Masters Degrees (Data Mining)

With the rapid development of smart sensors, smartphones and social media, "big" data is ubiquitous. This new MSc teaches the foundations of GIScience, database, spatial analysis, data mining and analytics to equip professionals with the tools and techniques to analyse, represent and model large and complex spatio-temporal datasets.

Degree information

Students will be equipped with computational foundations and skills needed for big data analytics including visualisation, prediction, clustering and simulation with statistical and machine learning approaches, as well as retrieving and mining big (open) data, web services and cloud computing, web and mobile applications, by practising with real case data and open software.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a dissertation/report (60 credits).

A Postgraduate Diploma, four core modules (60 credits), two optional modules (60 credits), full-time nine months is offered.

Core modules
-GIS Principles and Technology
-Principles of Spatial Analysis
-Spatial Databases and Data Management
-Spatio-temporal Analysis and Data Mining

Choose four options from the following:
-Introductory Programming (requires Applied Machine Learning option)
-Complex Networks and Web
-Representation, Structures and Algorithms
-Mapping Science
-Supervised Learning (requires Applied Machine Learning)
-Web Mobile GIS
-Information Retrieval & Data Mining (requires Introductory Programming)
-Geographic Information System Design
-Applied Machine Learning (requires Introductory Programming, and Supervised Learning)

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 15,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, seminars, and laboratory practicals. Assessment is through examination, coursework, practicals, dissertation, and poster presentation.

Careers

Graduates from this programme are expected to find positions in consultancy, local government, public industry, and the information supply industry, as well as in continued research. Possible career paths could include: data scientist in the social media, finance, health, telecoms, retail or construction and planning industries; developer of spatial tools and specialised spatial software; researcher or entrepreneur.

Employability
Graduates will be equipped with essential principles and technical skills in managing, modelling, spatial and spatial-temporal analysis, visualising and simulating "big" spatio-temporal data, with emphasis on real development skills including: Java, JavaScript, Python and R. Business Intelligence (BI) skills will also be taught via practical case studies and close collaborations with leading industrial companies and institutions. All these skills are highly valued in big data analysis.

Why study this degree at UCL?

As one of the world’s top universities, UCL excels across the physical and engineering sciences, social sciences and humanities.

Spanning two UCL faculties, this interdisciplinary programme exploits the complementary research interests and teaching programmes of three departments (Civil, Environmental & Geomatic Engineering, Computer Science, and Geography).

Students on the Spatio-Temporal Analytics and Big Data Mining programme will be part of a vibrant, enthusiastic, and international research environment in which collaboration and free-ranging debate are strongly encouraged. This is supported by weekly research seminars and industrial seminars from top employers in the field.

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Degree: Master of Science (120 credits) with a major in Statistics
Teaching language: English

The rapid growth of databases provides business people and scientists with vast new resources. This programme - on the interface between statistics and computer science - will help students discover patterns and trends in large and complex datasets, and turn data into information and knowledge.

Statistics and data mining is a programme with unique career opportunities. The methods to organise and analyse data are getting more and more powerful, and this makes the synergy between data mining and statistics immensely rewarding. Economic transactions, customer data, individual health records and environmental data are just a few examples of the contents of enormous databases awaiting thorough examination.

The programme prepares students for professional work with data analysis in private enterprises and government agencies. It can also provide an ideal background for students who aspire to pursue a career as researchers in statistics or computer science.

The programme is designed for students with a solid background in statistics, computer science or applied mathematics, and an aspiration to learn more about data analysis. Being at the interface between statistics and computer science, it provides skills that cannot be achieved in conventional discipline-oriented programmes. The focus is on applications of data analysis, but underlying theories are also given considerable space.

Data mining courses, covering prediction, classification, clustering and association analysis constitute the core of the programme. Visualisation, survey sampling and theory of science are also mandatory. Supporting courses and profile courses (in statistics or computer science) and the master’s thesis project enables students to tailor the programme towards specific applications.

