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Masters Degrees (Data Visualization)

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Parsons’ Master of Science in Data Visualization is a multidisciplinary program in which students develop skills bringing together visual design, computer science, statistical analysis, and ethical considerations of data analysis and representation. Read more

Parsons’ Master of Science in Data Visualization is a multidisciplinary program in which students develop skills bringing together visual design, computer science, statistical analysis, and ethical considerations of data analysis and representation. The presentation of data plays a critical role in the shaping of opinion, policy, and decision making in today’s increasingly global society. Giving students a competitive edge as they enter the field, the MS program responds to the increased demand for experts who can turn data into insight.

This program is part of Parsons' School of Art, Media, and Technology (AMT). Learn about the AMT community and explore our blog to see what students, faculty, and alumni are doing in NYC and around the world.

Theory and Practice

Housed within Parsons’ School of Art, Media, and Technology, the MS Data Visualization program can be completed in one or two years. The 30-credit curriculum integrates theory and studio practice, so students acquire the creative, quantitative, and coding tools needed to analyze and depict data, gaining a holistic understanding of context, audience, and objectives. With the MS in Data Visualization, students obtain the diverse skill set needed for success in a range of careers related to data interpretation. Students graduate with portfolios demonstrating their ability to create databases and Web-based software tools that reflect an understanding of data analysis and information visualization for varied applications.

Opportunities with Local Industry

The program’s setting in New York City, a technological hub and pioneer of open-source culture, offers invaluable industry access. Students intern with industry leaders and external partners from the government, nonprofit, and commercial sectors. Faculty invite guest lecturers and critics to share their insights and expose students to new possibilities in data visualization and related career paths.

Future Opportunities

Graduates find success in a wide variety of fields including data analysis, digital design, advertising and branding, journalism, business consulting and analytics strategy, management, strategic planning, entrepreneurship, social enterprise, public policy, trend forecasting, and business intelligence.

You can request more information about all possible future opportunities here: http://www.newschool.edu/m/data-visualization?utm_source=find_a_masters&utm_medium=hyperlink_listing&utm_campaign=pm_parsons_grad&utm_term=data_visualization

Financial Aid Deadline

All applicants selected for admission into our program are considered for a merit scholarship award that is determined by the strength of their application. Scholarship award notification is communicated at the same time as the admission decision. International students are eligible only for merit scholarships. If you are a U.S. citizen or eligible noncitizen, we encourage you to complete the Free Application for Federal Student Aid (FAFSA), which can be found on the Web at http://www.fafsa.gov. The FAFSA is available each year on October 1. You do not need to wait for an admission decision to apply for federal aid; we recommend submitting by our FAFSA priority deadline of February 1 for fall applicants. (The New School’s federal school code is 002780.)

You can request more information on available scholarships here: http://www.newschool.edu/m/data-visualization?utm_source=find_a_masters&utm_medium=hyperlink_listing&utm_campaign=pm_parsons_grad&utm_term=data_visualization



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This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques. Read more

This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.

Introducing your degree

This MSc programme will train students to become proficient data scientists.

You will gain advanced knowledge in areas such as data mining, machine learning, and data visualization, including state of the art techniques, programming toolkit, and industrial and societal application scenarios.

Overview

This programme prepares you to become a proficient data scientist, developing your specialist knowledge in subjects that are crucial for mastering the vast and ever-so-complex information landscape that is characteristic to modern, digitally empowered organisations.

This is typically linked to a number of core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to the know-how that is required to devise and apply sophisticated Big Data analytics techniques, and the creativity involved in designing powerful visualizations.

In the first semester you start with a review of key topics in data science. The course will introduce the core theoretical and technology components required to design and use a data science application, using open-source tools and openly accessible data sets. You will also cover the most important machine learning techniques, which are at the core of any attempt to analyse and reason about data.

You will be exposed to more advanced topics in data mining in the second semester, including feature engineering, methods to manipulate text and multimedia data, topic modelling, social network analysis, and spectral analysis. A new module on data visualization will introduce the most common types of visualization techniques and state-of-the-art technology used to build graphic elements into data science applications to present analytics results.

