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Masters Degrees (Data Scientist)

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There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data"). Read more
There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data").

Why study Data Science at Dundee?

The School of Computing has been working on 'big data' and data analysis for at least five years; not only working with data but also developing new algorithms and techniques for data scientists. The School already runs the most successful Business Intelligence Masters course in the UK.

This course will be led by Professor Mark Whitehorn and Andy Cobley. Mark is an emeritus professor at the University of Dundee and also runs a successful consultancy company that specialises in BI, Data Sciences and analytics. Andy is the course organiser for both the existing BI course and the new Data Science course.

This course will enhance your employability by providing you with knowledge, skills and understanding of data science research and implementation. You will also acquire skills in the professional procedures necessary to ensure that data science research and implementation is both valid and actionable and engage with contemporary debate about the role, ethics and utility of data science in commercial and other settings.

What is the difference between Data Science and Business Intelligence?

There is clearly a huge overlap with Business Intelligence. A BI specialist will need to understand data and data analytics. However there is a bias towards understanding how data is stored in the current operational systems within an enterprise the design and the implementation of an analytical system such as a data warehouse. A data scientist will be less concerned with the construction of a data warehouse and more interested in the message the specific sets of data can deliver.

However, without some understanding of data warehouses the data scientist will find it difficult to interrogate the data for its secrets. For this reason there is overlap between the two courses.

If you already have a strong grounding in Business Intelligence and would like to upgrade your knowledge to include topics from the Data Science MSc, we offer the relevant Data Science modules either on a stand alone basis or as a PGCert.

What's so good about Data Science at Dundee?

Our facilities will give you 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

A booming Postgraduate culture where the School of Computing maintains a friendly, intimate and supportive atmosphere, and we take pride in the fact that we know all of our students - you're far more than just a matriculation number to us. We have a thriving postgraduate department with regular seminars and guest speakers.

Duncan Ross (Director of Data Sciences at Teradata) has said that: "The first and most important trait is curiosity. Insane curiosity. In many walks of life evolution selects against the kind of person who decides to find out what happens 'if I push that button'. Data Science selects for it."

How you will be taught

The programme will be delivered by Prof. Mark Whitehorn with input from Andy Cobley, Yasmeen Ahmad, Chris Hillman and other specialists from within the School of Computing in an innovative blend of live co-presented master-classes, video seminars and recorded materials. A series of guest speakers from industry will provide case studies across both semesters.

The programme will be provided predominantly on-campus, with two intensive study weeks in each of the semesters. Other classes may be taken off-campus using the university’s VLE, remote desktop, Adobe Connect and video conferencing systems along with telephone conferencing.

What you will study

Semester 1
Big Data - 20 Credits
Business Intelligent Systems - 20 Credits
Data Analysis and Visualisation - 20 Credits

Semester 2
Analytical Database Models and Design - 20 Credits
Advanced statistics and data mining - 20 credits
MDX - 20 Credits

Semester 3
Data Science Mini Project - 20 credits (for Certificate)
Data Science Research Project - 60 credits

PGCert:
The PGCert is intended for students who have a strong grounding in Business Intelligence and would like to upgrade their knowledge to include topics from the Data Science MSc. The modules are available stand alone for those who want to take their time studying the material and perhaps build up to a PGCert.

The three modules that make up the PGCert are:
Big Data
Advanced Anlaysis
Mini Project

For more information about the content of the course, please visit the course webpage on the School of Computing website.

How you will be assessed

Assessment will be by examination, practical coursework and research project.

Careers

Various job sites now report an increase in jobs carrying the title of data scientist. Other career opportunities are in intelligence analysis, data management/database maintenance, data processing manager, database development and research, business intelligence consultant and more.

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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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The Data Science Program will prepare you to design and build data-driven systems for decision-making in the private or public sector. Read more

The Data Science Program will prepare you to design and build data-driven systems for decision-making in the private or public sector.

Program description

The digital revolution brings with it an explosion of data that carries significant potential value for businesses, science, and society.

As data becomes easily available as never before, so too does its volume grow, and extracting useful quantitative insights becomes more and more challenging. 

The Barcelona GSE Master in Data Science prepares its graduates to design and build data-driven systems for decision-making in the private or public sector, offering a thorough training in predictive, descriptive, and prescriptive analytics.

