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Masters Degrees (Data Modelling)

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

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

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics

- Data Science Research Methods and Seminars

- Big Data and Data Mining

- Big Data and Machine Learning

- Mathematical Skills for Data Scientists

- Data Visualization

- Human Computer Interaction

- High Performance Computing in C/C++

- Graphics Processor Programming

- Computer Vision and Pattern Recognition

- Modelling and Verification Techniques

- Operating Systems and Architectures

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst

- Data mining Developer

- Machine Learning Developer

- Visual Analytics Developer

- Visualisation Developer

- Visual Computing Software Developer

- Database Developer

- Data Science Researcher

- Computer Vision Developer

- Medical Computing Developer

- Informatics Developer

- Software Engineer



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

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

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

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

Why study Data Science at Radboud University?

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

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

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

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

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

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

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

Admission requirements for international students

- A proficiency in English

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

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

- IELTS score of >6.5

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

Career prospects

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

- To come up with a creative and useful solution.

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

- To communicate the obtained findings to others.

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

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

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

Our approach to this field

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

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

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

Radboud University Master's Open Day 10 March 2018



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The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Read more
The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:
-Computer science
-Programming
-Statistics
-Data analysis
-Probability

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

You also benefit from being taught in our School of Computer Science and Electronic Engineering, who are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of their research rated ‘world-leading’ or ‘internationally excellent’ (REF 2014).

The collaborative work between our departments has resulted in well-known research in areas including artificial intelligence, data analysis, data analytics, data mining, data science, machine learning and operations research.

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

The academic staff in our School of Computer Science and Electronic Engineering are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include Dr Paul Scott, who researches data mining, models of memory and attention, and artificial intelligence, and Professor Maria Fasli, who researches data exploration, analysis and modelling of complex, structured and unstructured data, big data, cognitive agents, and web search assistants.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We have six laboratories that are exclusively for computer science and electronic engineering students
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-You have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors
-We host regular events and seminars throughout the year
-Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
-The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course opens the door to almost any industry, from health, to government, to publishing.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Dissertation (optional)
-MSc Project and Dissertation (optional)
-Applied Statistics
-Machine Learning and Data Mining
-Modelling Experimental Data
-Text Analytics
-Artificial Neural Networks (optional)
-Bayesian Computational Statistics (optional)
-Big-Data for Computational Finance (optional)
-Combinatorial Optimisation (optional)
-High Performance Computing (optional)
-Natural Language Engineering (optional)
-Nonlinear Programming (optional)
-Professional Practice and Research Methodology (optional)
-Programming in Python (optional)
-Information Retrieval (optional)
-Data Science and Decision Making (optional)
-Research Methods (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)

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This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques. Read more

This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.

Introducing your degree

This MSc programme will train students to become proficient data scientists.

You will gain advanced knowledge in areas such as data mining, machine learning, and data visualization, including state of the art techniques, programming toolkit, and industrial and societal application scenarios.

Overview

This programme prepares you to become a proficient data scientist, developing your specialist knowledge in subjects that are crucial for mastering the vast and ever-so-complex information landscape that is characteristic to modern, digitally empowered organisations.

This is typically linked to a number of core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to the know-how that is required to devise and apply sophisticated Big Data analytics techniques, and the creativity involved in designing powerful visualizations.

In the first semester you start with a review of key topics in data science. The course will introduce the core theoretical and technology components required to design and use a data science application, using open-source tools and openly accessible data sets. You will also cover the most important machine learning techniques, which are at the core of any attempt to analyse and reason about data.

You will be exposed to more advanced topics in data mining in the second semester, including feature engineering, methods to manipulate text and multimedia data, topic modelling, social network analysis, and spectral analysis. A new module on data visualization will introduce the most common types of visualization techniques and state-of-the-art technology used to build graphic elements into data science applications to present analytics results.

Finally, during the summer the MSc project enables you will demonstrate your mastery of specialist techniques, relevant methods of enquiry, and your ability to design and deliver advanced application, systems and solutions to a tight deadline, including the production of a substantial dissertation.

Career Opportunities

Data scientists help organisations handle large amounts of data being produced thanks to digital technologies. Harvard Business Review described the role as 'The Sexiest Job of the 21st Century' due to the rare combination of skills that a trained data scientist possesses.

Data science has seen an unparalleled expansion as the data-driven economy grows. Increasingly organisations require skilled professionals who can handle large datasets and managers who can utilise the resulting analysis to make impactful decisions.

There is a range of potential jobs available; demand for big data staff is predicted to rise 92% over 5 years from Jan 2013. The programme provides an excellent opportunity for entry into data sciences or similar fields. Plus, big data positions offer a median salary of £55,000 – 24% higher than for IT staff in general (UK). There are also academic possibilities for doctoral study, as there are for entrepreneurial careers.

ECS runs a dedicated careers hub with is affiliated with more than 100 renowned companies such as IBM, Arm, Microsoft, Samsung, and Google. Visit our Careers Hub for more information.

Graduates from our MSc program can seek employment worldwide in:

  • established companies looking to spot trends in sales, marketing or operational data;
  • start-ups based around new opportunities in the booming data-driven economy;
  • government departments looking to utilise linked open data to gain insights to affect policy at the highest levels;
  • research/consultancy companies analysing data and feeding back to the wider community, with training and specialist services to clients.