Finally, during the summer the MSc project enables you will demonstrate your mastery of specialist techniques, relevant methods of enquiry, and your ability to design and deliver advanced application, systems and solutions to a tight deadline, including the production of a substantial dissertation.

Career Opportunities

Data scientists help organisations handle large amounts of data being produced thanks to digital technologies. Harvard Business Review described the role as 'The Sexiest Job of the 21st Century' due to the rare combination of skills that a trained data scientist possesses.

Data science has seen an unparalleled expansion as the data-driven economy grows. Increasingly organisations require skilled professionals who can handle large datasets and managers who can utilise the resulting analysis to make impactful decisions.

There is a range of potential jobs available; demand for big data staff is predicted to rise 92% over 5 years from Jan 2013. The programme provides an excellent opportunity for entry into data sciences or similar fields. Plus, big data positions offer a median salary of £55,000 – 24% higher than for IT staff in general (UK). There are also academic possibilities for doctoral study, as there are for entrepreneurial careers.

ECS runs a dedicated careers hub with is affiliated with more than 100 renowned companies such as IBM, Arm, Microsoft, Samsung, and Google. Visit our Careers Hub for more information.

Graduates from our MSc program can seek employment worldwide in:

  • established companies looking to spot trends in sales, marketing or operational data;
  • start-ups based around new opportunities in the booming data-driven economy;
  • government departments looking to utilise linked open data to gain insights to affect policy at the highest levels;
  • research/consultancy companies analysing data and feeding back to the wider community, with training and specialist services to clients.

Through an extensive blend of networks, mentors, societies and our on-campus startup incubator, we also support aspiring entrepreneurs looking to build their professional enterprise skills. Discover more about enterprise and entrepreneurship opportunities.



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics

- Data Science Research Methods and Seminars

- Big Data and Data Mining

- Big Data and Machine Learning

- Mathematical Skills for Data Scientists

- Data Visualization

- Human Computer Interaction

- High Performance Computing in C/C++

- Graphics Processor Programming

- Computer Vision and Pattern Recognition

- Modelling and Verification Techniques

- Operating Systems and Architectures

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst

- Data mining Developer

- Machine Learning Developer

- Visual Analytics Developer

- Visualisation Developer

- Visual Computing Software Developer

- Database Developer

- Data Science Researcher

- Computer Vision Developer

- Medical Computing Developer

- Informatics Developer

- Software Engineer



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Big Data and Data Engineering. Big data has turned out to have giant potential, but poses major challenges at the same time. On the one hand, big data is driving the next stage of technological innovation and scientific discovery. Read more

Big Data and Data Engineering

Big data has turned out to have giant potential, but poses major challenges at the same time. On the one hand, big data is driving the next stage of technological innovation and scientific discovery. Accordingly, big data has been called the “gold” of the digital revolution and the information age. On the other hand, the global volume of data is growing at a pace which seems to be hard to control. In this light, it has been noted that we are “drowning in a sea of data”.

Faced with these prospects and risks, the world requires a new generation of data specialists. Data engineering is an emerging profession concerned with big data approaches to data acquisition, data management and data analysis. Providing you with up-to-date knowledge and cutting-edge computational tools, data engineering has everything that it takes to master the era of big data.

Program Features

The Data Engineering program is located at Jacobs University, a private and international English-language academic institution in Bremen, Germany. The two-year program offers a fascinating and profound insight into the foundations, methods and technologies of big data. Students take a tailor-made curriculum comprising lectures, tutorials, laboratory trainings and hands-on projects. Embedded into a vibrant academic context, the program is taught by renowned experts. In a unique setting, students also team up with industry professionals in selected courses. Core components of the program and areas of specialization include:

- The Big Data Challenge

- Data Analytics

- Big Data Bases and Cloud Services

- Principles of Statistical Modeling

- Data Acquisition Technologies

- Big Data Management

- Machine Learning

- Semantic Web and Internet of Things

- Data Visualization and Image Processing

- Document Analysis

- Internet Security and Privacy

- Legal Aspects of Data Engineering and Data Ethics

For more details on the Data Engineering curriculum, please visit the program website at http://www.jacobs-university.de/data-engineering.