The curriculum will guide students from modeling and theory to computational practice and cutting edge tools, teaching skills that are in growing global demand. 

Data Science students will be armed with a solid knowledge of statistical and machine learning methods, optimization and computing, and the ability to spot, assess, and seize the opportunity of data-driven value creation.

Students will learn how to apply classroom examples using real data and answering concrete business questions from the perspectives of different industries. Through an independent master's project and the opportunity for industrial practicum work conducted with local businesses, students can have the opportunity to solve actual analytics problems hands-on.

Our courses are taught by leading academics and researchers in the fields of Economics, Operations, and Statistics, as well as experienced professionals from the analytics industry.The program also invites guest speakers and entrepreneurs working at the frontiers of the Data Science.

Degree

Upon successful completion of the program, students will receive a Master Degree in Data Science awarded jointly by Universitat Autònoma de Barcelona (UAB) and Universitat Pompeu Fabra (UPF). The degree requires the successful completion of 60 ECTS (European Credit Transfer System) credits of graduate courses (6 credits are equivalent to a 40 hour course), some compulsory and some elective. The students' final program must be discussed with and approved by the Master Director.

Who hires Data Science Graduates?

  • Consumer Goods, E-commerce, Entertainment, Pharmaceutical, and Telecommunications Industries
  • Logistics and Transportation Industries
  • Finance and Insurance Industry
  • Consulting and Research Organizations
  • Banking and Public Institutions

Examples of recent professional placements:

  • Accenture - Consultant (Barcelona)
  • Agoda - Data Scientist (Bangkok, Thailand)
  • Criteo - Business Intelligence Analyst (Barcelona)
  • Kernel Analytics - Data Scientist (Barcelona)
  • King - Junior Data Scientist (Barcelona)
  • Morgan Stanley - Quantitative Model Developer (Budapest, Hungary)
  • Nine Connections - Machine Learning Developer (Amsterdam, Netherlands)
  • Rhode Island Innovative Policy Lab - Data Scientist (Providence, RI, USA)
  • Stratagem Technologies - Reinforcement Learning Research (London, UK)
  • UNICEF - Monitoring and Evaluation Specialist (Kinshasa, Congo)




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The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science. Read more

The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.

The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.

The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.

The MSc in Data Science provides you with these skills. 

Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.

As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files. 

The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice. 

The programme includes:

  • A firm grounding in the theory of data mining, statistics and machine learning
  • Hands-on practical real world applications such as social media, biomedical data and financial data with Hadoop (used by Yahoo!, Facebook, Google, Twitter, LinkedIn, IBM, Amazon, and many others), R and other specialised software
  • The opportunity to work with real-world software such as Apache

Modules & structure

You will study the following core modules:

You will also choose from an anually approved list of modules which may include:

Skills & careers

Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government. 

The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing. 

This could lead to a variety of potential jobs including: 

  • Data Scientist
  • Data Mining Analyst
  • Big Data Analyst
  • Hadoop Developer
  • NoSQL Database Developer
  • R Programmer
  • Python Programmer
  • Researcher in Data Science and Data Mining

Find out more about employability at Goldsmiths.



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Learning how to turn real-world data sets into tools and useful insights, with the help of software and algorithms. Data plays a role in almost every scientific discipline, business industry or social organisation. Read more

Learning how to turn real-world data sets into tools and useful insights, with the help of software and algorithms.

Data plays a role in almost every scientific discipline, business industry or social organisation. Medical scientists sequence human genomes, astronomers generate terabytes of data per hour with huge telescopes and the police employ seismology-like data models that predict where crimes will occur. And of course, businesses like Google and Amazon are shifting user preference data to fulfil desires we don’t even know we have. There is therefore an urgent need for data scientists in whole array of fields. In the Master’s specialisation in Data Science you’ll learn how to turn data into knowledge with the help of computers and how to translate that knowledge into solutions.

Although this Master’s is an excellent stepping-stone for students with ambitions in research, most of our graduates work as data consultants and data analysts for commercial companies and governmental organisations.

Why study Data Science at Radboud University?

- This specialisation builds on the strong international reputation of the Institute for Computing and Information Sciences (iCIS) in areas such as machine learning, probabilistic modelling, and information retrieval.

- We’re leading in research on legal and privacy aspects of data science and on the impact of data science on society and policy.