Through an extensive blend of networks, mentors, societies and our on-campus startup incubator, we also support aspiring entrepreneurs looking to build their professional enterprise skills. Discover more about enterprise and entrepreneurship opportunities.



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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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Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science. Read more
Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science.

Furthermore, we are home to the Centre for Research in Social Simulation (CRESS) and its world-leading expertise in agent-based modelling.

PROGRAMME OVERVIEW

Interest in simulation has grown rapidly in the social sciences. New methods have been developed to tackle this complexity. This programme will integrate traditional and new methods, to model complexity, evolution and the adaptation of social systems.

These new methods are having an increasing influence on policy research through a growing recognition that many social problems are insufficiently served by traditional policy modelling approaches.

The Masters in Social Science and Complexity will equip you to develop expertise in the methods necessary to tackle complex, policy-relevant, real-world social problems through a combination of traditional and computational social science methods, and with a particular focus on policy relevance.

PROGRAMME STRUCTURE

This programme is studied full-time over one academic year and part-time over two academic years. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analysis
-Field Methods
-Computational Modelling
-Theory Model Data
-Modelling the Complex World
-Policy Modelling
-Theory and Method
-Statistical Modelling
-Evaluation Research
-Dissertation

EDUCATIONAL AIMS OF THE PROGRAMME

The main aims of the programme are to:
-Provide an appropriate training for students preparing MPhil/PhD theses, or for 
 students going on to employment involving the use of social science and policy research
-Provide training that fully integrates social science, policy modelling and computational methodologies to a high standard
-Provide training resulting in students with high quality analytic, methodological, computational and communication skills

PROGRAMME LEARNING OUTCOMES
The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:
-Develop skills in tackling real world policy problems with creativity and sound methodological judgment
-Cover the principles of research design and strategy, including formulating research 
questions or hypotheses and translating these into practicable research designs and models
-Introduce students to the methodological and epistemological issues surrounding research in the social sciences in general and computational modelling in particular
-Develop skills in programming in NetLogo for the implementation of agent-based models for the modelling of social phenomena
-Develop skills in the acquisition and analysis of social science data
-Make students aware of the range of secondary data available and equip them to evaluate its utility for their research
-Develop skills in searching for and retrieving information, using library and Internet resources
-Develop skills in the use of SPSS, and in the main statistical techniques of data analysis, including multivariate analysis
-Develop skills in the use of CAQDAS software for the analysis of qualitative data
-Develop skills in writing, in the preparation of a research proposal, in the presentation ofresearch results and in verbal communication
-Help students to prepare their research results for wider dissemination, in the form of seminar papers, conference presentations, reports and publications, in a form suitable for a range of audiences, including academics, stakeholders, policy makers, professionals, service users and the general public

Knowledge and understanding
-Show advanced knowledge of qualitative, quantitative and computational methodologies in the social science
-Show advanced knowledge of modelling methodologies, model construction and analysis
-Show critical understanding of methodological and epistemological challenges of social science and computer modelling
-Show critical awareness and understanding of the methodological implications of a range of sociological theories and approaches
-Show understanding the use and value of a wide range of different research approaches across the quantitative and qualitative spectra
-Show advanced knowledge in data collection, analysis and data driven modelling
-Show advanced knowledge of policy relevant social science research and modelling
-Show advanced understanding of the policy process and the role of social science and modelling therein
-Show advanced knowledge of statistical modelling

Intellectual / cognitive skills
-Systematically formulate researchable problems; analyse and conceptualise issues; critically appreciate alternative approaches to research; report to a range of audiences
-Conceptual development of Social Science and Complexity models to creatively enhance the understanding of social phenomena
-Integration of qualitative, quantitative and computational data
-Judgement of problem-methodology match
-Analyse qualitative and quantitative data drawn both from ‘real world’ and ‘virtual world’ environments, using basic and more advanced techniques, and draw warranted conclusions
-Develop original insights, questions, analyses and interpretations in respect of research questions
-Critically evaluate the range of approaches to research

Professional practical skills
-Formulate, design, plan, carry out and report on a complete research project
-Use the range of traditional and computational techniques employed in sociological research
-Ability to produce well founded, data driven and validated computational models
-Generate both quantitative and qualitative data through an array of techniques, and select techniques of data generation on appropriate methodological bases
-Employ a quantitative (SPSS) and qualitative software package to manage and analyse data
-Plan, manage and execute research as part of a team and as a sole researcher
-Ability to communicate research findings models in social science and policy relevant ways
-Ability to manage independent research

Key / transferable skills
-Communicate complex ideas, principles and theories by oral, written and visual means
-Apply computational modelling methodology to complex social issues in appropriate ways
-Creativity in approaching complex problems and a the ability of communicating and justifying problem solutions
-Apply computing skills for computational modelling, research instrument design, data analysis, and report writing and presentation
-Work to deadlines and within work schedules
-Work independently or as part of a team
-Demonstrate experience of a work environment

PLACEMENTS

On the MSc Social Science and Complexity, we offer the opportunity to take a research placement during the Easter vacation. This will provide you with first-hand experience of real-life policy research in action.