Career Options

Demand for data engineers is massive – in industry, commerce and the public sector. From IT to finance, from automotive to oil and gas, from health to retail: companies and institutions in almost every domain need experts for data acquisition, data management and data analysis. With an MSc degree in Data Engineering, you will excel in this most exciting and rewarding field with very attractive salaries. Likewise, an MSc degree in Data Engineering allows you to move on to a PhD and to a career in science an research.

Application and Admission

The Data Engineering program starts in the first week of September every year. Please visit http://www.jacobs-university.de/graduate-admission or use the contact form to request details on how to apply. We are looking forward to receiving your inquiry.

Scholarships and Funding Options

All applicants are automatically considered for merit-based scholarships of up to € 12,000 per year. Depending on availability, additional scholarships sponsored by external partners are offered to highly gifted students. Moreover, each admitted candidate may request an individual financial package offer with attractive funding options. Please visit http://www.jacobs-university.de/study/graduate/fees-finances to learn more.

Campus Life and Accommodation

Jacobs University’s green and tree-shaded campus provides much more than buildings for teaching and research. It is home to an intercultural community which is unprecedented in Europe. A Student Activities Center, various sports facilities, a music studio, a student-run café/bar, concert venues and our Interfaith House ensure that you will always have something interesting to do. In addition, Jacobs University offers accommodation for graduate students on or off campus.



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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. Read more

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.

Accreditation

Accredited by BCS, The Chartered Institute for IT for the purposes of fully meeting the further learning academic requirement for registration as a Chartered IT Professional, and on behalf of the Science Council for the purposes of partially meeting the academic requirement for registration as a Chartered Scientist and a Chartered Engineer.

Internships

MSc Data Science students can participate in our professional internships programme, which is supported by the Professional Liaison Unit. This will enable you to undertake your MSc project in an industrial or research internship over an extended period compared to regular projects. For example, the individual project can be carried out as a 6-month internship in one of the companies with which City has a long-standing relationship and history of collaboration in the big data and data science area.

Examples of company placements internships taken by our Data Science students in the recent past include: Google, SagePay, Reward, Black Swan.

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.

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.

Career & Skills Development Service at City, University of London

After successful completion of the course you may wish to consider a PhD degree in Computing.



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This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. Read more
This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. You will cover the fundamental statistical (eg machine learning) and technological tools (eg cloud platforms, Hadoop) for large-scale data analysis.

The Big Data science movement is transforming how Internet companies and researchers over the world address traditional problems. Big Data refers to the ability of exploiting the massive amounts of unstructured data that is generated continuously by companies, users, devices, and extract key understanding from it.

A Data Scientist is a highly skilled professional, who is able to combine state of the art computer science techniques for processing massive amounts of data with modern methods of statistical analysis to extract understanding from massive amounts of data and create new services that are based on mining the knowledge behind the data. The job market is currently in shortage of trained professionals with that set of skills, and the demand is expected to increase significantly over the following years.

The course leverages the world-leading expertise in research at Queen Mary with our strategic partnership with IBM and other leading IT sector companies to offer to students a foundational MSc on the field of Data Science. The MSc modules cover the following aspects:

-Statistical Data Modelling, data visualization and prediction
-Machine Learning techniques for cluster detection, and automated classification
-Big Data Processing techniques for processing massive amounts of data
-Domain-specific techniques for applying Data Science to different domains: Computer Vision, Social Network Analysis, Bio-Engineering, Intelligent Sensing and Internet of Things
-Use case-based projects that show the practical application of the skills in real industrial and research scenarios.
-Students will be offered lectures that explain the core concepts, techniques and tools required for large-scale data analysis. -Laboratory sessions and tutorials will put these elements to practice through the execution of use cases extracted from real domains. -Students will also undertake a large project where they will demonstrate the application of Data Science skills in a complex scenario.

