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Masters Degrees (Data Science)

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Goal of the pro­gramme. Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Read more

Goal of the pro­gramme

Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Data scientists help organisations make sense of their data. As data is collected and analysed in all areas of society, demand for professional data scientists is high and will grow higher. The emerging Internet of Things, for instance, will produce a whole new range of problems and opportunities in data analysis.

In the Data Science master’s programme, you will gain a solid understanding of the methods used in data science. You will learn not only to apply data science: you will acquire insight into how and why methods work so you will be able to construct solutions to new challenges in data science. In the Data Science master’s programme, you will also be able to work on problems specific to a scientific discipline and to combine domain knowledge with the latest data analysis methods and tools. The teachers of the programme are themselves active data science researchers, and the programme is heavily based on first-hand research experience.

Upon graduating from the Data Science MSc programme, you will have solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills. In particular, you will be able to

  • Understand the general computational and probabilistic principles underlying modern machine learning and data mining algorithms
  • Apply various computational and statistical methods to analyse scientific and business data
  • Assess the suitability of each method for the purpose of data collection and use
  • Implement state-of-the-art machine learning solutions efficiently using high-performance computing platforms
  • Undertake creative work, making systematic use of investigation or experimentation, to discover new knowledge
  • Report results in a clear and understandable manner
  • Analyse scientific and industrial data to devise new applications and support decision making.

The MSc programme is offered jointly by the Department of Computer Science, the Department of Mathematics and Statistics, and the Department of Physics, with support from the Helsinki Institute for Information Technology (HIIT) and the Helsinki Institute of Physics (HIP), all located on the Kumpula Science campus. In your applied data science studies you can also include multidisciplinary studies from other master's programmes, such as digital humanities, and natural and medical sciences.

Further information about the studies on the Master's programme website.

Pro­gramme con­tents

The Data Science MSc programme combines elements from computer science and mathematical sciences to provide you with skills in topics such as machine learning, distributed systems and statistical methods. You might also find that knowledge in a particular scientific field is useful for your future career. You can obtain this through elective studies in the MSc programme, or it might already be part of your bachelor-level degree.

Studies in the Data Science MSc programme include both theoretical and practical components, including a variety of study methods (lectures, exercises, projects, seminars; done both individually and in groups). Especially in applied data science, we also use problem-based learning methods, so that you can address real-world issues. You will also practise academic skills such as scientific writing and oral presentation throughout your studies. You are encouraged to include an internship in your degree in order to obtain practical experience in the field.

Elective studies give you a wider perspective of Data Science. Your elective studies can be an application area of Data Science (such as physics or the humanities), a discipline that supports application of Data Science (such as language technology), or a methodological subject needed for the development of new Data Science methods and models (such as computer science, statistics, or mathematics).



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

Visit the website http://www.study.monash/courses/find-a-course/2016/data-science-c6004?domestic=true

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 program of coursework involving advanced study and an Industry experience studio project.

The second option is a research pathway including a thesis. Students wishing to use this Masters course as a pathway to a higher degree by research should take this second option.

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.

For more information visit the faculty website - http://www.study.monash/media/links/faculty-websites/information-technology

Find out how to apply here - http://www.study.monash/courses/find-a-course/2016/data-science-c6004?domestic=true#making-the-application

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Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier. Read more

About the course

Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier.

The Data Science and Analytics MSc programme provides these skills, combining a strong academic programme with hands-on experience of leading commercial technology – and the chance to gain industry certification.

You will develop both your critical awareness of the state-of-the-art in data science and the practical skills that help you apply data science more effectively in the business, science and social world.

The programme is run in conjunction with SAS, a market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.

Brunel's programme is unique in being the only current MSc programme that is fully integrated with SAS, providing the SAS base certification.

Aims

The Harvard Business Review calls data science the “sexiest job of the 21st century” – with demand for graduates with SAS skills rapidly rising across financial, retail and government sectors. Data science is now in vogue.

From government, social networks and ecommerce sites to sensors, smart meters and mobile networks, data is being collected at an unprecedented speed and scale – creating an expanding job market for qualified data analysts.

The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). This experience is designed, in part, to develop skills in preparation for the SAS certification part of the programme.

