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Masters Degrees (Data Science And Analytics)

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The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. Read more
The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop skills in database management, programming, summarisation, modelling and interpretation of data. The programme provides graduates with an opportunity, through development of a research project, to investigate the more applied elements of the disciplines.

Visit the website: http://www.ucc.ie/en/ckr49/

Course Details

The MSc in Data Science and Analytics is a significant collaboration between the Departments of Computer Science and Statistics; designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever increasing and complex data. The programme emphasises the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making.

Format

A typical 5 credit module:
• 2 lecture hours per week
• 1–2 hours of practicals per week
• Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.

Structure

Students must attain 90 credits through a combination of:

- Core Modules (30 credits)
- Elective Modules (30 credits)
- Dissertation (30 credits)

Part 1 (60 credits)

- Core Modules (30 credits) -

CS6405 Data Mining (5 credits) - Dr. Marc Van Dongen
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (5 credits)

- Database Modules -

Students who have adequate database experience take:

CS6408 Database Technology (5 credits) - Mr. Humphrey Sorensen
CS6409 Information Storage and Retrieval (5 credits) - Mr. Humphrey Sorensen

- Students who have not studied databases take:

CS6503 Introduction to Relational Databases (5 credits)
CS6505 Database Design and Administration (5 credits)

Elective Modules (30 credits)

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

CS6322 Optimisation (5 credits) - Dr. Steve Prestwich
CS6323 Analysis of Networks and Complex Systems (5 credits) - Prof. Gregory Provan
CS6509 Internet Computing for Data Science (5 credits)
ST6032 Stochastic Modelling Techniques (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)

- Programming Modules -

Students who have adequate programming experience take:

CS6406 Large-Scale Application Development and Integration l (5 credits) - Professor Gregory Provan
CS4607 Large-Scale Application Development and Integration ll (5 credits) - Professor Gregory Provan

- Students who have not studied programming take:

CS6506 Programming in Python (5 credits)
CS6507 Programme in Python with Data Science and Applications (5 credits) - Dr. Kieran Herley

Part 2 (30 credits)

Students select one of the following modules:

CS6500 Dissertation in Data Analytics (30 credits)
ST6090 Dissertation in Data Analytics (30 credits)

Assessment

Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2015 Book and for each module in the Book of Modules 2015/2016 - http://www.ucc.ie/modules/

Postgraduate Diploma in Data Science and Analytics

Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.

Careers

This programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting Computer Science graduates in 2014-15 include:

Accenture, Aer Lingus, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Cisco, CiTi-Technology, Cloudreach, Dell, Digital Turbine Asia Pacific, EMC, Enterprise Ireland, Ericsson, First Derivatives, Guidewire, IBM, Intel, Open Text, Paddy Power, Pilz, PWC, SAP Galway Transverse Technologies, Trend Micro, Uniwink, Version 1 (Software).

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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

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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We are surrounded by data. The variety and amount we collect and store grows every day, from the simplest of retail transactions to the complex and intimate medical records of millions. Read more
We are surrounded by data. The variety and amount we collect and store grows every day, from the simplest of retail transactions to the complex and intimate medical records of millions.

There is increasing demand for individuals who can manage and control the way data is used. These individuals require an understanding of computer science and maths as well as a range of sector specific skills.

The emerging era of ‘big data’ brought about by the digital technology revolution shows no signs of abating. In this era, demand for data scientists will continue to grow, with a report from e-skills UK predicting the generation of approximately 28,000 data science jobs opportunities each year by 2017.

There are a broad range of job opportunities which require data science skills, including Business Analyst, Business Intelligence Analyst, Data Scientist, Data Engineer, Data Manager, Data Analyst, Data Architect and Data Modelling and Data Mining Engineer.

The course

This Data Science and Analytics MSc is a highly flexible course, with a wide range of option modules taught by research-active academics. The course combines expertise from our School of Mathematics, School of Computing, School of Business, School of Geography, and the Yorkshire Centre for Health Informatics. This collaboration allows you to benefit from a range of data science perspectives and applications, supporting you to tailor your learning to your career ambitions.

You’ll be supported to develop a range of skills, including analysing structured and unstructured data, analysing large datasets, and critically evaluating results in context.

Course structure

The first two semesters of your course will consist of taught modules, and in the third semester you will devote your time to a dissertation in data science.

Within each semester there is one compulsory module and a wide range of optional modules spanning the areas of mathematics, computing, business, health care and geography. The aim is to support you in developing your understanding of computer science and mathematics, with specific pathways in business management, health care and geographic information systems (GIS), allowing you to tailor the programme to your ambitions.