Organisations in which placements might be possible are a number of consultancies (e.g. Sandtable), government departments (e.g. Defra) and academic research centres (e.g. Centre for Policy Modelling at Manchester).

CAREER OPPORTUNITIES

Computational methods and especially computer-based simulations, are becoming increasingly important in academic social science and policy making.

Graduates might find career opportunities in government departments, consultancies, government departments, consultancies, NGOs and academia.

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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Our MSc in data analytics is designed to create rounded data analytics problem-solvers. Read more
Our MSc in data analytics is designed to create rounded data analytics problem-solvers.

This course focuses on the uses of data analytics techniques within business contexts, making informed decisions about appropriate technology to extract knowledge from data and understanding the theoretical principles by which such technology operates.

You'll gain a comprehensive skill set that will enable you to work in a variety of sectors using a blended learning approach that combines theory, intensive practice and industrial engagement.

Strathclyde's MSc in data analytics is unique by bringing together essential skills from three departments, Management Science, Mathematics & Statistics, and Computer & Information Sciences (CIS), in order to address the needs of a fast-growing industry.

This collaboration avoids the narrow interpretation of this subject offered by competitor institutions and presents significant opportunities for businesses to recruit data analytics experts with a high-level expertise and knowledge.

What you’ll study

The course will have a duration of 1 year, with two semesters of classes (120 credits in total) followed by an MSc dissertation project (60 credits) during the summer.

The class Data Analytics in Practice (20 credits) will be run over both semesters to provide you with a practical environment to apply methodological learnings from other classes into challenging projects from industry.

Semester 1

Semester 1 will additionally consist of five 10-credit core modules as listed under 'Course Content' which will provide the technical background to students. The contributions in Semester 1 will be split evenly between three departments.

This semester is designed to provide you with the fundamental technical analytics knowledge from all three departments.
-Computer & Information Sciences courses will cover core techniques including machine learning and data mining as well as data visualisation and big data platforms
-Mathematics courses will ensure you gain strong computational skills while establishing a broad knowledge of statistical tools essential for analytics
-Management Science courses will build the foundations of business skills including problem structuring as well as decision analysis, in addition to providing essential practical skills

Semester 2

Semester 2 will additionally consist of a 10-credit core module as well as 40 credits worth of elective modules. To ensure breadth of knowledge, you'll be required to choose electives from at least two departments. This semester is designed to extend your core skills and provide you with opportunities through a broad range of electives to specialise in areas that you are particularly interested to excel.

The only technical core class will provide you with a thorough theoretical and practical understanding of optimisation techniques essential for data analytics, whereas each of the three departments will offer four to five elective courses, the majority of which are accessible to everyone on the course without any prerequisites. The final component of the MSc course will be a summer dissertation project, which can be completed either through a client-based project or a desk-based research project, depending on your interests. You will submit your dissertation in September to complete your degree requirements (pending any resits).

Work placement

You will have optional opportunities to complete your MSc summer dissertation projects in client-based projects, where a number of host organisations will be arranged by the department. These projects will be normally unpaid, however, all costs such as travel and accommodation will be covered by the host organisation if out of town.

Major projects

The taught modules on the programme introduce you to a variety of tools, techniques, methods and models. However, the practical reality of applying analytical methods in business is often far removed from the classroom. Working with decision-makers on real issues presents a variety of challenges.

For example, data may well be ambiguous and hard to come by, it may be far from obvious which data analytics methods can be applied and managers will need to be convinced of the business merits of any suggested solutions. While traditional teaching can alert students to such issues, understanding needs to be reinforced by experience.

This is primarily addressed by the core module ‘Data Analytics in Practice’, which takes place over both semesters. Every year, case studies and challenging projects are presented to our students by various organisations.

Facilities

Strathclyde Business School (SBS) is one of the 76 triple-accredited business schools in the world, and is one of the largest of its kind in Europe. SBS was also recently selected as the "Business School of the Year" in Times Higher Education (THE) Awards."

The three departments involved in this course work together to provide a dynamic, fully-rounded and varied programme of specialist and cross-disciplinary postgraduate course.

Guest lectures

Every year, guest speakers attend our course, sharing their invaluable experiences. As part of the Data Analytics in Practice module, we host several presentations from external bodies.

Course content

Compulsory classes
-Big Data Fundamentals
-Big Data Tools & Techniques
-Data Analytics in R
-Business & Decision Modelling
-Optimisation for Analytics
-Data Analytics in Practice
-Dissertation in Data Analytics

Optional classes
Students are required to choose 40 credits worth of elective classes, and at least from two departments. All optional classes take place in Semester 2.

Learning & teaching

The course is delivered in various ways. While most classes have regular lectures, tutorials and hands-on software sessions, experiential learning is a crucial part of the course. This is delivered through projects and case studies with various external organisations, and MSc projects.

There are also guest lectures and recruitment events throughout the year, as well as a number of career support sessions that provide you with invaluable career information and generic job hunting skills such as CV writing and how to handle interviews.