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About the course

This MSc provides you with the knowledge and skills to effectively develop, apply and research business intelligence systems. These are computerised information systems that enable organisations to gain business intelligence information and typically include some form of data mining functionality.

This course emphasises the concepts and techniques of business intelligence systems and their application and development, which are underpinned and exemplified via the learning of one or more contemporary ‘best of breed’ business intelligence software tools.

Reasons to study:

• Taught by SAS accredited teaching staff
you will be taught by experienced SAS accredited teaching staff providing you with expert knowledge and skills allowing you to work toward your SAS accreditation

• SAS endorsed course
enhance your employability and gain substantial knowledge and skills in SAS business intelligence software leading towards SAS data miner accreditation

• 50 years history of research and teaching in computing technology
benefit from our well established academic expertise and advance your skills in, and knowledge of, developing business intelligence systems and data mining solutions to business problems

• Gain an insight into real world solutions
attend guest lectures and seminars, which will give you a real understanding of the impact of their work

• Excellent graduate prospects
graduates have gone into roles such as BI/SQL developers, logistics data modeller’s and insight analysts at organisations including Cognisco, LLamasoft and Occam DM

Course Structure

Modules

First semester (September to January)

• Fundamentals of Business Intelligence Systems
• Analytics Programming
• Data Warehouse Design and OLAP
• Research Methods

Second semester (February to May)

• Data Mining
• Business Intelligence Systems Application and Development

Plus two from the following list:
• Management of Information Systems
• Human Factors in Systems Design
• Applied Computational Intelligence
• Artificial Neural Networks

Third semester (June to September)

•The individual project module.

Teaching and Assessment

Teaching will normally be delivered through formal lectures, informal seminars, tutorials, workshops, discussions and e-learning packages. Assessment will usually be carried out through a combination of individual and group work, presentations, reports, projects and exams.

Compulsory taught modules give you the opportunity to gain the fundamental knowledge and practices required to apply, develop and research business intelligence systems, while optional modules provide you with chances to study particular aspects of system application and development in more depth.

The individual project module allows you to undertake research into an aspect of business intelligence systems that interests you, and/or to perform appropriate business intelligence development tasks in response to a given practical problem.

Contact and learning hours

Full-time students will normally attend around 16 hours of timetabled taught sessions per week, and can expect to undertake around 24 further hours of self-directed independent study and research to support your assignments and dissertation.

Industry Association

This course was developed and is run in conjunction with SAS. SAS is the world's largest independent business analytics company. It provides an integrated set of software products and services to more than 45,000 customer sites in 118 countries. Across the globe, both the public and private sector use SAS software to assist in their efforts to compete and excel in a climate of unprecedented economic uncertainty and globalization.

To find out more

To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx

Applying for a postgraduate course:
http://www.dmu.ac.uk/study/postgraduate-study/entry-criteria-and-how-to-apply/entry-criteria-and-how-to-apply.aspx

Funding for postgraduate students:
http://www.dmu.ac.uk/study/postgraduate-study/postgraduate-funding-2017-18/postgraduate-funding-2017-18.aspx

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This course is designed to train highly qualified data analysts – or data scientists – to embark on careers in a wide range of industries. You’ll be given an excellent practical and theoretical grounding in data mining and statistics with the chance to customise your degree through modules in artificial intelligence, visualisation, programming and database manipulation.

Data Scientists are highly prized for their advanced, practical skill set and their increasing importance to the success of a modern business. Organisations in almost any industry need to source, analyse and utilise vast amounts of data to aid strategic decision-making, so you’ll have great graduate career prospects as well as a wide range of transferable skills.

We have a large Data Mining, Machine Learning and Statistics research group, which has made significant contributions to the field in the last 10 years, so you’ll be working directly with pioneering experts.

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Machine learning, data mining and high-performance computing are concerned with the automated analysis of large-scale data by computer, in order to extract the useful knowledge hidden in it. Using state-of-the-art artificial intelligence methods, this technology builds computer systems capable of learning from past experience, allowing them to adapt to new tasks, predict future developments, and provide intelligent decision support. Bristol's recent investment in the BlueCrystal supercomputer - and our Exabyte University research theme - show our commitment to research at the cutting edge in this area.

