modules (60 credits) will lead to the award of a postgraduate certificate.

Modules

The postgraduate Information Systems and Technology programme is made up of five core modules, three elective modules and a final project. All the electives are studied in the second term. You will take core and elective modules in three main streams: information, systems and technology. The third term is reserved for the project.

Modules include hands-on lab-based tutorials, group work seminars and presentations. We teach technical skills in SQL, JavaScript and PhP, as well as design skills using UML. You can pursue a practical MSc project in an application area of your choice.

With respect to hours please consult the SMCSE programmes office.

Core Modules
-Systems Specification INM312 (15 credits)
-Databases INM343 (15 credits)
-Information and Knowledge Management INM351 (15 credits)
-Research Methods and Professional Issues INM373 (15 credits)
-Information Architecture INM401 (15 credits)

Elective Modules - you may choose three elective modules from the following:
-Information Retrieval INM305 (15 credits)
-Web Applications Development INM316 (15 credits)
-Business Engineering with ERP Solutions INM342 (15 credits)
-Information Law and Policy INM361 (15 credits)
-Project Management INM372 (15 credits)
-Data Visualization INM402 (15 credits) *
-Libraries and publishing in the information society INM380 (15 credits)
-Information Organisation INM303 (15 credits) +
-Business Intelligence and Analytics INM451 (15 credits)

+ Students who take INM303 must also take INM305 as an option.
* Students may only take one of INM402 or INM451 as an option.

Career prospects

City’s Information Systems and Technology MSc graduates are prepared for employment in information systems management roles within large and small organisations including banks, consultancies, the pharmaceutical and IT industries, central and local government and the education and health sectors.

Previous graduates have secured employment in some of the most prestigious companies in the world including Merrill Lynch, Deutsche Bank, Virgin Atlantic, Barclays Capital and the Royal Bank of Scotland.

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The MPH in Palliative Care Research is designed for students wishing to pursue a service or academic career in palliative care. It will provide you with an excellent understanding of both research and public health issues, thus increasing your career opportunities. Read more
The MPH in Palliative Care Research is designed for students wishing to pursue a service or academic career in palliative care. It will provide you with an excellent understanding of both research and public health issues, thus increasing your career opportunities.

Why study Palliative Care Research at Dundee?

Dundee is ideally placed to deliver the MPH in Palliative Care. The Division of Population Health Sciences has several internationally recognised research programmes, with associated academic and research staff, and the Division also houses the renowned Health Informatics Centre (HIC) which provides researchers access to anonymised record-linked data. This includes routinely collected NHS patient datasets for the whole population.

The MPH degree has been run successfully in Dundee for over 25 years and our past students now contribute to the global public health workforce. Building on this success, we are ideally placed to offer a new exit - in palliative care research - from the core MPH. The MPH Palliative Care Research presents an opportunity to integrate public health with quality palliative care research and the clinical palliative care services. This provides a rich learning environment for prospective students.

Research led supervision

The Co-Director of the course, Dr Deans Buchanan, was recently appointed Consultant in Palliative Medicine. The Tayside Palliative Care Service is well placed to support excellence in research and in research training. The varied clinical settings throughout Tayside provide an excellent basis for research projects. Clinicians from within the palliative care service will supervise dissertations and include: Dr Rosie Conway, Dr Claire Douglas, Dr Fiona McFatter, Dr Martin Leiper and Dr Alison Morrison. In addition, Dr Bridget Johnston, Reader in Palliative Care (School of Nursing and Midwifery) will also contribute to the teaching and supervision of students.

Aims of the Programme

This course will provide you with:

* The necessary skills and expertise to enable you to undertake well designed research and interpret research data.
* The requisite communication skills and understanding of the importance of such communication.

Teaching & Assessment

This course is based in the School of Medicine. The MPH in Palliative Care Research degree course starts in September each year and lasts for 12 months on a full time basis, or 24 months on a part time basis.

How you will be taught

A variety of teaching methods will be used including traditional lectures; tutorials; discussion sessions; self directed learning including the use of internet based resources; and supervised research.

The MPH programme of studies provides teaching within a supportive environment and students are encouraged to contact lecturers to raise specific questions.

What you will study

Semester 1:
Epidemiology (15 SCQF credits)
Introduction to Clinical Statistics (15 SCQF credits)
Palliative care: Foundations and research part 1 (7 SCQF credits)

Semester 2, part 1
Research methods (15 SCQF credits)
Applied Statistics with Routine Health Datasets (15 SCQF credits)
Palliative care: Foundations and research part 2 (3 SCQF credits)

Semester 2, part 2
Spatial Epidemiology (5 SCQF credits) OR Data Visualization (5 SCQF credits)
Systematic reviews (5 SCQF credits)

Dissertation
The purpose of the dissertation is to enable students to write a dissertation which utilises all of the knowledge and expertise that they have acquired during the taught component of the course.

How you will be assessed

Performance is monitored by formal examinations and continuous assessment. Formative assessment is delivered through group and individual feedback during the tutorials, discussion sessions and on coursework. Summative assessment is based on assignments and examinations. Examinations are marked by two independent members of the School who are blinded to student identity. Guidelines for markers are provided. The dissertations are also double marked.

Careers

An MPH (Palliative Care Research) will enhance the employability of professionals interested in palliative care research.