Assessment

Every module has its own methods of assessment appropriate to the nature of the material. These include written assignments, exams, practical team projects, presentations and individual projects. Many modules involve more than one method of assessment to realise your potential.

Careers

The aim of the MSc in data analytics is to develop graduates who can use data analytics technology, understand the statistical principles behind the technologies and understand how to apply these technologies to solve business problems.

Graduates will be able to bridge the various knowledge domains that are relevant for tackling data analytics problems as well as being able to identify emerging themes and directions within data analytics. Graduates will display abilities across the three component disciplines.

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The Big Data in Business pathway will provide you with the knowledge and skills to understand and direct the strategic use of the vast amounts of information being generated by businesses today. Read more

The Big Data in Business pathway will provide you with the knowledge and skills to understand and direct the strategic use of the vast amounts of information being generated by businesses today.

Commercial focus

Our students learn to develop a strategic approach to managing Big Data in business, through the analysis of business problems as well as understanding different approaches to business intelligence. Through this, they are able to create usable business intelligence to create competitive advantage for their organisation.

After you’ve graduated

Our graduates have will leave us with the knowledge and skills necessary to analyse and manage Big Data to benefit business in a variety of sectors.

Not sure which pathway to choose from 3 choices? Apply for the one that you feel fits you better and you will be able to change the pathway within the first few weeks from your arrival to the university.

Why Henley?

  • Consistently maintain highest standards: Henley is in top 1% of business schools worldwide to hold accreditation from all three bodies in the UK, Europe and US
  • Excellent networking potential : 72,000 Henley alumni members in 150 countries
  • High calibre students: always oversubscribed, 1,000 ambitious new Masters students join Henley each year
  • Award winning campus: beautiful, green, 134 hectares, with state of the art facilities
  • World-leading faculty: widely published, frequently asked for expert comment by media and to speak at events
  • Henley is proud to be part of the University of Reading. The University is ranked within the top 200 universities worldwide (Times Higher Education World University Rankings 2016/17 and QS World University Rankings 2018) and 98% of the research is rated as being of international standard.

Course content

Compulsory modules

Optional modules

In addition students must choose two optional module from the list below.

Please note there is no guarantee that in any one year all modules will be available. 

How we teach you

A holistic approach

Effective leadership requires more than first-class business acumen. It also requires a degree of self-awareness and sensitivity. Henley is renowned for its well-researched, professional approach to this aspect of business education and all our postgraduate programmes examine this aspect of leadership - helping to create emotionally intelligent graduates who can be fully effective in their chosen careers.

How you will learn

Henley Business School enjoys a strong reputation for the practical application of business ideas and concepts, underpinned by academic excellence and the strength of our research. We offer high-quality technical skills training as well as a deep understanding of the importance of personal development for leaders, a thread that runs through all of our Masters programmes.

Our postgraduate masters programmes feature a mix of core and optional modules, allowing you to tailor your degree towards your individual personal development needs and career ambitions. You will complete up to 10 taught modules during your programme, totalling 180 credits. One module usually equates to 20 credits or 10 hours of work per week. Your week will include lectures, tutorials, workshops and personal study, with each accounting for 25% of your time on average. This stimulating mix of lectures and interactive tutorials provides you with the opportunity to discuss and explore the subject material in depth with your lecturers and fellow students. You will be introduced to the latest thinking and research findings and be able to challenge some of those that have created it. You will also explore real-world issues and tackle current business challenges, and interact with guest lectures and speakers from industry, giving you the opportunity to test, extend and refine your knowledge and skills.

How we assess you

You will learn and be assessed through a wide variety of teaching methods which vary depending on your chosen Masters programme. These include online materials and multimedia content, guest lectures, individual and group assignments, case studies, field visits, dealing room simulations, presentations, applied projects, consultancy work and examinations.

On average examinations form around 70% of the assessed work with the remaining 30% coming from coursework, including a written dissertation or project depending on your chosen programme. The exam period falls between April and June in the summer term, with students taking an average of 5 or 6 exams. Graduation normally takes place in December.

Ongoing support

While postgraduate students are self-motivated and determined individuals, study at this level can present additional pressures which we take seriously. Lecturers are available to discuss the content of each module and your personal tutor can meet with you regularly to discuss any additional issues. Full-time support staff are also available to help with any questions or issues that may arise during your time at Henley

Careers and accreditations

Each pathway of our MSc Information Management is designed to give a rigorous academic understanding of real-life and current business issues. Graduates of the Big Data in Business pathway will be equipped to develop strategies to manage Big Data. These skills are much in demand, in a variety of fields.  

A number of our students join our PhD programmes each year.

Students who pass the module – Business Domain and Requirements Analysis with a mark of 60 or above will be eligible for the British Computer Society Professional Certificate in Business Analysis Practice.



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This programme teaches students the skills required to manage a software project by producing criteria to monitor the project's progress and measure outcomes. Read more
This programme teaches students the skills required to manage a software project by producing criteria to monitor the project's progress and measure outcomes. Students learn how to formulate requirements for a business system and are given an underpinning in the nature of software development and its inherent complexity. The programme also covers system modelling and user interface and database design. Students learn to develop a system from determining its requirements and graphic user interface to database implementation.