This programme is aimed at giving you a solid grounding in machine learning, data mining and high-performance computing technology, and will equip you with the skills necessary to construct and apply these tools and techniques to the solution of complex scientific and business problems.

Programme structure

Your course will cover the following core subjects:
-Introduction to Machine Learning
-Research Skills
-Statistical Pattern Recognition
-Uncertainty Modelling for Intelligent Systems

Depending on previous experience or preference, you are then able to take optional units which typically include:
-Artificial Intelligence with Logic Programming
-Bio-inspired Artificial Intelligence
-Cloud Computing
-Computational Bioinformatics
-Computational Genomics and Bioinformatics Algorithms
-Computational Neuroscience
-High Performance Computing
-Image Processing and Computer Vision
-Robotics Systems
-Server Software
-Web Technologies

You must then complete a project that involves researching, planning and implementing a major piece of work. The project must contain a significant scientific or technical component and will usually involve a software development component. It is usually submitted in September.

This programme is updated on an ongoing basis to keep it at the forefront of the discipline. Please refer to the University's programme catalogue for the latest information on the most up-to-date programme structure.

Careers

Skilled professionals and researchers who are able to apply these technologies to current problems are in high demand in today's job market.

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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.

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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. 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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This course is your opportunity to specialise in the development of web-based software systems that use databases. During your time with us, you will gain a critical awareness of the methodologies, tools and techniques used for the development of web-based computer systems and an advanced understanding of the techniques used for the development, evaluation and testing of databases.

The course also develops an awareness of the latest developments in the field of advanced databases, data mining and data warehousing. You will also gain substantial knowledge and skills in the deployment of SAS business intelligence software leading towards SAS data miner accreditation, and learn what the Semantic Web and Linked Data are, together with what these technologies enable.

Key benefits:

• The course gives you hands-on experience in design and implementation of databases in both Oracle and Microsoft SQL Server DBMS and prepares you to obtain DBA certification
• You learn how to design and implement a web application using ASP.NET, Microsoft SQL Server, and PHP with My SQL
• You learn the data mining techniques to mine data in different application domains using most popular data mining tools.

Visit the website: http://www.salford.ac.uk/pgt-courses/databases-and-web-based-systems

Suitable for

This course is for students who want to become trained professionals:

• In designing and implementing database systems in Oracle Micro Soft SQL Server DBMS and who want to be prepared to obtain DBA certification
• In designing and implementing a web application using ASP.NET, Microsoft SQL Server, and PHP with My SQL
• With hands-on experience in data mining techniques to mine data in different application domains using the most popular data mining tools.

Programme details

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

Format

Teaching on this course takes the form of lectures, individual and group class work, topical class discussions and critical case study evaluation.

You will gain hands-on lab experience of using and setting up databases and web-based systems. What’s more, tutorials will give you practice in solving the theoretical and design problems associated with these systems.

Module titles

• Advanced Databases
• Web-Based Software Development
• Semantic Web and Information Extraction
• Business Intelligence
• MSc Project

Assessment

• Coursework 60%
• Examinations 40%

Career potential

With this qualification, you’ll be equipped as web/database designer and programmer, data analytics and miner among other roles. Your experience will be in high demand across all industrial and commercial sectors.

Previous students have gone on to work with companies including British Airways, Google, Hewlett-Packard, Oracle and other IT firms.

How to apply: http://www.salford.ac.uk/study/postgraduate/applying

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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- http://www.gold.ac.uk/pg/msc-data-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

Contact the department

If you have specific questions about the degree, contact the Programme Director, Dr Daniel Stamate.

Modules & Structure

You will study the following:
Data Programming- 15 credits
Data Science Research- 15 credits

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

Funding

The Department of Computing offers a number of scholarships for students with remarkably good applications. The scholarships will be a one-off payment of £2,000. You don't need to submit a separate application to be considered for one of these awards. You can find out more from the department.