The programme is offered by academics from the Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science research groups from the School of Electronic Engineering and Computer Science. This is a team of more than 100 researchers (academics, post-docs, research fellows and PhD students), performing world leading research in the fields of Intelligent Sensing, Network Analytics, Big Data Processing platforms, Machine Learning for Multimedia Pattern Recognition, Social Network Analysis, and Multimedia Indexing.

Industrial Experience

The industrial placement currently takes place towards the end of the first year for a maximum of 12 months. It is the student’s responsibility to secure their placement, the school will offer guidance and support in finding and securing the placement but the onus is on the student to secure the job and arrange the details of the placement.

Currently if you are not able to secure a placement by the end of your second semester we will transfer you onto the 1 year FT taught programme without the Industrial Experience, this change would also be applied to any visa if you were here on a student visa.

The industrial placement consists of 8-12 months spent working with an appropriate employer in a role that relates directly to your field of study. The placement is currently undertaken between the taught component and the project. This will provide you with the opportunity to apply the key technical knowledge and skills that you have learnt in your taught modules, and will enable you to gain a better understanding of your own abilities, aptitudes, attitudes and employment potential. The module is only open to students enrolled on a programme of study with integrated placement.

If you do not secure a placement you will be transferred onto the 1 year FT programme.

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Why choose the MSc in Business Analytics?. Do you want to be a professional analyst who understand both the technologies and the business?. Read more

Why choose the MSc in Business Analytics?

  • Do you want to be a professional analyst who understand both the technologies and the business?
  • Do you want to master the skills in making data-driven business decisions?
  • Do you want to learn the practical use of data visualization tools, statistical analysis tools, and big data technologies?

What is business analytics?

Business Analytics is the intersection of management science and machine learning in real world applications.

It offers new potential to improve financial performance, strategic management and operational efficiency.

Business Analytics is an increasingly critical component in preparing organizations to solve 21st-century business challenges and support data driven decision making.

Programme overview

Our MSc Business Analytics programme is a one year, full-time programme consisting of 6 core modules, and 2 elective modules from a choice of 7 elective modules.

The core modules are conducted via lectures, tutorials, and computer laboratory sessions. Students undertake the dissertation project in Business Analytics in collaboration with one of our international industrial partners.

Graduates of the programme will have gained the necessary skills and knowledge in a range of fields, including business operation, database, statistics, informatics, data analytics, machine learning and big data technologies in real-world business contexts.

Applicants for this programme are required to have at least a second class honours in the first division or international equivalent in any discipline, including business and management, and at least 10 credits equivalent value with significant mathematical/statistical content (However, this course is not suitable for students who have previously studied a significant amount of business analytics).

Teaching and Learning

Our learning environment is highly interactive and innovative with student-centred learning activities.

Other than examinations, our students will be assessed via essays writing, practical exercises, group and individual projects, and oral presentations.

The dissertation focuses on developing students’ skills in applying analytic techniques, communicating and solving the data analytics problem.

Career options for this degree

The area of business analytics is growing in financial sectors, customer services, enterprise optimization, and consumer marketing.

When our students graduate, they will be able to:

  • Find a job in the business firms that require the knowledge of big data and advanced analytic techniques.
  • Study the organisations, management, and international external environments.
  • Gain business insights and professional skills in data mining, data visualization, data management, process modeling, predictive and advanced analytics.
  • Develop the ability to optimize the business processes and management practice.
  • Contribute to business and society at large.

What are the potential careers of our graduates?

  • Business intelligence analytics,
  • Marketing analyst
  • Business systems analyst
  • Data scientist
  • Business consultant
  • Solution Architects


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MSc - full time. 12 months, part time. 24 months. PGDip - full time. 8 months,  part time. 24 months. PGCert - full time. Read more

MSc - full time: 12 months, part time: 24 months

PGDip - full time: 8 months,  part time: 24 months

PGCert - full time: 8 months, part time: up to 24 months

Our Data Science MSc gives you the knowledge, experience, and expertise to solve real-world problems and realise data-driven insights for organisations.