This programme is of particular interest to those with a first degree in business or with business experience. Students are given an understanding of the information technologies upon which e-commerce is built and how these technologies provide us with new ways of organising and managing business. On successful completion of this programme, students should be proficient in the development of Internet, web and database technologies. They should also have the project management skills required for IT consultancy and strategic decision making.

Through our short course centre opportunity may also be provided to study for the Microsoft Technology Associate Exams.

Visit the website http://www2.gre.ac.uk/study/courses/pg/inftec/mbit

Computing - Information Technology

The School of Computing and Mathematical Sciences is an extremely successful part of the university and is recognised both nationally and internationally for its cutting edge research and its innovative approach to curriculum development.

Our up-to-date, relevant and exciting programs are designed in close collaboration with industry to provide the skills that employers really want. Our research record is outstanding, focusing on practical and important real-life problems.

What you'll study

Full time
- Year 1:
Students are required to study the following compulsory courses.

PG Project (CIS) (60 credits)
Systems Design and Development (15 credits)
Data Modelling (15 credits)
Project Management (15 credits)
Web and Intranet Content Management (15 credits)
Essential Professional and Academic Skills for Masters Students
English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)

Students are required to choose 30 credits from this list of options.

Cyber Security (15 credits)
Managing IT Security and Risk (15 credits)
User Centred Web Engineering (15 credits)
Strategic IT (15 credits)

Students are required to choose 30 credits from this list of options.

System Modelling (15 credits)
Audit and Security (15 credits)
User Experience Design (15 credits)
Organisational Awareness and Outsourcing (15 credits)

Part time
- Year 1:
Students are required to study the following compulsory courses.

Essential Professional and Academic Skills for Masters Students
English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)

Students are required to choose 30 credits from this list of options.

Cyber Security (15 credits)
Systems Design and Development (15 credits)
Managing IT Security and Risk (15 credits)
Web and Intranet Content Management (15 credits)
User Centred Web Engineering (15 credits)
Strategic IT (15 credits)

Students are required to choose 30 credits from this list of options.

System Modelling (15 credits)
Audit and Security (15 credits)
User Experience Design (15 credits)
Data Modelling (15 credits)
Organisational Awareness and Outsourcing (15 credits)
Project Management (15 credits)

- Year 2:
Students are required to study the following compulsory courses.

PG Project (CIS) (60 credits)

Students are required to choose 30 credits from this list of options.

Cyber Security (15 credits)
Systems Design and Development (15 credits)
Managing IT Security and Risk (15 credits)
Web and Intranet Content Management (15 credits)
User Centred Web Engineering (15 credits)
Strategic IT (15 credits)

Students are required to choose 30 credits from this list of options.

System Modelling (15 credits)
Audit and Security (15 credits)
User Experience Design (15 credits)
Data Modelling (15 credits)
Organisational Awareness and Outsourcing (15 credits)
Project Management (15 credits)

Fees and finance

Your time at university should be enjoyable and rewarding, and it is important that it is not spoilt by unnecessary financial worries. We recommend that you spend time planning your finances, both before coming to university and while you are here. We can offer advice on living costs and budgeting, as well as on awards, allowances and loans.

Assessment

Students are assessed through examinations, coursework and a project.

Professional recognition

This degree is accredited by the British Computer Society (BCS). This programme has the following accreditation: partial CITP. Your programme can therefore lead to partial exemption of the BCS Chartered IT Professional (CITP) status.

Career options

Graduates from this programme can pursue careers as business analysts, IT consultants and IT managers. Opportunities exist to develop a career working as independent consultants or within teams in diverse areas such as business and IT, internet and e-commerce applications, teaching and training.

Find out about the teaching and learning outcomes here - http://www2.gre.ac.uk/?a=643973

Find out how to apply here - http://www2.gre.ac.uk/study/apply

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Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Read more

About the course

Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Statistical analysis and data analytics is listed as one of the highly desirable skills employers are looking for, and with data becoming an ever increasing part of modern life, the talent to extract information and value from complex data is scarce.

The new Statistics and Data Analytics MSc is designed to train the next generation of statisticians with a focus on the field of data analytics. Employers expect skills in both statistics and computing. This master’s programme will provide a unique and coherent blend of modern statistical methods together with the associated computational skills that are essential for handling large quantities of unstructured data. This programme offers training in modern statistical methodology, computational statistics and data analysis from a wide variety of fields, including financial and health sectors.

Aims

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. The aim of the MSc Statistics and Data Analytics is to produce graduates that:

- Are equipped with a range of advanced statistical methods and the associated computational skills for handling large quantities of unstructured data
- Have developed a critical awareness of the underlying needs of industry and commerce through relevant case studies
- Are able to analyse real-world data and to communicate the output of sophisticated statistical models in order to inform decision making processes
- Have the necessary computational skills to build and analyse simple/appropriate solutions using statistical Big Data technologies

Course Content

Compulsory modules:

Quantitative Data Analysis
Research Methods and Case Studies
Computer Intensive Statistical Methods
Modern Regression and Classification
Data Visualisation
Big Data Analytics
Time Series Modelling
Network Models
Dissertation

Statistics with Data Analytics Dissertation
Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel Statistics or Financial Mathematics group. In some cases the project may be overseen by an external supervisor based in industry or another academic institution..