Funding

Please visit http://www.gold.ac.uk/pg/fees-funding/ for details.

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This interdisciplinary MSc is aimed at students who wish to develop skills in the emerging discipline of Data Science. Building upon data science fundamentals, a variety of pathways through the MSc are available and allow students to choose from a range of elective modules according to their skills, interests and career aspirations. Students then undertake a 12-week summer placement either within industry (in a business setting), or as part of an academic research project to consolidate their learning.

Optional pathways span fundamentals and also application areas including:
• Business Analytics: how to gain business insight from large and complex industrial data
• Data Mining: how data mining can be performed at scale, and in a range of application areas (eg marketing and finance, social computing)
• Health Informatics: how to build models and gain insight to improve public health and aid clinical decision making
• Systems and Technologies: how to build large-scale systems for answering data science questions
• Statistical Inference: how to specify models and build a statistical framework to gain insights from data

Modules
Core modules:
• Data Mining
• Data Science Fundamentals
• Generalised Linear Models
• Likelihood Inference
• Programming for Data Scientists

Optional (elective) Modules:
• Applied Data Mining
• Clinical Trials
• Data Mining for Marketing, Sales and Finance
• Elements of Distributed Systems
• Environmental Epidemiology
• Extreme Value Theory
• Forecasting
• Longitudinal Data Analysis
• Methods for Missing Data
• Multi-level Modelling
• Optimisation and Heuristics
• Principles of Epidemiology
• Statistical Genetics and Genomics
• Survival Analysis
• Systems Architecture and Integration

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This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.

You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.

Course content

Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools and methods. These include warehousing and data mining, distributed data management, and the technologies, architectures, and appropriate middleware and infrastructures supporting application layers. The second theme will enhance your knowledge of algorithms and the quantitative techniques suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area.

Teaching approaches include lectures, tutorials, seminars and practical sessions. You will also learn through extensive course work, class presentations, group research work, and the use of a range of industry standard software such as R, Python, Simul8, Palisade Decision Tools, Hadoop and Oracle.

Taught modules may be assessed entirely through course work, or may include a two-hour exam at the end of the year.

Modules

The following modules are indicative of what you will study on this course.

Core modules
-BIG DATA THEORY AND PRACTICE
-BUSINESS ANALYTICS
-DATA MINING AND MACHINE LEARNING
-RESEARCH METHODS AND PROFESSIONAL PRACTICE
-BUSINESS SYSTEMS POSTGRADUATE PROJECT

Option modules
-ADVANCED BIG DATA ANALYTICS
-BUSINESS OPTIMISATION
-DATA VISUALISATION AND DASHBOARDING
-DATA WAREHOUSING AND OLAP
-DATA REPOSITORIES PRINCIPLES AND TOOLS
-SIMULATION MODELLING: RISK, PROCESSES, AND SYSTEMS
-WEB AND SOCIAL MEDIA ANALYTICS

Associated careers

Graduates can expect to find employment as consultants, decision modelling or advanced data analyst, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (such as GlaxoSmithKline, Prudential, Santander and Unilever), public sector organisations (such as the NHS and primarily care trusts), retail head offices, the BBC, the Civil Service and the host of banks, brokers and regulators that makeup the city, along with all the specialist support consultancies in IT and market research and forecasting, all of the whom us data for the full range of decision making.

Professional recognition

This course is accredited by the British Computer society for partial fulfilment of the academic requirement for Chartered IT professional.

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This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.

You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.

Course content

Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools and methods. These include warehousing and data mining, distributed data management, and the technologies, architectures, and appropriate middleware and infrastructures supporting application layers. The second theme will enhance your knowledge of algorithms and the quantitative techniques suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area.

Teaching approaches include lectures, tutorials, seminars and practical sessions. You will also learn through extensive course work, class presentations, group research work, and the use of a range of industry standard software such as R, Python, Simul8, Palisade Decision Tools, Hadoop and Oracle.