- Our approach is pragmatic as well as theoretical. As an academic, we don’t just expect you to understand and make use of the appropriate tools, but also to program and develop your own.

- Because of its relevance to all kinds of different disciplines, we offer our students the chance to take related courses at other departments like at language studies (information retrieval and natural language processing), artificial intelligence (machine learning for cognitive neuroscience), chemistry (pattern recognition and chemometrics) and biophysics (machine learning and optimal control).

- The job opportunities are excellent: some of our students get offered jobs before they’ve even graduated and almost all of our graduates have positions within six months after graduating.

- Exceptional students who choose this specialisation have the opportunity to study for a double degree in Computing Science together with the specialisation in Web and Language Interaction (Artificial Intelligence). This will take three instead of two years.

See the website http://www.ru.nl/masters/datascience

Admission requirements for international students

- A proficiency in English

In order to take part in the programme, you need to have fluency in English, both written and spoken. Non-native speakers of English without a Dutch Bachelor's degree or VWO diploma need one of the following:

- TOEFL score of >575(paper based) or >90 (internet based)

- IELTS score of >6.5

- Cambridge Certificate of Advanced English (CAE) or Certificate of Proficiency in English (CPE), with a mark of C or higher

Career prospects

A professional data scientist has fine problem-solving, analytical, programming, and communication skills. He or she applies those skills to analyse a problem in the light of the available real-world data:

- To come up with a creative and useful solution.

- To find or program the right tool to turn the data into knowledge.

- To communicate the obtained findings to others.

By combining data, computing power and human intellect, data scientists can make a real difference to help and improve our society.

The job perspective for our graduates is excellent. Industry desperately needs data science specialists at an academic level, and thus our graduates have no difficulty in find an interesting and challenging job. A few of our graduates decide to go for a PhD and stay at the university, but most of our students go for a career in industry. They then typically either find a job at a larger company as consultant or data analysis, or start up their own company in data analytics.

Examples of companies where our graduates end up include SMEs like Orikami, Media11 and FlexOne, and multinationals like ING Bank, Philips, ASML, Capgemini, Booking.com and perhaps even Google.

Our approach to this field

Data nowadays plays a role in almost every scientific discipline as well as industry and is rapidly becoming a key driver of scientific discoveries, business innovation, and solutions for societal challenges such as better healthcare. Medical scientists are sequencing and analysing human genomes to uncover clues to infections, cancer, and other diseases. With huge telescopes, astronomers generate terabytes of data per hour to study the formation of galaxies and the evolution of quasars. Businesses like Google and Amazon are sifting social networking and user preference data to fulfill desires we don't even know we have. Police employing seismology-like data models can predict where crimes will occur and prevent them from happening.

It is then with good reason that data science has been called the sexiest job of the 21st century. Many companies complain about the difficulty to find skilled data scientists and predict this to be even harder in the future. A professional data scientist has fine problem-solving, analytical, programming, and communication skills. He or she applies those skills to analyse a problem in the light of the available real-world data, to come up with a creative and useful solution, to find or program the right tool to turn the data into knowledge, and to communicate the obtained findings to others. By combining data, computing power and human intellect, data scientists can make a real difference to help and improve our society.

See the website http://www.ru.nl/masters/datascience



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

Healthcare, with an already established strong relationship with Information & Communication Technologies (ICT), is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.

Processing this data to extract valuable information about a population (epidemiological applications) or the individual (personalised healthcare applications) is the work of health data scientists. Their work has the potential to improve quality of life on a large scale.

Swansea University is the first institution in the UK to offer this taught master's programme in Health Data Science designed to develop the essential skills and knowledge required of the Health Data Scientist.

Key Features of the Health Data Science Programme

- A one year full-time taught master's programme designed to develop the essential skills and knowledge required of the Health Data Scientist.

- The Health Data Science course is also available for three years part-time study.

- An integrated programme of studies tailored to the essential skill set required for Data Scientists operating within healthcare organisations covering key topics in computation, data modeling, visualisation, machine learning and key methodologies in the analysis of linked health data.

- Hands on experiential learning from the professionals behind the Secure Anonymised Information Linkage (SAIL) Databank, a UK-exemplar project for the large scale mining of healthcare data within a secure environment.

- Strong collaboration links with colleagues from the Centre for Health Services Research of the University of Western Australia, a group of leading experts in the analysis of linked health data.