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Our MSc in Data Science and Analytics aims to provide you with a comprehensive set of skills needed to handle, collect, store and analyse large and complex sets of data. Read more
Our MSc in Data Science and Analytics aims to provide you with a comprehensive set of skills needed to handle, collect, store and analyse large and complex sets of data. You will be taught by subject experts from both the School of Mathematics and the School of Computer Science and Informatics, which will allow you to see the topic from different perspectives and provides access to a wide range of modules across both Schools.

Throughout the course you will develop data handling and extraction skills, programming skills, machine learning and informatics skills, and problem solving and modelling skills. You will undertake case studies and project work which will give you the opportunity to put your skills into practice and provides valuable experience of working in the field. The dissertation project, typically undertaken with an industrial partner, will allow you to work with complex data in a creative manner and a problem-solving environment, as well as to communicate your ideas and findings effectively.

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The MSc in Data Science and Analytics is a 12 month conversion course (new in 2017) designed to give students the knowledge and skills to collect, process analyse and visualise data in order to extract useful information, explore patterns and evaluate models. Read more
The MSc in Data Science and Analytics is a 12 month conversion course (new in 2017) designed to give students the knowledge and skills to collect, process analyse and visualise data in order to extract useful information, explore patterns and evaluate models. The course is a collaboration between the Departments of Mathematics & Statistics, Computer Science and the National Centre for Geocomputation.

Students will gain skills in programming, statistics and databases, followed by an advanced module on statistical machine learning. The course includes material on the social and ethical consequences of the use of data and the implications for business and government. Applications from many industry sectors will be explored in our Case Studies module. In the Project module, students will put these technical skills in to practice. They will also gain experience in report writing, presentations and teamwork. Students also do a 30-credit thesis.

The Data Analytics jobs market is expanding in Ireland. Jobs are available in any industry or sector that collects data, ranging from IT, to Healthcare, Finance, Food science and Travel.

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

Who is it for?

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

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

Objectives

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

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

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

Placements

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

Academic facilities

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

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

Scholarships

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

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

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

Teaching and learning

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

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

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

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

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

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

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

Course content

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

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

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

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

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

Individual project - (60 credits)

Career prospects

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

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

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1. Big Challenges being addressed by this programme – motivation. Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills. Read more

About the Course

1. Big Challenges being addressed by this programme – motivation

• Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills.
• Accenture, Gartner and McKinsey have all identified Data Analytics as one of the fastest growing employment areas in computing and one most likely to make an impact in the future.
• The Irish Government’s policy is for Ireland to become a leading country in Europe for big data and analytics, which would result in 21,000 potential new employment opportunities in Ireland alone.
• CNN has listed jobs in this area in their Top 10 best new jobs in America.

2. Programme objectives & purpose

This is an advanced programme that provides Computing graduates with advanced knowledge and skills in the emerging growth area of Data Analytics. It includes advanced topics such as Large-Scale Data Analytics, Information Retrieval, Advanced Topics in Machine Learning and Data Mining, Natural Language Processing, Data Visualisation and Web-Mining. It also includes foundational modules in topics such as Statistics, Regression Analysis and Programming for Data Analytics. Students on the programme further deepen their knowledge of Data Analytics by working on a project either in conjunction with a research group or with an industry partner.

Graduates will be excellently qualified to pursue careers in national and multinational industries in a wide range of areas. Our graduates currently work for companies as diverse as IBM, SAP, Cisco, Avaya, Google, Fujitsu and Merck Pharmaceuticals as well as many specialised companies and startups. Opportunities will be found in:
• Multinational companies, in Ireland and elsewhere, that provide services and solutions for analytics and big data or whose business depend on analytics and big data technologies;
• Innovative small to medium-sized companies and leading-edge start-ups who provide analytics solutions, services and products or use data analytics to develop competitive advantage
• Companies looking to extend their research and development units with highly trained data analytic specialists
• PhD-level research in NUI Galway, elsewhere in Ireland, or abroad

3. What’s special about CoEI/NUIG in this area:

• The MSc in Computer Science (Data Analytics) is being delivered by the Discipline of Information Technology in collaboration with the Insight Centre for Data Analytics (http://insight-centre.org) and with input from the School of Mathematics, Statistics and Applied Mathematics in NUI Galway
• The Discipline of Information Technology at NUI Galway has 25-year track record of education, academic research, and industry collaboration in the field of Computer Science
• The Insight centre at NUI Galway is Europe’s largest research centre for Data Analytics

4. Programme Structure – ECTS weights and split over semester; core/elective, etc.:

• 90ECTS programme
• one full year in duration, beginning September and finishing August
• comprises:
- Foundational taught modules (20 ECTS)
- Advanced taught modules (40 ECTS)
- Research/Industry Project (30 ECTS).