Taught modules may be assessed entirely through course work, or may include a two-hour exam at the end of the year.

Modules

The following modules are indicative of what you will study on this course.

Core modules
-BIG DATA THEORY AND PRACTICE
-BUSINESS ANALYTICS
-DATA MINING AND MACHINE LEARNING
-RESEARCH METHODS AND PROFESSIONAL PRACTICE
-BUSINESS SYSTEMS POSTGRADUATE PROJECT

Option modules
-ADVANCED BIG DATA ANALYTICS
-BUSINESS OPTIMISATION
-DATA VISUALISATION AND DASHBOARDING
-DATA WAREHOUSING AND OLAP
-DATA REPOSITORIES PRINCIPLES AND TOOLS
-SIMULATION MODELLING: RISK, PROCESSES, AND SYSTEMS
-WEB AND SOCIAL MEDIA ANALYTICS

Associated careers

Graduates can expect to find employment as consultants, decision modelling or advanced data analyst, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (such as GlaxoSmithKline, Prudential, Santander and Unilever), public sector organisations (such as the NHS and primarily care trusts), retail head offices, the BBC, the Civil Service and the host of banks, brokers and regulators that makeup the city, along with all the specialist support consultancies in IT and market research and forecasting, all of the whom us data for the full range of decision making.

Professional recognition

This course is accredited by the British Computer society for partial fulfilment of the academic requirement for Chartered IT professional.

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The operating officers of tomorrow master in Operations Management today. The program focuses on the effective management of the resources and activities that produce or deliver the goods and services of a business. Operations managers oversee the people, materials, equipment and information resources that a business needs to produce and deliver its goods and services. Many of the most esteemed data systems operating the processes and activities of worldwide business are designed by operations managers.

Visit the website: http://manderson.cba.ua.edu/academics/departments/masters_program/master_of_science_in_operations_management

Choose from two track options:

Track 1: Operations Management

The master's program in Operations Management is offered both on campus and online. The degree requires 30 hours of coursework (10 courses). Full-time, on-campus students can complete the program in three semesters (fall/spring/summer). Students who want to pursue this degree program part-time while continuing to work can choose between the two-year and three-year schedules for completing the degree online.

Required Courses:

- OM 500 Management Science I
- OM 517 Supply Chain Modeling & Analysis
- OM 522 Operations Scheduling Problems
- OM 523 Inventory Management
- OM 524 Manufacturing Scheduling & Control Systems
- OM 540 Systems Simulation
- OM 596 Capstone Project
- ST 560 Statistical Methods

Track 2: Decision Analytics

The concept for this track is to offer an Operations Management master’s degree that combines the prescriptive modeling and analytical skills arising from the OM program with the data management and data mining skills arising from the SAS-UA Data Mining certification program offered in the Statistics program.

The Decision Analytics track consists of 10 courses: five from Operations Management, four from Statistics, and one from either Statistics or Management Information Systems.

- Required Courses:

- ST 560 Statistical methods in Research I
- ST 521 Statistical Data Management
- ST 531 Knowledge Discovery and Data Mining I
- ST532 Advanced Data Mining
- OM 500 Management Science and Spreadsheet Modeling
- OM 540 Systems Simulation
- OM 596 Capstone Project

- Two OM Elective Courses:

- OM 517 Supply Chain Modeling and Analysis
- OM 522 Operations Scheduling Problems
- OM 523 Inventory Management
- OM 524 Manufacturing Scheduling and Control Systems

*Choose two courses from this set of four courses.

- One Restricted Elective Course:

- ST 522 Advanced Statistical Data Management
- MIS 541 Business Analytics Support Systems

*Choose one of these two with consultation by program advisor.