Data Science is revolutionising every area of science, engineering and commerce. It offers the potential for huge societal and economic benefits. The Data Science MSc was created in collaboration with a number of high profile industry leaders to address the skills shortage in data analytics. The course brings together students and industry practitioners in a setting which new technologies are developed and translated into industry practise. 

What you'll learn

Through this course you'll receive a comprehensive grounding in theory and application of data science. You'll develop the multi-disciplinary combination of skills in statistics and computer science. You'll also gain the ability to apply these skills to real problems in a given application area.

Topics covered in the course include:

  • data visualization
  • cloud computing
  • Bayesian statistics
  • machine learning.

Your development

We have substantial expertise in data science, focusing on a wide range of application areas. This includes:

  • healthcare
  • transport
  • cybersecurity
  • smart cities
  • manufacturing.

We are home to the UK’s National Innovation Centre for Data (NICD). We are also a partner of the Alan Turing Institute, the national institute for data science and artificial intelligence. All our academic staff involved in teaching data science modules have international reputations for their contributions to the field. Many of them have extensive experience as practitioners in industry as well as work in academia.

You will be encouraged to play a full part in the life of the School, including:

  • taking advantage of dedicated computing and study facilities
  • participating in seminars delivered by researchers and distinguished external speakers.

Project work

You will undertake individual and group-based projects. You will work in collaboration with regional and national industry and charitable organisations.

Your five-month individual project gives you an opportunity to:

  • develop and deepen your knowledge and skills
  • work in a research or development team.

 You can develop your project:

  • at the University under an academic supervisor
  • by securing an industrial placement
  • working with your current employer.

You will have one-to-one supervision from an experienced member of staff, supported with supervision from industry partners as required.

Delivery

The School of Computing and School of Mathematics, Statistics and Physics deliver the course. The course starts in mid-September. You will be taught in state-of-the-art facilities in the newly-opened Urban Sciences Building. The course has three phases.

In phase one you’ll be introduced to core knowledge and skills in statistics and computer science. These modules are taught as an intensive block. Pairs of modules will be taught concurrently over four weeks of lectures and lab classes. Teaching is timetabled to accommodate participants from industry, working alongside full-time employment.

Phase two will present further advanced technical modules. You will be introduced to the aspects that underlie all areas of data science practice:

  • professionalism
  • legislation
  • ethics.

This phase also includes a group project in collaboration with industry. You'll develop and evaluate a data science solution to a complex, real-world problem.

Phase three is an individual research and development project. You'll receive personal supervision in one of the School’s research labs in collaboration with industry or with your current employer.

You'll be assessed by a portfolio of practical work, accompanied by an oral interview. There will be no written examinations as part of the Data Science MSc.

If you’re a part time student, you have the flexibility to study over two years. The part time version of the course encourages participation of practitioners from industry. As a part time student you can:

  • align your assessed work with the priorities of your job role
  • carry out your individual project in your place of work (as long as the supervisory processes in place meet University standards).

Facilities

You'll be taught on the Newcastle Helix campus which brings together:

  • academia
  • the public sector
  • communities
  • business and industry.

You will benefit from state-of-the-art teaching facilities within the newly-opened £58m Urban Sciences Building, including a purpose-built Decision Theatre and 3D visualisation facility.



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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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We offer Industrial Experience options on all our full-time taught MSc programmes, which combine academic study with a one-year industrial placement between your taught modules and summer project. Read more
We offer Industrial Experience options on all our full-time taught MSc programmes, which combine academic study with a one-year industrial placement between your taught modules and summer project. Taking the Industrial Experience option as part of your degree gives you a route to develop real-world, practical problem-solving skills gained through your programme of study in a professional context.

This can give you an important edge in the graduate job market. As a leading research School, we have excellent links with industry. We also employ dedicated staff to help you arrange your year in industry. The Industrial Experience programmes are highly competitive and attract the best students given the limited availability of placements. We are unable to guarantee all students secure an industrial placement, as our industrial partners conduct their own employment application and interview processes.

This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. You will cover the fundamental statistical (eg machine learning) and technological tools (eg cloud platforms, Hadoop) for large-scale data analysis.