5. Programme Content – module names

Sample Foundational Modules:

• Tools and Techniques for Large Scale Data Analytics
• Programming for Data Analytics
• Machine Learning and Data Mining
• Modern Information Management
• Probability and Statistics
• Discrete Mathematics
• Applied Regression Models
• Digital Signal Processing

Sample Advanced Modules:

• Advanced Topics in Machine Learning and Information Retrieval
• Web Mining and Analytics
• Systems Modelling and Simulation
• Natural Language Processing
• Data Visualisation
• Linked Data Analytics
• Case Studies in Data Analytics
• Embedded Signal Analysis and Processing

6. Testimonials

Ms. Gofran Shukair, MSc, Research Engineer at ZenDesk, Ireland

After graduating with an MSc at NUI Galway, Gofran worked with Fujitsu’s Irish Research Lab as a research engineer before moving to a software engineering position at Zendesk, Ireland.

“The mix of technical and soft skills I gained through my Masters studies at NUI Galway is invaluable. I had the chance to work with great people and to apply my work on real world problems. With the data management and analysis skills I gained, I am currently pursuing my research in an international research project with one of the leading IT companies. I will be always thankful for studying at NUI Galway, a great historic place based in a culturally-rich vibrant city with an international mix of young and ambitious students that made me eager to learn and contribute back the moment I graduated.”

For further details

visit http://www.nuigalway.ie/courses/taught-postgraduate-courses/msc-in-computer-science-data-analytics.html

How to Apply:

Applications are made online via the Postgraduate Applications Centre (PAC) https://www.pac.ie
Please use the following PAC application code for your programme:

M.Sc. Computer Science – Data Analytics - PAC code GYE06

Scholarships :

Please visit our website for more information on scholarships: http://www.nuigalway.ie/engineering-informatics/internationalpostgraduatestudents/feesandscholarships/

Visit the M.Sc. Computer Science – Data Analytics page on the National University of Ireland, Galway web site for more details!

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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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Master in BIG DATA. Read more
Master in BIG DATA : Data Analytics, Data Science, Data Architecture”, accredited by the French Ministry of Higher Education and Research, draws on the recognized excellence of our engineering school in business intelligence and has grown from the specializations in Decision Support, Business Intelligence and Business Analytics. The Master is primarily going to appeal to international students, "free movers" or those from our partner universities or for high-potential foreign engineers who are looking for an international career in the domain of Business Analytics.

This program leads to a Master degree and a Diplôma accredited by the French Ministry of Higher Education and research.

Objectives

Business Intelligence and now Business Analytics have become key elements of all companies.

The objective of this Master is to train specialists in information systems and decision support, holding a large range of mathematic- and computer-based tools which would allow them to deal with real problems, analyzing their complexity and bringing efficient algorithmic and architectural solutions. Big Data is going to be the Next Big Thing over the coming 10 years.

The targeted applications concern optimization in the processing of large amounts of data (known as Big Data), logistics, industrial automation, but above all it’s the development of BI systems architecture. These applications have a role in most business domains: logistics, production, finance, marketing, client relation management.

The need for trained engineering specialists in these domains is growing constantly: recent studies show a large demand of training in these areas.

Distinctive points of this course

• The triple skill-set with architecture (BI), data mining and business resource optimization.
• This master will be run by a multidisciplinary group: statistics, data mining, operational research, architecture.
• The undertaking of interdisciplinary projects.
• The methods and techniques taught in this program come from cutting-edge domains in industry and research, such as: opinion mining, social networks and big data, optimization, resource allocation and BI systems architecture.
• The Master is closely backed up by research: several students are completing their end-of-studies project on themes from the [email protected] laboratory, followed and supported by members from the laboratory (PhD students and researcher teachers).
• The training on the tools used in industry dedicated to data mining, operational research and Business Intelligence gives the students a plus in their employability after completion.
• Industrial partnerships with companies very involved in Big Data have been developed:
• SAS via the academic program and a ‘chaire d’entreprise’ (business chair), allowing our students access to Business Intelligence modules such as Enterprise Miner (data mining) and SAS-OR (in operational research).