How to apply: http://graduate.ua.edu/prospects/application/

Fund your studies

Student Financial Aid provides comprehensive information and services regarding opportunities to finance the cost of education at The University of Alabama. We recognize that financial assistance is an important key to helping reach your educational and career goals. The financial aid staff is dedicated to making the financial aid process as straightforward as possible. Visit the website to find out more: http://financialaid.ua.edu/

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About the course

Businesses now frequently possess and want to exploit huge, high volume, varied dynamic data sets, known as big data. Analytics is a subset of what has become to be called Business Intelligence. This is a set of technologies and processes used to understand data and analyse business performance.

Data Analytics MSc, developed and run with SAS, has been specifically designed to equip you with the skills and abilities to address this shortage. On successful completion of the course you will have developed your analytic and technical knowledge, and enhanced your professional skills within a Business Intelligence context.

You will be equipped with the relevant skills for employment in any field of data science (such as business intelligence, data mining, SAS programming and database design) within any target industry, with the additional option to complete a placement year in industry to further enhance your employability.

There is a growing need for professionals who can combine both analytical and software techniques in appropriate ways to allow the processing of ‘big data’. Data Analytics MSc is designed to provide these analytics and processing skills embedded within a business intelligence context.

Reasons to study:

• Taught by SAS accredited teaching staff
you will be taught by experienced SAS accredited teaching staff providing you with expert knowledge and skills

• Developed to fill skills shortage
course content has been developed to enhance your employability and gain substantial knowledge and equipping you with the skills required in for the use of the SAS software as well as Hadoop Distributed File System (HDFS) in industry

• 50 years history of research and teaching in computing technology
benefit from our well established academic expertise and advance your skills in, and knowledge of, data analytics to business problems

• Industry placement opportunity
you can chose to undertake a year-long work placement gaining valuable experience and skills as well as networking opportunities to build your industry contacts

• Excellent graduate prospects
equipped with the relevant skills for business intelligence and data mining roles including SAS Programming, Database Design and Business Intelligence

Course Structure

Modules

First semester (September to January)

• Statistics
• Fundamentals of Business Intelligence Systems
• Analytics Programming
• Data Warehouse Design and OLAP

Second semester (February to May)

• Business Intelligence Systems Application and Development
• Big Data Analytics
• Data Mining
• Research Methods

Third semester (June to September)

• Individual project

Teaching and Assessment

Teaching will normally be delivered through formal lectures, informal seminars, tutorials, workshops, discussions and e-learning packages. Assessment will usually be carried out through a combination of individual and group work, presentations, reports, projects and exams.

The course is run in association with SAS, the leading independent vendor in the business intelligence industry, and you will gain substantial SAS software skills as part of your study.

First semester modules provide you with fundamental abilities in the use of statistics so that you can gain insights and practice of using business intelligence systems and analytics programming to exploit multidimensional data sets.

In the second semester you are exposed to a variety of business intelligence systems, including those that use big data and data mining techniques. A further module prepares students to undertake an individual research project. This project module allows you to undertake extensive research into an aspect of big data, and/or provides an opportunity to develop and demonstrate your analytical and processing abilities in response to a given practical problem.

Contact and learning hours

You will normally attend 3 hours of timetabled taught sessions each week for each module undertaken during term time, for full time study this would be 12 hours per week during term time. You are expected to undertake around 24 further hours of independent study and assignments as required per week.

Industry Association

The Data Analytics MSc was developed and is run in conjunction with SAS. SAS is the world's largest independent business analytics company. It provides an integrated set of software products and services to more than 45,000 customer sites in 118 countries. Across the globe, both the public and private sector use SAS software to assist in their efforts to compete and excel in a climate of unprecedented economic uncertainty and globalization.

To find out more

To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx

Applying for a postgraduate course:
http://www.dmu.ac.uk/study/postgraduate-study/entry-criteria-and-how-to-apply/entry-criteria-and-how-to-apply.aspx

Funding for postgraduate students:
http://www.dmu.ac.uk/study/postgraduate-study/postgraduate-funding-2017-18/postgraduate-funding-2017-18.aspx

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Take advantage of one of our 100 Master’s Scholarships to study Health 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.

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 [email protected].

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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