By the end of the course you should be able to:

Comprehend the key concepts and nuances of the disciplines that need to be synthesised for effective data science.
Demonstrate a critical understanding of the challenges and issues arising from taking heterogeneous data at volume and scale, understanding what it represents and turning that understanding into insight for business, scientific or social innovation (i.e. data science).
Develop a practical understanding of the skills, tools and techniques necessary for the effective application of data science.
Apply a practical understanding of data science to problems in social, business and scientific domains.
Evaluate the effectiveness of applied data science in relation to the issues addressed.

Course Content

Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Typical Modules:

Digital Innovation
Quantitative Data Analysis
High Performance Computational Infrastructures
Systems Project Management
Big Data Analytics
Research Methods
Data Visualisation
Learning Development Project
Dissertation

Special Features

SAS Certification
As an integral part of the programme, you will gain hands-on experience of commercial SAS tools – SAS being the market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
You will have the opportunity to obtain SAS certification (e.g. SAS Base Programming) which is a recognised industry qualification, following a two week SAS certification ‘boot camp’ preparation course.

Women in Engineering and Computing Programme

Brunel’s Women in Engineering and Computing mentoring scheme provides our female students with invaluable help and support from their industry mentors.

Teaching

Module are typically presented in a mixture of lecture and seminar/lab format. However, where appropriate other teaching methods will also be incorporated. All our learning environments are supported by the market leader in Virtual Learning Environments (VLE), the BlackboardLearn system.

Assessment

Your learning will be evaluated through a combination of in module assessments and more traditional exams, with module specific assessments – for example, presentations within the Learning Development Project.

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April 2018. Please note that this course has been in high demand and is not currently accepting additional applications for 2018 entry. Read more

April 2018: Please note that this course has been in high demand and is not currently accepting additional applications for 2018 entry. We suggest you consider our Urban Informatics MSc course, which is still accepting 2018 applications. (Urban Informatics is a growing discipline which uses data to understand how cities function and to influence their growth)

The Data Science MSc is an interdisciplinary study programme that will provide you with advanced technical and practical skills in the collection, collation, curation and analysis of data. It also examines the professional, legal and ethical responsibilities of data scientists. This is an ideal study pathway for graduates with a background in quantitative subjects, or who possess relevant work experience in the current methods and techniques of data science.

  • Located in central London, giving access to major libraries and leading scientific societies, including the Chartered Institute for IT (BCS), and the Institution of Engineering and Technology (IET).
  • You will gain an in-depth understanding of the general principles of the computational and statistical approaches and methods used in data science, as well as their underlying assumptions and limitations.
  • Frequent access to speakers of international repute through seminars and external lectures, enabling you to keep abreast of emerging knowledge in advanced computing and related fields.
  • Exposure to interdisciplinary aspects of Data Science through opportunities to interact with multiple departments and faculties across King's diverse campuses
  • The Department of Informatics has a reputation for delivering research-led teaching and project supervision from leading experts in their field.

Description

The Data Science MSc degree will provide you with the practical skills needed to effectively assemble, collate, store, manage and analyse data required for data science projects and the critical judgement to decide the appropriate statistical and computational data modelling and analysis techniques to evaluate data science activities and projects. You will study the computational approaches and techniques used to examine mathematical statistics, as well as developing an appreciation for the professional, ethical and legal responsibilities of the data scientist, along with standard conceptual or scientific models in at least one domain of application of data science. You will complete the course in one year, studying September to September and taking a combination of required and optional modules totalling 180 credits, including 60 credits that will come from a research project and dissertation.

Course purpose

The purpose of this degree programme is to train graduates from quantitative disciplines or with relevant quantitative work experience in current methods and techniques of data science, particularly the science of large-scale data collections. These methods and techniques include both computational techniques and methods from mathematical statistics. The MSc will also provide you with an appreciation for the professional, ethical and legal responsibilities of the data scientist, along with standard conceptual or scientific models in at least one domain of application of data science. Your individual project will typically aim to apply these methods to a problem in a specific application domain, and provide valuable preparation for a career in research or industry.

Course format and assessment

Lectures; tutorials; seminars; laboratory sessions; optional career planning workshops. Assessed through: coursework; written examinations; final project report.

Career destinations

Via the Department’s Careers Programme, students are able to network with top employers and obtain advice on how to enhance career prospects.