Practical information

The Master’s degree counts for 120 ECTS (European Credit Transfer System) in total and lasts two years. The training lasts 1252 hours (611 hours in M1 and 641 hours in M2). The semesters are divided as follows:
• M1 courses take place from September until June and count for a total of 60 ECTS
• M2 courses take place from September until mid-April and count for a total of 42ECTS
• A five-month internship (in France) from mid- April until mid- September for 9 ECTS is required and a Master thesis for 9 ECTS.

Non-French speakers will be asked to participate to a one week intensive French course that precedes the start of the program and allows students to gain the linguistic knowledge necessary for daily interactions.

[[Organization ]]
M1 modules are taught from September to June (60 ECTS, 611 h)
• Data exploration
• Inferential Statistics (3 ECTS, 30h, 1 S*)
• Data Analysis (2 ECTS, 2h, 1 S)
• Mathematics for Computer science
• Partial Differential Equations and Finite Differences (3 ECTS, 30h, 1 S)
• Operational Research: Linear Optimization (2 ECTS, 20h, 1 S)
• Combinatory Optimization (2 ECTS, 18h, 1 S)
• Complexity theory (1 ECTS, 9h, 1 S)
• Simulation and Stochastic Process (3 ECTS, 30h, 2 S**)
• Introduction to Predictive Modelling (2ECTS, 21h, 2 S)
• Deterministic and Stochastic Optimization (3 ECTS, 30h, 2 S)
• Introduction to Data Mining (2 ECTS, 21h, 2 S)
• Software and Architecture
• Object-Oriented Modelling (OOM) with UML (3 ECTS, 30h, 1 S)
• Object-Oriented Design and Programming with Java (2 ECTS, 30h, 1 S)
• Relational Database: Modelling and Design (3ECTS, 30h, 1 S)
• PLSQL (2 ECTS, 21h, 2 S)
• Architecture and Network Programming (3 ECTS, 30h, 2 S)
• Parallel Programming (3 ECTS, 30h, 2 S)
• Engineering Science
• Signal and System (3 ECTS, 21 h, 1 S)
• Signal processing (3 ECTS, 30h, 1 S)

• Research Initiation
• Scientific Paper review (1 ECTS, 9h, 1 S)
• Final research project on BIG DATA (5 ECTS, 50h, 2 S)
• Project Management
• AGIL Methods & Transverse Project (2 ECTS, 21h, 2 S)
• Languages and workshops
• French and Foreign languages (6 ECTS, 61h, 1&2 S)
• Personal and Professional Project (1 ECTS, 15, 1 S)
*1 S= 1st semester, ** 2 S= 2nd semester

M2 Program: from September to September (60 ECTS, 641h)
M2 level is a collection of modules, giving in total 60 ECTS (42 ECTS for the modules taught from September to April, plus 9 ECTS for the internship and 9 ECTS for the Master thesis).

Computer technologies
• Web Services (3 ECTS, 24h, 1 S)
• NOSQL (2 ECTS, 20h, 1 S)
• Java EE (3 ECTS, 24, 1S)
Data exploration
• Semantic web and Ontology (2 ECTS, 20h, 1 S)
• Data mining: application (2 ECTS, 20h, 1S)
• Social Network Analysis (2ECTS, 18h, 1S)
• Collective intelligence: Web Mining and Multimedia indexation (2 ECTS, 20h, 2 S)
• Enterprise Miner SAS (2 ECTS, 20h, 2 S)
• Text Mining and natural language (2 ECTS, 20h, 2 S)
Operations Research
• Thorough operational research: modelling and business application (2 ECTS, 21h, 1 S)
• Game theory (1 ECTS, 10h, 1 S)
• Forecasting models (2 ECTS, 20h, 1 S)
• Constraint programming (2 ECTS, 20h, 2 S)
• Multi-objective and multi-criteria optimisation (2 ECTS, 20h, 2 S)
• SAS OR (2 ECTS, 20h, 2 S)
Research Initiation Initiative
• Scientific Paper review (1 ECTS, 10h, 1 S)
• Final research project on BIG DATA (2 ECTS, 39, 2 S)
BI Architecture
• BI Theory (2 ECTS, 20h, 2 S)
• BI Practice (2 ECTS, 20h, 2 S)
Languages and workshops (4 ECTS, 105h, 1&2 S)
• French as a Foreign language
• CV workshop
• Personal and Professional Project
Internship
• Internship (9 ECTS, 22 weeks minimum)
Thesis
• Master thesis (9 ECTS, 150h)

Teaching

Fourteen external teachers (lecturers from universities, teacher-researchers, professors etc.), supported by a piloting committee, will bring together the training given in Cergy.