The course leverages the world-leading expertise in research at Queen Mary with our strategic partnership with IBM and other leading IT sector companies to offer to students a foundational MSc on the field of Data Science. The MSc modules cover the following aspects:

-Statistical Data Modelling, data visualization and prediction
-Machine Learning techniques for cluster detection, and automated classification
-Big Data Processing techniques for processing massive amounts of data
-Domain-specific techniques for applying Data Science to different domains: Computer Vision, Social Network Analysis, Bio Engineering, Intelligent Sensing and Internet of Things
-Use case-based projects that show the practical application of the skills in real industrial and research scenarios.

Students will be offered lectures that explain the core concepts, techniques and tools required for large-scale data analysis. Laboratory sessions and tutorials will put these elements to practice through the execution of use cases extracted from real domains. Students will also undertake a large project where they will demonstrate the application of Data Science skills in a complex scenario.

The programme is offered by academics from the Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science research groups from the School of Electronic Engineering and Computer Science. This is a team of more than 100 researchers (academics, post-docs, research fellows and PhD students), performing world leading research in the fields of Intelligent Sensing, Network Analytics, Big Data Processing platforms, Machine Learning for Multimedia Pattern Recognition, Social Network Analysis, and Multimedia Indexing.

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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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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-recognised Institutions

Ecole Polytechnique and HEC Paris are both world leading academic institutions, renowned for the quality of their degrees, faculties and research (see HEC 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 Data Science 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 the 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

The key aim of the teaching in this Joint Degree is to provide students with the tools needed to solve real problems, using structured and unstructured data masses, teaching them to ask the ‘right’ questions (both from statistics and ‘business’ perspectives), to use the appropriate mathematical and IT tools to answer these questions.

Students will be equipped to shift constantly from data to knowledge, from knowledge to strategic decision, and from strategic decision to operational business implementations.

All these shifts carry with them numerous challenges that each require an interdisciplinary approach involving mathematics, IT, business strategy, and management skills.



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Our MSc Big Data course addresses the growing importance of big data in business, and society at large. International Data Corporation (IDC. Read more

Our MSc Big Data course addresses the growing importance of big data in business, and society at large.

International Data Corporation (IDC: a market research firm) forecast that the Big Data technology market will grow at a 26.4% compound annual growth rate (CAGR) to £28.79 billion by 2018 – approximately six times the growth rate of the overall information technology market; with 30% organizations collecting big data and/or the market of data driven services.

Our modules will prepare you to make notable contributions in modern day organizations with Big Data technologies. Equipped with necessary knowledge and hand-on experience you will enhance your employability within UK and internationally.

Unique and challenging modules are introduced, including Mobile networks and smartphone applications, Data mining and visualisation in addition to Ethics for IT professionals and Object oriented analysis and design that provide the foundation for Big Data.

Course Details

You will study the latest trends and technologies in Big Data in the following modules together with a Master's dissertation to obtain the MSc degree:

  • Mobile Networks and Smartphone Application
  • Data Mining and Visualization
  • Ethics for IT professionals
  • Object Oriented Analysis & Design
  • Research Design and Methods
  • Emerging Topics in Smart Networks
  • Advanced Data Science*
  • Intelligent Systems*

*Specialist modules

Teaching & Assessment

All modules are designed to respect the themes of Big Data, delivering research informed teaching via:

  • Lectures
  • Tutorials
  • Laboratory sessions

Our assessment methodology is influenced by the learning outcomes to be tested and employs a range of methods including:

  • essays
  • reports
  • strategic planning proposals
  • critical incident analysis
  • projects
  • research proposal
  • dissertation

Career Prospects

Jobs

Examples of jobs available to you upon graduation include:

  • Data analysts
  • Data scientists
  • Data mining analysts
  • Technical developer
  • Project manager in Big Data

Salaries in these roles range from £30,000 to £75,000.

Further Study

Successful completion of our course prepares you for advanced research studies in related technology areas. You will have the priority to be admitted to the MPhil/PhD degree courses.