Sign up for more information. Email now

Have a question about applying to King’s? Email now



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

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

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

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

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

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

What you'll learn

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

Topics covered in the course include:

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

Your development

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

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

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

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

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

Project work

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

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

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

 You can develop your project:

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

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

Delivery

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

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

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

  • professionalism
  • legislation
  • ethics.

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

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

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

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

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

Facilities

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

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

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



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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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EMBRACE DATA-INTENSIVE SCIENCE AND HELP REVOLUTIONISE HEALTH CARE. There is a big demand for applied data scientists in all disciplines in society, where data is being used for research, decision-making and customer interaction. Read more

EMBRACE DATA-INTENSIVE SCIENCE AND HELP REVOLUTIONISE HEALTH CARE

There is a big demand for applied data scientists in all disciplines in society, where data is being used for research, decision-making and customer interaction. Data scientist is even being refered to as ‘the sexiest job of the 21st century’.

Do you have an obvious interest in data science and eagerness to apply this science within the health domain? The Applied Data Science Postgraduate Master’s programme trains you to be an expert in current and upcoming data science methods and techniques. You will be doing the full cycle of knowledge discovery research, both by following relevant courses and doing a research project.

STUDYING THE INTERSECTION OF DATA SCIENCE AND HEALTH

This Master’s programme is for those who have a strong interest in, and affinity for, performing application-oriented research and the implementation of data science in the field of health. Our study programme enables you to apply state-of-the-art scientific concepts and techniques in the growing field of data analytics. You will prepare and organize data analytics in health care projects and contribute to innovation in the field of data science.

WHY YOU SHOULD JOIN OUR MASTER’S PROGRAMME

  1. This is the first postgraduate Master’s programme in the Netherlands that does not focus mainly on business analytics, but on the application of data science in the field of health. A fast growing area in which data are quickly becoming increasingly important.
  2. Applied Data Science provides for an active data science community in which students, researchers, and data scientists from a range of companies will regularly meet and cooperate. You get a chance to join the frontrunners in a growing field.
  3. It directly leverages the top research between three UU faculties: the Faculty of Science, the Faculty of Social and Behavioural Sciences and the Faculty of Medicine.
  4. Good career prospects. There is a growing demand for data specialists since data is increasingly important in many branches of economy.


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This programme has been designed in collaboration with representatives from industry and local government to ensure that students possess knowledge and skills that are highly valued by employers. Read more

This programme has been designed in collaboration with representatives from industry and local government to ensure that students possess knowledge and skills that are highly valued by employers. The degree specifically addresses the need for graduates who have a good understanding of spatial data and the more technical aspects of Geographical Information Science and Systems (GIS) including: 

  • The role of spatial data and information systems in the context of the research literature and current industry practice
  • Expertise in spatial data collection
  • Management and analysis
  • High-level technical abilities such as programming and spatial data analytics.

Students will critically evaluate the role of spatial data and information systems in the context of the research literature and current industry practice. Students will be able to demonstrate expertise in spatial data collection, management and analysis with the use of specific GIS software such as QGIS and ArcGIS. High-level technical abilities such as programming and spatial data analytics will also be taught. The completion of an independent research project will allow students to showcase their organisational and management skills in addition to being able to critically evaluate and synthesize new and emerging concepts and techniques from a wide range of research literature.

Collaborations with local industry and government will allow students to develop interpersonal skills in addition to an understanding and experience of the relevant professional, legal, social and ethical frameworks that they will need to adhere to as professionals within the area of spatial data science.

Full time

Year 1

Students are required to study the following compulsory courses.

Students are required to choose 15 credits from this list of options.

Part time

Year 1

Students are required to study the following compulsory courses.

Year 2

Students are required to study the following compulsory courses.

Students are required to choose 15 credits from this list of options.

Assessment

Assessment for each course will be various forms of continuous assessment as described in the course specifications. The continuous assessment for each course will involve an appropriate combination of coursework, presentations, peer assessment, practical work, group work and log books.

Careers

Possible jobs for graduates could include GIS graduate consultant, spatial analyst, GIS project manager, GIS developer and data curator. Students could also go on to further research opportunities (e.g. a PhD).



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Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data; and the application and use of those principles. Read more

Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data; and the application and use of those principles. Large data sets are now generated by almost every activity in science, society, and commerce - ranging from molecular biology to social media, from sustainable energy to health care.