All the classes will be taught in English, with the exception of:
• The class of FLE (French as a foreign language), where the objective is to teach the students how to understand and express themselves in French.
• Cultural Openness, where the objective is to enrich the students’ knowledge of French culture.
The EISTI offers an e-learning site to all its students, which complements everything the students will learn through their presence and participation in class:
• class documents, practical work and tutorials online
• questions and discussions between teachers and students, and among students
• a possibility of handing work in online

All Master’s students are equipped with a laptop for the duration of the program that remains the property of the EISTI.

Read less
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 (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
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 (eg 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.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

Read less
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 (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
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 (eg 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.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

As preparation for the dissertation, you will be given a grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research.

Read less
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling… Read more
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations, from retailers such as Tesco or Amazon, to manufacturers like BMW, to health-care providers, and to public administration.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscdatascienceandanalytics.aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will acquire both the foundational knowledge and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations from retailers such as Tesco or Amazon, to manufacturers like BMW, health-care providers, or public administration. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning – the science behind ‘Big Data’ – is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liason Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).
Please visit our websitefor additional information on this degree.

On completion of the course graduates will have:
- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant. Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling… Read more
This degree, offered by the Department of Computer Science, will teach you both the foundational aspects and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations, from retailers such as Tesco or Amazon, to manufacturers like BMW, to health-care providers, and to public administration.

As part of the course, you will take an industrial placement, where you will gain valuable experience by putting your knowledge and skills into practice.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/mscdatascienceandanalytics(yearinindustry).aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will acquire both the foundational knowledge and the practical skills that prepare you for handling and analysing different types of data in different fields, thus responding to the needs of a huge variety of companies and organisations from retailers such as Tesco or Amazon, to manufacturers like BMW, health-care providers, or public administration. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- You will also be well prepared to pursue studies at PhD level, which several companies prefer for their research laboratories and more advanced roles.

- Taking a placement is an excellent opportunity to gain industrial experience (which gives you an extra edge when applying for jobs in the future) and acquire skills that can only be fully picked up in a work environment.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The excellence of our research in Machine Learning – the science behind ‘Big Data’ – is recognized worldwide, and the topics taught reflect that excellence.

- In the most recent Research Assessment Exercise (RAE 2008), the Department ranked 11th among UK Computer Science departments for its research output.

- The Department is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- The Department has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

Your placement will take up to one year and, if you are an overseas student, your visa will cover the two years of the programme. The placement attracts a salary and is assessed as part of your degree. You will be assigned a supervisor by the host company, who is responsible for directing your work. You will be assigned an academic supervisor, who visits to check if you are integrating successfully and the type of work being undertaken is appropriate, and supports you in general during your placement. If you cannot or decide not to take a placement, you revert to the normal one-year degree.

On completion of the course graduates will have:
Throughout your degree, you will have the opportunity to acquire the following skills:

- A highly analytical approach to problem solving.
- A strong background in data modelling and business intelligence.
- Knowledge of computational and statistical data analysis.
- A background in machine learning, statistics, and data mining.
- Ability to develop, validate, and use effectively machine learning models and statistical models.
- Ability to apply machine learning and data mining techniques to Information Retrieval and Natural Language Processing.
- Knowledge of and ability to work with software to automate tasks and perform data analysis.
- Knowledge of and ability to work with structured, unstructured, and time-series data.
- Ability to extract value and insight from data.
- Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, neural networks.
- Appreciation and knowledge of non-statistical approaches to data analysis and machine learning.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.
- Work experience and appreciation of how your work fits into the organizational and development processes of a company.

Assessment

Assessment is carried out by a variety of methods including coursework, examinations and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are among the most employable in the UK – we rank third in the UK for graduate employability – and, in recent years, have entered many different Computer Science-related roles including network systems design and engineering, web development and production. Other graduates choose to enter careers with a management or financial slant.

Our graduates have found employment at a wide range of organisations including Logica, British Telecom, British Aerospace, Microsoft, Amazon.com, American Express, Sky and Orbis Technology. At the same time, this course also equips you with a solid foundation for continued PhD studies.