- The Health Data Science course is based within the award winning Centres for Excellence for Administrative Data and eHealth Research of Swansea University, awarded by the Economic and Social Research Council (ESRC) and Medical Research Council (MRC), enhancing the quality of the course.

Who should study MSc Health Data Science?

The Health Data Science course is suitable for those working in healthcare with roles involving the analysis of health data and also computer scientists with experience in working with data from the healthcare domain, as well as biomedical engineers and other similar professions.

Course Structure

Students must complete 6 modules of 20 credits each and produce a 60 credits dissertation on a Health Data Science project. Each module of the programme requires a short period of attendance that is augmented by preparatory and reflective material supplied via the course website before and after attendance.

Attendance Pattern

Health Data Science students are required to attend the University for 1 week (5 consecutive days) for each module in Part One. Attendance during Part Two is negotiated with the supervisor.

Modules

Modules on the Health Data Science programme typically include:

Scientific Computing and Health Care

Health Data Modelling

Introductory Analysis of Linked Health Data

Machine Learning in Healthcare

Health Data Visualisation

Advanced Analysis of Linked Health Data

Professional Development

The College of Medicine offers the modules on the Health Data Science course as standalone opportunities for prospective students to undertake continued professional development (CPD) in the area of Health Data Science.

You can enroll on the individual modules for the Health Data Science programme as either an Associate Student (who will be required to complete the module(s) assessments) or as a Non-Associate Student (who can attend all teaching sessions but will not be required to complete any assessments).

For information and advice on applying for any of the continuing education opportunities, please contact the College directly at .

Employability

Postgraduate study has many benefits, including enhanced employability, career progression, intellectual reward and the opportunity to change direction with a conversion course.

From the moment you arrive in Swansea, specialist staff in Careers and Employability will help you plan and prepare for your future. They will help you identify and develop skills that will enable you to make the most of your postgraduate degree and enhance your career options. The services they offer will ensure that you have the best possible chance of success in the job market.

The student experience at Swansea University offers a wide range of opportunities for personal and professional development through involvement in many aspects of student life.

Co-curricular opportunities to develop employability skills include national and international work experience and study abroad programmes and volunteering, together with students' union and athletic union societies, social and leisure activities.

For the MSc Health Data Science course, we are in the process of identifying opportunities for our students to complete volunteering placements with a number of our collaborative partners.



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Role of a data scientist. We live in a data-driven world that’s generating huge volumes of information at ever-increasing rates, via social media, financial transactions, telecommunications, and even scientific discovery. Read more

Role of a data scientist

We live in a data-driven world that’s generating huge volumes of information at ever-increasing rates, via social media, financial transactions, telecommunications, and even scientific discovery.

The emerging interdisciplinary field of data science combines areas of computer science with mathematical statistics and domain expertise to manage and analyze this data.

Though incorporating statistical methods, data science puts a greater emphasis on the specialized computational skills required to manage and analyze big data from sources such as social media, sensors, mobile and transaction data.

Data scientists develop the capability to derive insight and opportunity from the vast repositories of data that many organizations collect. They help organizations in all sectors of the economy to make sense of these very large volumes of data; enabling businesses to gain a competitive edge, governments to deliver more targeted services, and research teams make new discoveries.

Career outlook

As data science is still a new and emerging field, the roles available for data scientists are quite varied and diverse. As well as the title of data scientist, other positions include: analytics specialist, business intelligence analyst/developer, data analyst, data architect, data engineer, data miner, research scientist and web analyst.

Program structure

The first year of the program develops a solid foundation in computer science and statistics –

core skills necessary for every data scientist in their professional work.

Students with an undergraduate background in computer engineering may gain some advanced standing. During the first year of this master's degree, students with an undergraduate background in Mathematics will take advanced courses in computer science to supplement their knowledge. While students with a bachelor's degree in computer science will follow specialized courses of applied mathematics in order to get ready for the advanced core courses of the second year.  

The second year also includes a major project, which can be working on an industry or research project.

International Students

Find out more about studying as an international student at USJ here: https://www.usj.edu.lb/sri/index_en.php



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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 is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. Read more

Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs.

This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.

The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management.

In the statistics section you study modules on data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.

The computing section covers areas related to data integration, massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes.

The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.