Teaching

You’ll be taught using a range of teaching methods, including lectures, computer labs and discussion groups. Lectures are supplemented by computer labs and seminars/exercise classes and small group discussions. The seminars will be useful for you to carry out numerical data analysis, raise questions arising from the lectures, exercise sheets, or self-studies in an interactive environment.

The first term provides a thorough grounding in core programming, statistical and data analysis skills. In addition to acquiring relevant statistical and computational methods, students are encouraged to engage with real commercial and/or industrial problems through a series of inspiring case studies delivered by guest speakers. Support for academic and personal growth is provided through a range of workshops covering topics such as data protection, critical thinking, presentation skills and technical writing skills.

You’ll also complete an individual student project supervised by a relevant academic on your chosen topic.

Assessment

The assessment of all learning outcomes is achieved by a balance of coursework and examinations. Assessments range from written reports/essays, group work, presentations through to conceptual/statistical modelling and programming exercises, according to the demands of particular modular blocks. Additionally, class tests are used to assess a range of knowledge, including a range of specific technical subjects.

Special Features

The Statistics Group is a growing, highly-research active group, with collaborations across industry and academia, including engineering and pharmaceutical companies, Cambridge University and Imperial College London

Brunel’s Mathematics department is a member of the London Graduate School in Mathematical Finance. This consortium of mathematical finance groups comprises Birkbeck College, Brunel University London, Imperial College London, King’s College London, London School of Economics and Political Science and University College London. 

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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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This Joint Degree between HEC Paris and Ecole Polytechnique will equip students with both the technical skills and the strategic mindset to lead successfully any business career requiring a strong expertise in Big Data. Read more

This Joint Degree between HEC Paris and Ecole Polytechnique will equip students with both the technical skills and the strategic mindset to lead successfully any business career requiring a strong expertise in Big Data.

Study in two globally-recognised Institutions

Ecole Polytechnique and HEC Paris are both world leading academic institutions, renowned for the quality of their degrees, faculties and research (see HEC rankings).

Their association within this Joint Degree represents the best Business/Engineering combination Europe could possibly offer, with extraordinary added value for the students who will follow this program in Data Science and Business.

Lead the digital transformation of the economy

Big data marks the beginning of a major transformation of the digital economy, which will significantly impact all industries. There are three main challenges to face:

  • Technological: dealing with the explosion of data by managing the spread of vast amounts of information that is often very disorganized (IP addresses, fingerprinting, website logs, static web or warehouse data, social media, etc.)
  • Scientific: replacing mass data with knowledge,i.e. developing the expertise that makes it possible to structure information, even out of tons of vague or corrupt data.
  • Economic: managing data both to control risks and benefit from the new opportunities they offer. On the one hand, it is absolutely vital to be able to control the flow of information, anticipate data leaks, keep the information secure and ensure privacy. On the other hand, it is also essential to come up with solutions capable of transforming this flow of data into economic results and, at the same time, discover new sources of value from the data.

Acquire the skills to make a difference in tomorrow's digital world

Exploiting this vast amount of data requires the following:

  • A mastery of the sophisticated mathematical techniques needed to extract the relevant information.
  • An advanced understanding of the fields where this knowledge can be applied in order to be in a position to interpret the analysis results and make strategic decisions.
  • A strong business mindset and an even stronger strategic expertise, to be able to fully benefit from the new opportunities involved with Big Data problematics and develop business solutions accordingly.
  • The ability to suggest and then decide on the choice of IT structures, the ability to follow major changes in IT systems, etc.

Therefore the program has three objectives:

  • To train students in data sciences which combines mathematic modelling, statistics, IT and visualization to convert masses of information into knowledge.
  • To give students the tools to understand the newest data distributing structures and large scale calculations to ease decision-making and guide them in their choices.
  • To form data ‘managers’ capable of exploiting the results from analysis to make strategic decisions at the heart of our changeable businesses.

Make the most of the worldwide networking and alumni power 

Students will benefit not only from the close ties that HEC Paris has developed with the business world but also those of Ecole Polytechnique, through various networking events, conferences and career fairs.

The HEC Alumni network alone, consists of more than 52,300 members in 127 countries.

Program details

The key aim of the teaching in this Joint Degree is to provide students with the tools needed to solve real problems, using structured and unstructured data masses, teaching them to ask the ‘right’ questions (both from statistics and ‘business’ perspectives), to use the appropriate mathematical and IT tools to answer these questions.

Students will be equipped to shift constantly from data to knowledge, from knowledge to strategic decision, and from strategic decision to operational business implementations.

All these shifts carry with them numerous challenges that each require an interdisciplinary approach involving mathematics, IT, business strategy, and management skills.



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Whether you are looking to start a career in data science or wanting to further develop your current career, our innovative online Masters programme in Data Analytics provides you with vital data science skills. Read more

Whether you are looking to start a career in data science or wanting to further develop your current career, our innovative online Masters programme in Data Analytics provides you with vital data science skills. This skills-based, yet rigorous curriculum provides you both with a thorough foundation in the underlying principles of learning from data and practical technical expertise in data handling, visualisation and modelling. The programme uses cutting-edge learning technology to deliver an interactive and collaborative online learning experience. Community building and collaborative learning is a key focus of our online delivery and you will be encouraged and supported to interact with your fellow classmates and tutors in a variety of ways throughout the duration of the course.