Your careers ambitions are supported by our College Careers Service, located right next door to the Department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
Our IT systems and devices are constantly creating data and the amount of data created and stored grows exponentially. Data, and in particular patterns and trends within data, have the ability to inform and provide valuable insights, that help us predict and diagnose specific outcomes. Read more
Our IT systems and devices are constantly creating data and the amount of data created and stored grows exponentially. Data, and in particular patterns and trends within data, have the ability to inform and provide valuable insights, that help us predict and diagnose specific outcomes. Whilst the amount of data grows, the science of gaining insights from this data grows with it. Industry, research institutions and government all seek to extract value from data to improve products and services, serve their customers better and run more operationally efficient organisations. Data Scientists use their mathematical, computational and presentational skills to mine data for value and their skills are highly sort after. There is a significant shortage of skilled Data Scientists and so there are many job opportunities available.

Course content
We have designed this MSc course in consultation with industry partners.

This has enabled us to understand their needs for Data Scientists, what skills will be required and on successful completion of this course individuals will be highly employable within businesses.

Having this close understanding of what industry needs makes this course relatively unique and the very best suited to these looking for a career in the Data Sciences.

The course will be of specific interest to :

A mathematics graduate wishing to use your skills in a vocational business based environment
A computer science graduate wishing to follow a vocational route
Individuals currently working in Business and looking to grow their career through gaining Data Science and Business Analytics skills
Six modules go to make up this MSc:

Data Science Foundation
Managing Data
Data Exploration and Analysis
Mathematics
Machine Learning & Cognitive Computing
Data Visualisation and Presentation
The 1 year full time MSc course will be stimulating and interactive, making use of lectures, self-learning, workshops and hands-on projects.

You will be assigned a Personal Tutor from the start of your course who will work with you throughout your studies to help you achieve your academic best.

The knowledge we provide you with in these areas will give you all of the essential know-how on methods, tools and techniques to deliver in your career as a Data Scientist.

We believe Data Science is very much an intellectual ‘contact sport’ and through this course we provide you with every opportunity to put your theoretical knowledge into practice.

The project work we have imbedded within the course has been chosen and developed based on real-world scenarios across a range of industry and government sectors and is specifically designed to:

Provide an essential link between your theoretical learning and real-world challenges
Create an environment where you decide the methods and tools best suited to the challenge based on what you have learnt
Recreate some of the challenges facing industry and Government today and those very similar to what you will encounter in the workplace as a Data Scientist
Be adaptable to reflect new methods / tools and scenarios in this fast developing discipline
Be able upon completion of the projects to reference your experience in working with such challenges

Fees for 2017

Home fees - 1 year full-time: £8000.00

International fees: £10,920.00

Our facilities
You will undertake your workshops in training rooms that are bang up-to-date with design features, touch screen electronic white boards and high speed wifi; housed across three stunning Georgian mansions.

All of our current students love the learning environment, the culture, camaraderie and the fact that tutors know them by name so they are more than just a ‘face in the crowd’.

You will have access to the very best IT facilities in order to support your studies. These range from computer labs to access to cloud analytics from the leading providers.

We will use software from the academic programs of the major enterprise I.T. vendors such as IBM and Amazon as well as commonly used open source programs and frameworks.

From September 2018, many of the teaching sessions will take place in the purpose-built Engineering and Digital Technology building in the Bognor Regis campus.

What's more, you have lots of other facilities on this dedicated university campus including latest books, journals and online data in a truly modern library, an IT centre, a student zone complete with Costa Coffee, a gym and much more.

Where this can take you
The course has been designed to provide you with a very practical understanding of the issues associated with sourcing, curating, analysing and presenting data in business and other public sector and not-for-profit organisations.

On completion of your MSc studies and successful graduation, you will have very transferable skills and can choose to move directly in to the workplace perhaps in retail, banking, government or transport.

Indicative modules
Data Science Foundation (20 Credits)
Managing Data (20 Credits)
Data Exploration and Analysis (20 Credits)
Mathematics (20 Credits)
Machine Learning & Cognitive Computing (20 Credits)
Data Visualisation and Presentation (20 Credits)
Dissertation/Project (60 Credits)


Teaching and Assessment
Our approach to supporting your learning, and how your learning is assessed, is designed to mirror the workplace environment. With this in mind, key features of our approach to learning and assessment include the following:

We place a lot of emphasis on course work related activity.
Opportunities to work with organisations on current commercial/business problems and projects. These experiences are used to provide the basis for assessments that enable you to apply your learning within authentic commercial situations.

Read less
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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