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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.
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 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
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.
- 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
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.
- 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
- 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
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.
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
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.
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.
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.
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:
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.
The Big Data in Business pathway will provide you with the knowledge and skills to understand and direct the strategic use of the vast amounts of information being generated by businesses today.
Our students learn to develop a strategic approach to managing Big Data in business, through the analysis of business problems as well as understanding different approaches to business intelligence. Through this, they are able to create usable business intelligence to create competitive advantage for their organisation.
After you’ve graduated
Our graduates have will leave us with the knowledge and skills necessary to analyse and manage Big Data to benefit business in a variety of sectors.
Not sure which pathway to choose from 3 choices? Apply for the one that you feel fits you better and you will be able to change the pathway within the first few weeks from your arrival to the university.
In addition students must choose two optional module from the list below.
Please note there is no guarantee that in any one year all modules will be available.
A holistic approach
Effective leadership requires more than first-class business acumen. It also requires a degree of self-awareness and sensitivity. Henley is renowned for its well-researched, professional approach to this aspect of business education and all our postgraduate programmes examine this aspect of leadership - helping to create emotionally intelligent graduates who can be fully effective in their chosen careers.
How you will learn
Henley Business School enjoys a strong reputation for the practical application of business ideas and concepts, underpinned by academic excellence and the strength of our research. We offer high-quality technical skills training as well as a deep understanding of the importance of personal development for leaders, a thread that runs through all of our Masters programmes.
Our postgraduate masters programmes feature a mix of core and optional modules, allowing you to tailor your degree towards your individual personal development needs and career ambitions. You will complete up to 10 taught modules during your programme, totalling 180 credits. One module usually equates to 20 credits or 10 hours of work per week. Your week will include lectures, tutorials, workshops and personal study, with each accounting for 25% of your time on average. This stimulating mix of lectures and interactive tutorials provides you with the opportunity to discuss and explore the subject material in depth with your lecturers and fellow students. You will be introduced to the latest thinking and research findings and be able to challenge some of those that have created it. You will also explore real-world issues and tackle current business challenges, and interact with guest lectures and speakers from industry, giving you the opportunity to test, extend and refine your knowledge and skills.
How we assess you
You will learn and be assessed through a wide variety of teaching methods which vary depending on your chosen Masters programme. These include online materials and multimedia content, guest lectures, individual and group assignments, case studies, field visits, dealing room simulations, presentations, applied projects, consultancy work and examinations.
On average examinations form around 70% of the assessed work with the remaining 30% coming from coursework, including a written dissertation or project depending on your chosen programme. The exam period falls between April and June in the summer term, with students taking an average of 5 or 6 exams. Graduation normally takes place in December.
While postgraduate students are self-motivated and determined individuals, study at this level can present additional pressures which we take seriously. Lecturers are available to discuss the content of each module and your personal tutor can meet with you regularly to discuss any additional issues. Full-time support staff are also available to help with any questions or issues that may arise during your time at Henley
Each pathway of our MSc Information Management is designed to give a rigorous academic understanding of real-life and current business issues. Graduates of the Big Data in Business pathway will be equipped to develop strategies to manage Big Data. These skills are much in demand, in a variety of fields.
A number of our students join our PhD programmes each year.
Students who pass the module – Business Domain and Requirements Analysis with a mark of 60 or above will be eligible for the British Computer Society Professional Certificate in Business Analysis Practice.
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.
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.
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:
Exploiting this vast amount of data requires the following:
Therefore the program has three objectives:
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.
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.
Whether you are looking to start a career in data science or wanting to further develop your current career, our innovative online Masters programme in Data Analytics provides you with vital data science skills. This skills-based, yet rigorous curriculum provides you both with a thorough foundation in the underlying principles of learning from data and practical technical expertise in data handling, visualisation and modelling. The programme uses cutting-edge learning technology to deliver an interactive and collaborative online learning experience. Community building and collaborative learning is a key focus of our online delivery and you will be encouraged and supported to interact with your fellow classmates and tutors in a variety of ways throughout the duration of the course.
This flexible part-time programme is completed over three years. In the first two years you will be taking two courses each trimester. In the third year you will be working on a project and dissertation.
The courses are designed to allow you to work at your own pace, with milestones and assessment to be completed according to an agreed timetable.
You will also carry out a 60 credit research project.
In the first year of the programme you will need to take three paper-based examinations, held on the second Monday of May and the following Tuesday. UK-based students will have to take these examinations in Glasgow. Students from abroad can choose to either travel to Glasgow or take the examination in a local test centre, such as British Council offices. Test centres are subject to approval by the University and the candidate is responsible for any local fees charged by the test centre.
Data is becoming an ever increasing part of the modern world, yet the talent to extract information and value from complex data is scarce. There is a massive shortage of data-analytical skills in the workforce. Statistical Analysis and Data Mining was listed by LinkedIn as the hottest skill in 2014 and came second in 2015 and 2016. This programme opens up a multitude of career opportunities and/or boosts your career trajectory.
Graduates from the programmes in our School have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance, business consulting and government statistical services, while others have continued on to a PhD.
The Energy Systems and Data Analytics MSc provides an academically leading and industrially relevant study of energy systems through the lens of data analytics. Advanced analytics, fuelled by big data and massive computational power, has the potential to transform how energy systems are designed, operated and maintained. You will gain the skills and knowledge to unlock the transformative potential of big energy data, and understand how it can reshape the energy sector.
You will gain a broad understanding of energy systems as a whole, covering supply and demand, the interconnectedness and dependencies between different sectors and a multi-vector multi-sector approach to analysis. You will learn about the theory and practice of data analysis and will gain practical experience of the challenges of working with different data sets relating to energy throughout the programme and modules.
The programme consists of five compulsory modules (75 credits), two optional modules (45 credits) and a dissertation (60 credits).
The list of optional modules is correct for the 2018/19 academic year. Enrolment on modules is subject to availability.
All students undertake an independent research project whch culminates in a 10,000-word dissertation.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials, problem-based learning and project work. Assessment is through a combination of methods including problem sets, individual assignments and coursework, group based design tasks with a report and presentation, unseen examinations and a dissertation.
Further information on modules and degree structure is available on the department website: Energy Systems and Data Analytics MSc
Graduates of the ESDA MSc will be ideally placed to gain employment as energy analysts/ data scientists in consultancies, utilities, innovative start-ups and government institutions which value expertise in energy systems and have a need for data literate analysts.
There is a strong emphasis placed on innovation throughout the programme. Based on our market research and the trends in the industry (which is increasingly driven by data) there will be a healthy demand for our graduates.
Students will also benefit from a skill set in data analytics that will be highly transferable and applicable across a range of industries and domains.
The programme has been developed with input from industry leaders. You will gain exposure to real life energy and sustainability challenges.
The MSc in Energy Systems and Data Analytics is the first programme in the UK to combine the study of energy systems with data science. The MSc is delivered by leading researchers in the UCL Energy Institute and UCL Institute for Sustainable Resources. You will benefit from their specific expertise, research communities and industry contacts (including guest lecturers drawn from the energy industry), as well as our multidisciplinary and cross-domain approach.
The UCL Energy Institute has consulted across industry to identify key skills gaps for the energy analysts that will be required by utilities, consultancies and small and medium enterprises. There is a growing need in industry for graduates who combine an understanding of energy systems with the skills and abilities to extract insights from data through the use of advanced analytics.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.