Why this programme?

  • The University of Glasgow’s School of Mathematics and Statistics is ranked 3rd in Scotland and 16th in the UK (Complete University Guide 2018).
  • The Statistics Group at Glasgow is the largest statistics group in Scotland and internationally renowned for its research excellence.
  • You will obtain an MSc degree from a world renowned university while being in full-time employment (around 10 hours of study per week).
  • You can personalise your learning by having the freedom to work at your own pace.
  • You can take advantage of rich interactive reading material, tutor-led videos and computer-led programming sessions.

Programme Stucture

This flexible part-time programme is completed over three years. In the first two years you will be taking two courses each trimester. In the third year you will be working on a project and dissertation.

The courses are designed to allow you to work at your own pace, with milestones and assessment to be completed according to an agreed timetable.

Core courses

  • Stochastic Models and Probability
  • Learning from Data
  • Predictive Models
  • R Programming
  • Data Programming in Python
  • Data Management and Analytics using SAS
  • Advanced Predictive Models
  • Data Mining and Machine Learning I: Supervised and Unsupervised Learning
  • Data Mining and Machine Learning II: Big and Unstructured Data
  • Uncertainty Assessment and Bayesian Computation
  • High-performance Computing for Data Analytics
  • Data Analytics in Business and Industry

You will also carry out a 60 credit research project.

In the first year of the programme you will need to take three paper-based examinations, held on the second Monday of May and the following Tuesday. UK-based students will have to take these examinations in Glasgow. Students from abroad can choose to either travel to Glasgow or take the examination in a local test centre, such as British Council offices. Test centres are subject to approval by the University and the candidate is responsible for any local fees charged by the test centre.

Career prospects

Data is becoming an ever increasing part of the modern world, yet the talent to extract information and value from complex data is scarce. There is a massive shortage of data-analytical skills in the workforce. Statistical Analysis and Data Mining was listed by LinkedIn as the hottest skill in 2014 and came second in 2015 and 2016. This programme opens up a multitude of career opportunities and/or boosts your career trajectory.

Graduates from the programmes in our School have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance, business consulting and government statistical services, while others have continued on to a PhD. 



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The Energy Systems and Data Analytics MSc provides an academically leading and industrially relevant study of energy systems through the lens of data analytics. Read more

The Energy Systems and Data Analytics MSc provides an academically leading and industrially relevant study of energy systems through the lens of data analytics. Advanced analytics, fuelled by big data and massive computational power, has the potential to transform how energy systems are designed, operated and maintained. You will gain the skills and knowledge to unlock the transformative potential of big energy data, and understand how it can reshape the energy sector.

About this degree

You will gain a broad understanding of energy systems as a whole, covering supply and demand, the interconnectedness and dependencies between different sectors and a multi-vector multi-sector approach to analysis. You will learn about the theory and practice of data analysis and will gain practical experience of the challenges of working with different data sets relating to energy throughout the programme and modules. 

The programme consists of five compulsory modules (75 credits), two optional modules (45 credits) and a dissertation (60 credits).

Core modules

  • Energy Systems
  • Energy Data Analytics
  • Statistics for Energy Analysis
  • Energy Analytics in the Built Environment
  • Energy and Transport Analytics

Optional modules

  • Spatial Analysis of Energy Data
  • Introduction to Systems Dynamics Modelling in the Built Environment
  • Econometrics for Energy and the Environment
  • Energy, Technology and Innovation
  • UK Energy and Environment Policy and Law
  • Smart Energy Systems: Theory, Practice and Implementation
  • Eco-innovation and Sustainable Entrepreneurship

The list of optional modules is correct for the 2018/19 academic year. Enrolment on modules is subject to availability.

Dissertation/report

All students undertake an independent research project whch culminates in a 10,000-word dissertation.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, tutorials, problem-based learning and project work. Assessment is through a combination of methods including problem sets, individual assignments and coursework, group based design tasks with a report and presentation, unseen examinations and a dissertation.

Further information on modules and degree structure is available on the department website: Energy Systems and Data Analytics MSc

Careers

Graduates of the ESDA MSc will be ideally placed to gain employment as energy analysts/ data scientists in consultancies, utilities, innovative start-ups and government institutions which value expertise in energy systems and have a need for data literate analysts.

Employability

There is a strong emphasis placed on innovation throughout the programme. Based on our market research and the trends in the industry (which is increasingly driven by data) there will be a healthy demand for our graduates.

Students will also benefit from a skill set in data analytics that will be highly transferable and applicable across a range of industries and domains.

The programme has been developed with input from industry leaders. You will gain exposure to real life energy and sustainability challenges.

Why study this degree at UCL?

The MSc in Energy Systems and Data Analytics is the first programme in the UK to combine the study of energy systems with data science. The MSc is delivered by leading researchers in the UCL Energy Institute and UCL Institute for Sustainable Resources. You will benefit from their specific expertise, research communities and industry contacts (including guest lecturers drawn from the energy industry), as well as our multidisciplinary and cross-domain approach.