Our partnerships with business inform the course design, ensuring the content is relevant, up to date and meets the needs of industry. These partnerships also enable the inclusion of some leading edge software such as SAS, SAP Hana, and Hadroop within the course. You may be able to study abroad as part of the Erasmus programme.

Key areas of study

Key areas of study include • data quality and analysis • technologies to store and mine data • professionalism and research

Professional recognition

This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam.

Sheffield Hallam is a member of the SAS Student Academy, the SAP Student Academy and founding member of the SAP University Alliance.

Course structure

Full time – September start – typically 12 or 18 months

Part time – September start – typically 36 months

Core modules

  • research skills and principles
  • industrial expertise
  • data integration
  • statistical modelling
  • data mining
  • handling data in the cloud
  • big data and distributed systems
  • social and economic aspects of the cloud
  • advanced statistical modelling
  • dissertation

Options

Choose one from :

  • organisational dynamics
  • social and economic aspects of the cloud

Assessment

  • essays
  • assignments
  • computer-based tests
  • practical projects
  • presentations
  • vivas

Employability

Many jobs for data scientists, data analysts and data mining analysts are available with salaries ranging from £35,000 to £80,000.

Jobs typically list the skills to be in areas such as statistical analysis and machine learning techniques, database and programming technologies, and expertise in statistical theory, which are all areas you cover on this course.

You also gain skills and knowledge in HaDoop, MapReduce, Java, SAS, MSQL which are some of the common technologies used in data scientist roles.



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There is an enormous and increasing amount of data that is collected. Examples include not just traditional data such as sales transactions, but location data (GPS), interactions between people on social network, measurements of sleep patterns, medication being taken, state of health, and much much more. Read more

There is an enormous and increasing amount of data that is collected. Examples include not just traditional data such as sales transactions, but location data (GPS), interactions between people on social network, measurements of sleep patterns, medication being taken, state of health, and much much more.

A key challenge is then to make use of this wealth of data. How can we manage this data, and analyse it to exploit useful information that can guide decision making?

This emerging area goes under the name “Data Science”. There is growing demand for people, “Data Scientists”, who have the skills to manage and analyse enormous amounts of data using a range of techniques such as data mining, statistical techniques, and machine learning.

Data Scientist has been called the “Sexiest job of the 21st century”, and the unique combination of technical skills (stats, data management) and business understanding has been said to make Data Scientists “highly sought after and highly paid”.

Master of Business Data Science (MBusDataSc)

The MBusDataSc primary focus is to equip you to become a practitioner, allowing you to meet the needs of industry, and solve the data problems of the world. However, there will also be an alternative path that will focus on preparing students for research in the area (e.g. going on to do a masters by research or PhD).

The proposed degree is inherently multidisciplinary, featuring Information Science and Marketing, which gives the degree a strong business focus; as well as contributions from Computer Science and from Statistics.

Once you have completed the MBusDataSc you will have developed an advanced knowledge of data science. You will understand how data analysis can be used in business, including being able to identify opportunities to use data, be aware of ethical and privacy issues and possible mitigations, and be able to select appropriate means of presenting the results of analysis. You will be able to select and apply techniques to manage and analyse large collections of data.

Degree Structure

The programme of study shall consist of seven 20 point taught papers together with a 40 point applied project or research project. Papers are either taught in semester one, semester two or are full-year papers. 

You must complete:

INFO 424 - Adaptive Business Intelligence  

COSC 430 - Advanced Database Topics

INFO 411 - Machine Learning and Data Mining

MART 448 - Advanced Business Analytics  

INFO 420 - Statistical Techniques for Data Science

INFO 408 - Management of large scale data

BSNS 401 - The Environment of Business & Economics

Plus one of the following project papers

BSNS 501 - Applied Project  

or

BSNS 580 - Research Project (for students who may wish to progress to PhD study)

Graduate Profile

The University of Otago coursework masters programmes provide you with an opportunity to specialise in advanced study with a focus on either applied practical or academic research.

Graduates of the MBusDataSc will gain skills in three areas: those relating to the business and organisational context, those relating to computing technologies for managing data, and those relating to data analysis techniques.