For Specialty Trainees in Palliative Medicine this will add distinct skills sets and an essential understanding of both research and public health issues. Such qualifications will open job opportunities in academic medicine and at policy development levels. This course is compatible with the Palliative Medicine Curriculum and dissertation projects could be undertaken in candidates own localities.

Non-medical staff (including nursing staff) will benefit from the same skill set and enhanced ability to enter academic palliative care. Multi-professional learning is encouraged and dissertation projects can be tailored to specific backgrounds.

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This programme is aimed at researchers and professionals who commission, coordinate and use digital research in business, policy-making and the third sector, including digital marketing and analytical services officers. Read more

Programme description

This programme is aimed at researchers and professionals who commission, coordinate and use digital research in business, policy-making and the third sector, including digital marketing and analytical services officers.

This programme comprises a suite of courses that together provide the critical understanding and skills needed to make best use of digital research findings, with a particular focus on social media research, web 2.0 data and their synergies with publicly available administrative datasets.

These skills include:

understanding the production and consumption of automatic measures of user/citizen/customer feedback online
making best use of evidence from digital data analysis to inform business and policy decision-making
organising interdisciplinary strategies for digital research groups
identifying and accessing relevant expertise to undertake digital research for business and policy
anticipating future evolution and potential use of the digital research service market
Based on the latest research into the social and economic influences on how digital research is being developed and used, this PgCert is designed both for novices and for more experienced professionals who need to maintain a critical appreciation of the fast-moving field of digital research.

Online learning

This programme is available as a part-time online offering to allow professionals to study while working.

Programme structure

The programme is designed as a carefully thought-through progression from practical skills to a more critical understanding, through a comprehensive overview of available applications of digital research.

Courses:

Managing Digital Influence
Technologies of Civic Participation
Understanding Data Visualization
The Use and Evolution of Digital Data Analysis and Collection Tools
The Social Shaping of Digital Research
Engaging with Digital Research

Learning outcomes

Students who successfully complete this PgCert will be able to:

assess evidence deriving from monitoring digitally derived internet data, recognizing its strengths and limitations in comparison to other ways of apprehending customer and citizen needs
understand the work-practices of information professionals in digital research
critically discuss the current context and the future evolution of digital research
appreciate the practical benefits and limitations of digital data for organizational decision-making and policy-making
make best use of the results of digital data analytics for service design, marketing, institutional reputation management and policy-making
assess the relevance and value of projects at the forefront of digital research
manage and coordinate an interdisciplinary digital research team where social scientists, computer scientists and domain experts work together
identify, access and commission on-line data analytics tools and services appropriate to their needs
understand when and how to procure social media data analytics services and how to combine them with their existing knowledge practice

Career opportunities

This programme is aimed at creating career opportunities in the commercial sector (such as user experience consultants, industry analysts).

By offering a critical overview of digital research applications in different fields (user analytics, influence marketing and institutional reputation management), it will introduce you to careers in marketing and design as well as policy making.

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Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights. Read more
Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights.

Who is it for?

This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.

Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.

Objectives

The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.

City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.

The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.

Placements

There is the opportunity to do an internship as part of the programme. The final project, which is normally three months for a full-time student, can be extended to six months if you want to study within a specific organisation. When it comes to the big data and data science area, we have established relationships with organisations including the BBC, Microsoft and The British Library so you can be confident that with City, your access to professional experience is unparalleled. One recent student undertook an internship with Google and has since secured a job within the company.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, you will have access to an array of tools to support your learning.

The MSc Data Science programme offers two (three by mid 2016) dedicated computer servers for the Big Data module, which you can also use for your final project to analyse large data sets. We give you the opportunity to undertake training in MATLAB, the most popular numerical and technical programming environment, while you study.

Scholarships

A scholarship for the full fees of the MSc will be offered to an outstanding applicant. The scholarship is available to UK/EU and overseas students, studying full-time. To be considered for the scholarship, please include with your full application a one-page essay with your answer to the question:

'What are the challenges that Data Science faces and how would you address those challenges?'

The submission deadline for anyone wishing to be considered for the scholarship is: 1 MAY 2017

Teaching and learning

The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:
-Present and exemplify the concepts underpinning a particular subject.
-Highlight the most significant aspects of the syllabus.
-Indicate additional topics and resources for private study.

Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.

Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.

Course content

Data science is the area of study concerned with the extraction of insight from large collections of data.

The course covers the study, integration and application of advanced methods and techniques from:
-Data analysis and machine learning
-Data visualisation and visual analytics
-High-performance, parallel and distributed computing
-Knowledge representation and reasoning
-Neural computation
-Signal processing
-Data management and information retrieval.

It gives you the opportunity to specialise so, once you graduate, you can apply data science to any sector from health to retail. By engaging with researchers and industrial partners during the programme, you can develop your knowledge and skills within a real-world context in each of the above areas.

Core modules
-Principles of data science (15 credits)
-Machine learning (15 credits)
-Big Data (15 credits)
-Neural computing (15 credits)
-Visual analytics (15 credits)
-Research methods and professional issues (15 credits)

Elective modules
-Advanced programming: concurrency (15 credits)
-Readings in computer science (15 credits)
-Advanced databases (15 credits)
-Information retrieval (15 credits)
-Data visualisation (15 credits)
-Digital signal processing and audio programming (15 credits)
-Cloud computing (15 credits)
-Computer vision (15 credits)
-Software agents (15 credits)

Individual project - (60 credits)

Career prospects

From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.

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