The UCL Energy Institute has consulted across industry to identify key skills gaps for the energy analysts that will be required by utilities, consultancies and small and medium enterprises. There is a growing need in industry for graduates who combine an understanding of energy systems with the skills and abilities to extract insights from data through the use of advanced analytics.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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The current model of healthcare delivery in the UK is subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable. Read more
The current model of healthcare delivery in the UK is subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable.

This, coupled with a drive towards personalised medicine, presents an opportunity for a step change in healthcare delivery.

To do this, we need to make best use of the health data we collect and create a better understanding of the relationship between treatments, outcomes, patients and costs.

This requires individuals who understand:
-The healthcare sector and medicine
-How data is collected and analysed
-How this can be communicated to influence various stakeholders

Our MSc in Health Data Science aims to create a new breed of scientist able to work across all three sectors - health data scientists who will be at the forefront of this step change in healthcare delivery.

The course promotes the need for translational thinking to provide the knowledge, skills and understanding that will be applied across new challenges within healthcare delivery.

Students from a variety of professional backgrounds will benefit from the course, as the structure of the MSc ensures that you will share this knowledge with each other and learn to work in multidisciplinary teams, rather than in specialist silos.

The course has nine taught units covering key skills for health data science. Seven units are core and there is one optional unit depending on training needs and background. For those studying for an MSc, there is also a 60 credit research project.

Aims

This course will allow you to:
-Gain key background knowledge and an understanding of the healthcare system, from the treatment of individuals to the wider population;
-Gain an understanding of the governance structures and frameworks used when working with health data and in the healthcare sector;
-Experience key technical skills and software for working with and manipulating health data;
-Understand the breadth and depth of application methods and the potential uses of health data;
-Comprehend key concepts and distinctions of the disciplines that need to be synthesised for effective health data science;
-Appreciate the role of the health data scientist and how they fit into the wider healthcare landscape;
-Understand the importance of patient-focused delivery and outcomes;
-Develop the in-depth knowledge, understanding and analytical skills needed to work with health data effectively to improve healthcare delivery;
-Develop a systematic and critical understanding of relevant knowledge, theoretical frameworks and analytical skills to demonstrate a critical understanding of the challenges and issues arising from heterogeneous data at volume and scale, and turn them into insight for healthcare delivery, research and innovation;
-Apply practical understanding and skills to problems in healthcare;
-Work in a multi-disciplinary community and communicate specialist knowledge of how to use health data to a diverse community;
evaluate the effectiveness of techniques and methods in relation to health challenges and the issues addressed;
-Extend your knowledge, understanding and ability to contribute to the advancement of healthcare delivery knowledge, research or practice through the systematic, in-depth exploration of a specific area of practice and/or research.

Special features

MSc students will have an opportunity to conduct their research project in other settings such as the NHS and the biopharmaceutical industry, as well as academia.

Teaching and learning

The course covers four main areas that bring together technical, modelling and contextual skills to apply these to real world problems when harnessing the potential of health data.

In each of the units that deliver the key skills, both the importance of the patient and the governance surrounding working in the healthcare environment (especially structures around information governance) is embedded throughout.

Each unit will use case studies provided by existing work and research at the Health eResearch Centre (HeRC). The course will focus on large and complex health datasets (often routinely collected) in environments that safeguard patient confidentiality.

The course will encourage intellectual curiosity, creativity, and critical thinking, providing transferable skills for lifelong learning and research and cultivation of reflective practice.

Through the development of these innovation, critical, evaluative, analytical, technical, problem solving and professional skills, you will be able to conduct impactful work and advance healthcare delivery.

We see learning and teaching as collaborative knowledge construction, which recognises the contribution of all stakeholders (academic staff, service users and carers and students). This is demonstrated in the course through contributions made by these stakeholders through case studies, examples, invited seminars and participation in group work.

A variety of teaching methods will be used within the constraints of the method of delivery. The course will be student centred and will be delivered from the outset using a combination of face-to-face, distance learning and blended learning units.

Coursework and assessment

A range of assessments are used within each course unit and across the course as a whole.

All assessments require you to integrate knowledge and understanding, and to apply this to case studies and the outcomes of each unit.

Assessment will occur in a variety of forms including (but not exclusively) essays, case studies, assessed seminar/tutorial presentations and literature reviews.

Written assignments and presentations have a formative role in providing feedback (particularly in the early stages of course units) as well as contributing to summative assessment.

Online quizzes provide a useful method of regular testing, ensuring that you actively engage with the taught material.
The assessment of tutorials contains an element of self and peer evaluation, so you can learn the skills associated with the effective management of and participation in collaborative activity.

The course also places an emphasis on group work, as this a vital skill for professionals operating in a multidisciplinary area such as health data science, and this is shown in the teaching methods and assignments.

Each unit has a different emphasis on the group work assessment based on the nature of the material being covered, how they are to apply the knowledge and the work they are to complete.

The dissertation for the MSc requires you to undertake an extended written piece of work (10,000 to 15,000 words) that focuses on a specific aspect of health data science.

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