As a graduate of the MBusDataSc you should be able to:

  1. Understand where data analysis is used in business
  2. Identify opportunities to use data to improve decision making in a business context
  3. Be aware of ethical and privacy implications, concerns, and approaches to mitigating these, including the ability to identify potential areas of concern, and recommend appropriate mitigation actions
  4. Use appropriate means of communicating the results of analysis in graphical form
  5. Develop and maintain databases, including familiarity with performance management for large databases
  6. Have knowledge of a range of data storage and manipulation technologies (such as relational databases, NoSQL, XML), and the ability to select an appropriate technology for a given context and need
  7. Use high performance computing tools (including cloud computing) to manage and analyse data
  8. Be familiar with a range of data analysis approaches (e.g. statistical techniques, data mining, data warehousing), and be able to to select and apply these techniques to suit the context
  9. Develop an appreciation of the concept of ethics from multicultural perspectives, including Māori development aspirations and also an international business environment


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What's the"sexiest job of the 21st century"? According to Harvard Business Review, it's data scientist. A job devoted to giving structure to large quantities of formless data. Ever-changing, ever-challenging big data. Read more

What's the"sexiest job of the 21st century"? According to Harvard Business Review, it's data scientist. A job devoted to giving structure to large quantities of formless data. Ever-changing, ever-challenging big data.

The Master of Data Science (MDS) teaches you how to explore data and discover its potential – how to find innovative solutions to real problems in science, business and government, from technology start-ups to global organisations. With a degree in science, engineering, arts or computing, you can pursue a Master of Data Science, gaining skills in data management, data analytics and data processing – skills needed in this fast-growing field.

The MDS expands your knowledge of the analytical, organisational and computational aspects of data. You learn to manage data and gain an understanding of its impact on society.

The MDS caters to students from a variety of backgrounds by including foundation units in programming, databases and maths or statistics. However, if you have this background from previous studies or work experience, you may accelerate your study with an exemption from these units, or choose to take more data science electives.

The core coursework covers data science objectives, data analysis and data management. You then select data science electives such as applied data analysis, visualisation, data pre-processing, big data handling and data in society. You can also choose to take the Advanced Data Analytics stream where you build deeper skills in data analytics and machine learning.

Our highly regarded faculty takes great pride in developing the most up-to-date material while maintaining a solid core of established theory and platforms, including Python and R (two of the most popular open-source programming languages for data analysis), Hadoop and Spark (for distributed processing). You also gain hands-on experience with state-of-the-art tools and get exposure to key industry players.

In your final semester, you will take part in an Industry Experience team project, working with industry mentors to develop data-driven IT solutions. Or you will undertake a minor-thesis research project, investigating cutting-edge problems under the supervision of internationally recognised researchers.

Students wishing to exit early can complete with a Graduate Certificate or Graduate Diploma of Data Science.

Course structure

The course is structured in three parts, A, B and C. All students complete Part B (core studies). Depending upon prior qualifications, you may receive credit for Part A (foundation studies) or Part C (advanced studies) or a combination of the two.

Note that if you are eligible for credit for prior studies you may elect not to receive the credit.

PART A. Foundations for advanced data science studies

These studies will provide an orientation to the field of data science at graduate level. They are intended for students whose previous qualification is not in a cognate field.

PART B. Core Master's study

These studies draw on best practices within the broad realm of data science practice and research.  You will gain a critical understanding of theoretical and practical issues relating to data science. Your study will focus on your choice either of data science or advanced data analytics.

PART C. Advanced practice

The focus of these studies is professional or scholarly work that can contribute to a portfolio of professional development. You have two options.

The first option is a minor thesis research program, consisting of a research project and a research methods training unit. Students wishing to use this Masters course as a pathway to a higher degree by research should take this first option.

The second option is a program of coursework involving advanced study and an Industry experience studio project.

Students admitted to the course, who have a recognised honours degree in a discipline cognate to data science, will receive credit for Part C, however, should they wish to complete a 24 point research project as part of the course they should consult with the course coordinator.



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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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IN BRIEF. Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area. Read more

IN BRIEF:

  • Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area.
  • Gain SAS certification.
  • Learn to tell a story from data. Become immersed in Big Data techniques and platforms, working with real-world messy data to gain experience across the data science stack.
  • Part-time study option
  • International students can apply

COURSE SUMMARY

Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?      

This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to    a  lack of suitably qualified people. This course is unique in the UK in that it has been developed as a MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem solving,    and  an ambition for a career in data science; then this course is for you.    

During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite  due  to    the  partnership of the University with the SAS Student Academy.  