As an MSc Data Science student you will explore how to efficiently find patterns in these vast streams of data. Many research areas have tackled parts of this problem. Machine learning focuses on finding patterns and making predictions from data; ideas from algorithms and databases are required to build systems that scale to big data streams; and separate research areas have grown around different types of unstructured data such as text, images, sensor data, video, and speech.

Programme structure

You follow two taught semesters of lectures, tutorials, project work and written assignments, after which you will learn research methods before individual supervision for your project and dissertation.

Compulsory courses

  • Informatics Research Review
  • Informatics Project Proposal
  • Dissertation

You are also required to take a breadth of courses in data science, with at least one in each of the following areas:

  • Machine Learning, Statistics and Optimization
  • Databases and Data Management
  • Applications

You can take up to two courses from other schools.

Learning outcomes

The School of Informatics' MSc in Data Science is designed to attract students who want to establish a career as a data scientist in industry or the public sector, as well as students who want to explore the area prior to further training such as in our CDT in Data Science.

The learning objectives of the degree are to foster:

  • A breadth of knowledge across the data science areas
  • An advanced technical background in at least one of the data science areas
  • An appreciation for real-world problems involving the use of data in industry, science, and the public sector
  • Research experience in one of the data science areas.

Career opportunities

You will develop specialist, advanced skills in data science methods and their applications. You will gain practical experience and a thorough theoretical understanding of the field, making you attractive to a wide range of employers or preparing you for further academic study.



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

Who is it for?

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

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

Objectives

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

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

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

Accreditation

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

Internships

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

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

Teaching and learning

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

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:

  • present and exemplify the concepts underpinning a particular subject
  • highlight the most significant aspects of the syllabus
  • indicate additional topics and resources for private study.

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

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

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

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

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

Career prospects

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

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

Career & Skills Development Service at City, University of London

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



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From business and finance or health and medicine, to infrastructure or education, data science plays a vital role in all aspects of the modern world. Read more

From business and finance or health and medicine, to infrastructure or education, data science plays a vital role in all aspects of the modern world. Our MSc programme will ensure you have an advanced level of skills, knowledge, and experience to achieve your career aspirations.

Studying for an MSc in Data Science at Lancaster will provide you with the perfect environment to develop an expertise in the discipline. Your study will build upon the fundamentals, and our specialist pathways will allow you to practise and enhance technical skills, while gaining professional knowledge that will support and advance your career aspirations.

Over the year, you will explore five core Data Science modules. These will ensure you have a solid advanced grounding in the subject, to support your choice of specialism.

You can choose from two specialisms according to your background and interests:

  • Computing
  • Statistical Inference

In taking one of these routes, you will gain access to a range of exciting, advanced pathway-specific modules. These modules will allow you to either enhance your understanding of data science technologies; or to gain expertise in the application of data science to business intelligence, bioinformatics, population health, the environment, or the study of society. Our specialist modules will provide you with detailed, expert knowledge and will enhance your employability. This format means that you will be equipped to apply for any data science related career, while providing you with an advantage in many industries.

In addition to these taught modules, you will also have the opportunity to undertake a 12-week placement either within industry or as part of an academic research project. This will provide you with a fantastic opportunity to apply your skills and knowledge to real-world situations and challenges, allowing you to gain valuable professional experience and demonstrate a working grasp of the discipline.

The placement project represents a substantial, independent research project. Supervised by an academic, you will develop your ability to gather and analyse data, draw valuable conclusions, and present findings in a professional environment. This research will be an opportunity to bring together everything you have learnt over the year, exercise your ability to solve problems and manage a significant project. This will be great experience for you to draw upon in an interview and in your career.

Course Structure

You will study a range of modules as part of your course, some examples of which are listed below.

Core

Optional

Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.

Assessment

We offer an excellent range of learning environments, which include traditional lectures, laboratories, and workshops. We are also committed to providing timely feedback for all submitted work and projects.

Assessment varies across modules, allowing students to demonstrate their capabilities in a range of ways, including laboratory reports, essays, exercises, literature reviews, short tests, poster sessions, oral presentations, and formal examination.

Community

We have a great relationship with our students and alumni, who have praised the School for its ambition, positivity and friendly atmosphere. By providing a number of support methods, accessible at any stage of your degree, we strive to give our students the best opportunity to fulfil their potential and attract the very best opportunities for a successful career. Our academics are welcoming and helpful; you will be assigned an academic advisor who can offer advice and recommended reading; and our open door policy has been a popular feature among our students. We believe in encouraging and inspiring our computing and communications scientists of the future.