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study High Performance and Scientific Computing at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study High Performance and Scientific Computing at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

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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Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. Read more
Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. As a result, there is a large and growing demand for trained researchers who can harness the power of big data using the latest tools and analytical techniques to uncover new insights and drive businesses forward.

This Marketing Research and Analytics program combines advanced courses in marketing research and big data analytics with training on leading commercial technologies and platforms and the opportunity to gain in-demand industry certifications.

This program equips you with knowledge, skills and training in leading business intelligence and marketing research technologies and tools used in the field. Among them are SAS Enterprise Guide and SAS Enterprise Miner, Environics Analytics Envision (used to develop comprehensive profiles of selected target markets), SPSS, Tableau (the leading data visualization software), Excel, XL Miner, Dell Factiva and NVIVO (qualitative research and text analysis software).

Upon graduation, you receive an Ontario Graduate Certificate from Centennial College, plus certificates of recognition from SAS and Environics Canada. In addition, you are put on an accelerated track to earning the Certified Marketing Research Professional (CMRP) designation, the premier credential in Canadian marketing research from the Marketing Research and Intelligence Association (MRIA).

Career Opportunities

Program Highlights
-The Marketing – Research and Analytics program combines marketing research principles and skills with cutting edge "big data" analytics techniques to equip you with the training required to deliver insights and strategies to help organizations make smarter and more impactful business decisions.
-Employed is an extensive use of learner-centered approaches such as case studies, simulations and project-based learning, with a focus on developing project management, teamwork, analytical thinking, and report writing and presentation skills.
-Hands-on learning covers areas such as questionnaire design, data manipulation, quality control, statistical output and program development.
-There is a strong focus on applying marketing research and analytics to strategic marketing decision-making.
-In the second semester, you develop and implement a capstone project that will integrate and apply your learning.
-In addition to market research technologies, you also have access to the full suite of Microsoft products, including Microsoft Excel, XL Miner, Access and PowerPoint.
-Once you graduate, you have the option to take the Comprehensive Marketing Research Exam (CMRE) on campus at Centennial College, which leads to the Certified Marketing Research Professional (CMRP) designation.

Articulation Agreements
Start with a graduate certificate, and continue to a master of business administration through our degree completion partnership. Successful graduates of this Marketing – Research and Analytics program may choose to continue with courses leading to a graduate degree.

Career Outlook
-Marketing research specialist or analyst
-Research analyst
-Marketing research and intelligence coordinator
-Market intelligence specialist or analyst
-Customer insights analyst
-Consumer research manager
-Business intelligence analyst
-Market research analytics manager
-Web marketing analyst
-Customer experience analyst
-CRM analyst
-Direct response analyst
-Digital marketing analyst
-Social media analyst
-Data and analytics specialist
-Business analytics specialist
-Loyalty program analyst
-Sales analyst
-Marketing strategy analyst

Program Outcomes
-Optimize the financial results produced by interactive marketing programs through the application of marketing analytics
-Contribute to the design of a marketing analytics team project (develop charter, business case financials, technical requirements, design, test plan, test results, approval to proceed) and the management of the resulting project
-Create, manage and mine, and apply modelling and decision making functions to a database
-Utilize data auditing techniques and quality control processes that are consistent with current marketing research codes of conduct and Canadian privacy principles to ensure the integrity of the data collection, storage, analysis and presentation processes
-Compare and contrast, evaluate and select appropriate data sources to meet specific marketing objectives
-Conduct industry, competitor and customer analyses using a wide variety of secondary research sources
-Produce reliable and analyzable data through the application of sound questionnaire design principles to marketing research projects
-Design marketing research projects and interactive marketing programs that are founded in sound sampling techniques, hypothesis testing and research design
-Solve business and marketing problems by identifying, selecting and applying effective, current and relevant techniques such as descriptive and inferential analysis
-Prepare provisional output of analyses including cross-tabulations and pivot tables that address the needs of analysts and prepare final output, including research reports, presentation sides and visual representations of data that address the needs of management
-Develop actionable recommendations based on situation analyses and research findings

Areas of Employment
-Retail corporations
-Organizations with in-house analytical and research functions
-Marketing research firms

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