"We are especially pleased to endorse the new MSc in Data Science. With the explosion of interest and investment in data science teams, our customers cannot get enough graduates with SAS-based analytical skills. Courses such as this new MSc are an important step forward by the University to addressing this skills shortage, especially amongst home students." - SAS

COURSE DETAILS

This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project worth 60 credits. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.

TEACHING

The course is focused around the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for examination. This makes a total of 300 hours total learning experience.

  • Lectures will be used to introduce ideas, and to stimulate group discussions.
  • Tutorials will be used to develop problem solving strategies and to provide practice and feedback with scenarios to help with exam preparation.
  • Workshops will be used to develop expertise in SAS tools, by analysing example datasets of increasing complexity.

ASSESSMENT

  • 50% of the assessment will comprise a practical project where students will be given some data, will devise and carry out an analysis strategy and will present their interpretations and explain their strategy. 
  • 50% will comprise an examination, which will assess more theoretical aspects of the course and will explore students’ immediate response to unseen scenarios or data.

CAREER PROSPECTS

A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”

With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.

FURTHER STUDY

The Informatics Research Centre in the School of Computing, Science and Engineering at the University of Salford builds on the history, success and achievements of the research in Computer Science and Information Systems developed at the University of Salford over the last thirty years.

Evolving around Data and Information in all their types and usages, the Centre covers all phases and processes from data pre-processing to engineering and visualisation. The Centre is developing novel methods and systems for the analysis and recognition of various data sets, learning behaviours and causal models. The techniques and systems developed have a wide range of potential applications including digitisation of historical documents, medical diagnosis, semantic tagging, segmentation of types of viewers and their behaviours, text mining and retrieval and data visualisation.

Forensic computing, digital investigation and Cyber security is another area of expertise supported by the centre both at the theoretical and application levels.

Many students go on to further research in the fields of:

  • Actionable Knowledge Discovery and Semantic Web
  • Software Engineering and applications
  • Big Data, Data Mining and Analytics
  • Image and document processing and analysis
  • Cyber Security and Forensics
  • Information visualisation and virtual environments

FACILITIES

Facilities include a new Dell Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialized in Machine Learning, Data Mining, Statistical Analysis and Big Data including:

  • R, SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner
  • NoSQL databases ie MongoDB


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This intensive programme in data science and software engineering is designed for graduates who are new to computer science and provides an excellent grounding for working as a data scientist or analyst in industry. Read more
This intensive programme in data science and software engineering is designed for graduates who are new to computer science and provides an excellent grounding for working as a data scientist or analyst in industry. You will gain a broad knowledge of computing and acquire programming and data analysis skills, as well as comprehensive, practical problem-solving and analytical skills. You will also critically explore current research and methodologies and have the opportunity to investigate an area of current research in more depth via a project.

If you are new to computer science, this programme provides a solid foundation for a career in IT as a data scientist or analyst. For those already working in IT, the programme is an ideal opportunity to strengthen and update your knowledge and skills in the areas of data science and software engineering, while obtaining a formal Master's qualification.

This programme has been funded by the Higher Education Funding Council for England (HEFCE), as part of an innovative initiative to fund conversion courses in computing and engineering. This course uniquely enables students without any previous computer or data science experience at undergraduate level to study towards a Master's degree in this area of emerging importance. Crucially, the course covers both data science and software engineering, a combination of skills sought after in industry.

Why study this course at Birkbeck?

This programme is ideal if you are new to computer science and want to develop a career in IT as a data scientist or analyst.
Our Department of Computer Science and Information Systems is one of the longest-established in the world - we are celebrating our 60th anniversary in 2017.
We provide a stimulating teaching and research environment, with academic specialists in all fields, including information and knowledge management, web and pervasive technologies, computational intelligence, and information systems development, among others.
Our research dates back to the late 1940s, when one of the first electronic computers was developed at Birkbeck by Dr Andrew Booth. We now house the Computational Intelligence Research Group and the Information Management and Web Technologies Research Group, both of which collaborate with other research groups and with industry, in the UK and abroad, and undertake interdisciplinary research in the life, natural and social sciences, and the humanities.
We are also part of the London Knowledge Lab, a unique collaboration between Birkbeck and the UCL Institute of Education, which brings together computer and social scientists to explore how we learn, the role of technology in this process, and how technology relates to broader social, economic and cultural factors.
In the 2014 Research Excellence Framework (REF), more than 75% of our research outputs in Computer Science were ranked world-leading or internationally excellent.
You will have 24-hour access to several laboratories of networked PCs with a range of language compilers, database and other application software. We are connected, via the SuperJANET network, to the computers of other academic institutions in London, elsewhere in the UK and abroad.