Career

The gathering, interpretation and evaluation of data is fundamental to all aspects of modern life. As a result, data science can lead to a career in a wide range of industries. The core modules of this programme will ensure you are properly equipped to apply yourself to any data role, while your specialist pathway will enhance your opportunities in specific industries, should that be the route you wish to pursue.

Studying at Masters level will further enhance your career prospects, opening up opportunities to progress further in your career.

In addition, many of our Data Scientists also elect to study a PhD qualification.

  • We provide careers advice and host a range of events throughout the year, including our annual careers fair, attended by exhibitors who are interested in providing placements and vacancies to computer science students and graduates. You can speak face-to-face with employers such as Network Rail, Oracle, and Johnson and Johnson, in addition to a large range of SMEs.


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The UCL programme in Data Science for Research in Health and Biomedicine covers computational and statistical methods as applied to problems in data-intensive medical research. Read more

The UCL programme in Data Science for Research in Health and Biomedicine covers computational and statistical methods as applied to problems in data-intensive medical research. Students learn techniques that are transforming medical research and creating exciting new commercial opportunities. Our recent graduates, many of whom begin paid internships while completing the MSc, have moved on to roles in industry and academia.

About this degree

Students learn how to link and analyse large complex datasets. They design and carry out complex and innovative clinical research studies that take advantage of the increasing amount of available data about the health, behaviour and genetic make-up of small and large populations. The content is drawn from epidemiology, computer science, statistics and other fields, including genetics.

Students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), three optional modules (45 credits) and a dissertation/report (60 credits).

A Postgraduate Diploma (120 credits) is offered.

A Postgraduate Certificate (60 credits) is offered.

Core modules

  • Principles of Epidemiology Applied to Electronic Health Records Research
  • Data Management for Health Research
  • Statistics for Epidemiology and Public Health
  • Statistical Methods in Epidemiology
  • Topics in Health Data Science

Optional modules

  • Advanced Statistics for Records Research
  • Database Systems
  • Information Retrieval and Data Mining
  • Principles of Health Informatics
  • Machine Learning in Healthcare and Biomedicine
  • Statistics for Interpreting Genetic Data
  • Electronic Health Records
  • Clinical Decision Support Systems

Dissertation/report

All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).

Teaching and learning

The programme is delivered by clinicians, statisticians and computer scientists from UCL, including leading figures in data science. We use a combination of lectures, practical classes and seminars. A mixture of assessment methods is used including examinations and coursework.

Further information on modules and degree structure is available on the department website: Data Science for Research in Health and Biomedicine MSc

Careers

Students on this programme will be passionate about research and know that, in the 21st century, some of the most exciting, stimulating and productive research is carried out using large collections of data acquired in big collaborative endeavours or major public or private initiatives. We hope that graduates will build on that passion and, together with the experience gained on the programme, will go one to develop careers as entrepreneurs, scientists and managers, working in industry, academia and healthcare.

Employability

The programme is designed to meet a need, identified by the funders of health research and by a number of industrial organisations and healthcare agencies, for training in the creation, management and analysis of large datasets. This programme is practical, cross-disciplinary and closely linked to cutting-edge research and practice at UCL and UCL’s partner organisations. Data science is arguably the most rapidly growing field of employment at the moment and employers recruiting in health data science include government agencies, technology companies, consulting and research firms as well as scientific organisations. A number of employers are supporting the programme in different ways, including providing paid internships to selected students.

Why study this degree at UCL?

Data science is an exciting area with a dynamic job market, including in healthcare. Our graduates have gone on to work for a range of companies, including large research organisations and small start-ups, while others are working in health care or pursuing their interests in universities.

The lecturers on this programme are international experts in health data science and students will learn about cutting-edge research projects. The collaboration is part of the Farr Institute, a network of centres of excellence created to enhance the UK’s strength in data-intensive research. This MSc will draw on that collaboration, giving students access to the most advanced research in the field.

We work closely with a range of employing organisations to ensure that our graduates have the best possible preparation for a career in data science. This includes offering industry-sponsored dissertations for selected students.

Research Excellence Framework (REF)

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.



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