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The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. Read more
The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. This unique course will give you the skills you’ll need to succeed as a Data Engineer.

Why study Data Engineering at Dundee?

The role of “Data Scientist” has been described as the “sexiest job of the 21st Century. However, there is a emerging a new role, that of Data Engineer as more companies are realising they need employees with specific skills to handle the amount of data that is being generated and the coming tidal wave from the Internet of Things.

This MSc has been created with industry input to prepare its students with the skills to handle this wave of data and to be at the forefront of its exploitation. Students on the sister programmes (“Data Science” and “Business Intelligence”) have gone on to work for some of the biggest companies in the industry and we are confident that graduates from this MSc will have the same success.

The School of Computing at the University of Dundee has been successfully offering related MSc programmes such as Business Intelligence and Data Science since 2010. These innovative programmes attract around 40 students per year, drawn from across Europe and Overseas.

What's so good about Data Engineering at Dundee?

Our facilities:
You will have 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

Special features

The University of Dundee has close ties with the Big Data industry, including Teradata, Datastax and Microsoft. We have worked with SAS, Outplay, Tag, GFI Max, BrightSolid and BIPB, and our students have enjoyed guest lectures from Big Data users such as O2, Sainsbury’s, M&S and IBM.

You will be able to work with a range of leading researchers and tutors, including top vision and imaging researchers and BI experts. Our honorary staff include legal experts, entrepreneurs and renowned industry experts such as John Richards of the newly formed IBM Watson Group.

How you will be taught

The course will be taught by staff of the School of Computing. Depending on the modules you take this will include Andy Cobley, Professor Mark Whitehorn, and Professor Stephen McKenna.

What you will study

The course will be taught in 20 credit modules with a 60 credit dissertation. Students will require to complete 180 credits for the award of the MSc (including 60 credits for the dissertation). Students completing 120 credits (without the dissertation) will be eligible for a Postgraduate Diploma.

Course content

Each module on the course is designed to give the student the skills and understanding they need to succeed in the Data Engineering/ Science field. Content on the course includes (but is not limited to):

CAP theorem
Lamda Architecture
Cassandra, Neo4j and other nosql databases
The Storm distributed real time computation system
Hadoop, HDFS, MapReduce, and other Hadoop/SQL technologies
Spark and Shark frameworks
Data Engineering languages such as Python, erlang, R, Matlab
Vision systems, which are becoming increasingly important in data engineering for extracting features from large quantities of images such as from traffic, medical and industrial
RDBMS systems which will continue to play an important role in data handing and storage. You will be expected to research the history of RDMBS and delve in to the internals of modern systems
OLAP cubes and Business Intelligence systems, which can be the best and quickest way to extract information from data stores
Goals of machine learning and data mining
Clustering: K-means, mixture models, hierarchical
Dimensionality reduction and visualisation
Inference: Bayes, MCMC
Perceptrons, logistic regression, neural networks
Max-margin methods (SVMs)
Mining association rules
Bayesian networks

How you will be assessed

The course is assessed through a combination of examinations, coursework, presentations and interviews. Each module is different: for instance the Big data module has 40% coursework, consisting of Erlang programming and a presentation on nosql databases, along with an examination worth 60%.

Careers

Our experience suggests that graduates of this course will have most impact in the following areas:

Cloud and web based industries that handle large volumes of fast moving data that need to be stored, analysed and maintained. Examples include the publishing industry (paper, TV and internet), messaging services, data aggregators and advertising services

Internet of Things. A large amount of data is being generated by devices (robotic assembly lines, home power management, sensors etc.) all of which needs to be stored and analysed.

Health. The NHS (and others) are starting to store and analyse patient data on an unprecedented scale. The healthcare industry is also combining data sources from a large number of databases to improve patient well-being and health outcomes

Games industry. The games industry records an extraordinary amount of data about its customers' play activities, all of which needs to be stored and analysed. This course will equip students with the knowledge and skill to engage with the industry.

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