Masters Degrees (Predictive Analytics)

The digital revolution has led to an unprecedented volume of information about consumers, which progressive organisations are eager to understand and use. This innovative masters degree will give you the practical skills to analyse consumer data and provide insights for successful marketing strategies.

Taught by leading academics from Leeds University Business School and School of Geography, you’ll explore a range of analytical techniques including applied Geographic Information Systems (GIS) and retail modelling, consumer and predictive analytics and data visualisation. You’ll also develop the softer skills to use the results of these analyses to inform decisions about marketing strategy.

Thanks to our connections with businesses worldwide, you’ll have access to emerging trends in topics such as consumer behaviour, decision science and digital and interactive marketing. You’ll further develop your practical skills with the opportunity to work on a live data project provided by a company.

Academic excellence

This courseoffers you a rare combination of teaching expertise; the Business School’s academic excellence in Marketing alongside world-class teaching from the School of Geography, which draws on the knowledge of the Centre for Spatial Analysis and Policy.

The University of Leeds is a major centre for big data analytics and you’ll benefit from affiliation with the UK’s Consumer Data Research Centre. The centre aims to make data that are routinely collected by businesses and organisations accessible for academic purposes. Coordinating and analysing this large and complex data has the potential to increase productivity and innovation in business, as well as to inform public policy and drive development.

Read an interview with the academic team to learn more about our expertise and the growing importance of this emerging subject area.

Course content

Core modules will introduce you to a range of analytical methods, ensuring you develop a solid foundation in the essential skills for consumer analytics and marketing strategy.

You’ll learn how to analyse geographic data using GIS software and understand the application of this in retail modelling, to evaluate new markets and locations. You’ll study predictive analytics, big data and consumer analytics, business analytics and decision science, and learn how to communicate results through data visualisations.

Alongside this, you’ll learn how to deploy data to inform decisions about marketing strategy. Marketing modules include marketing strategy, consumer behavior and direct, digital and interactive marketing. You’ll also deliver your own data-driven marketing research project for a company.

Optional modules allow you to further your knowledge in a related area of interest, either corporate social responsibility, internal communications and managing change, or applied population and demographic analysis.

By the end of the course, you’ll submit an independent project. You can either research a topic in-depth and submit a dissertation, or gain practical experience through a consultancy project working with an external organisation.

Course structure

Compulsory modules

You’ll take the nine compulsory modules below, plus your dissertation, which can be a choice of either a research dissertation or marketing consultancy project.

• Geographic Data Visualisation & Analysis 15 credits
• Big Data and Consumer Analytics 15 credits
• Predictive Analytics 15 credits
• Applied GIS and Retail Modelling 15 credits
• Business Analytics and Decision Science 15 credits
• Consumer Behaviour 15 credits
• Marketing Research Consultancy Project 15 credits
• Direct, Digital and Interactive Marketing 15 credits
• Marketing Strategy 15 credits
• Dissertation OR Marketing Consultancy Project 30 credits

Optional modules

You'll take one further optional module.

• Applied Population and Demographic Analysis 15 credits
• Corporate Social Responsibility and Sustainability 15 credits
• Internal Communications and Change Management 15 credits

For more information on typical modules, read Consumer Analytics and Marketing Strategy MSc in the course catalogue

Learning and teaching

We use a range of teaching methods so you can benefit from the expertise of our academics, including lectures, workshops, seminars, simulations and tutorials. Company case studies provide an opportunity to put your learning into practice.

Independent study is also vital for this course, allowing you to prepare for taught classes and sharpen your own research and critical skills.

Assessment

Assessment methods emphasise not just knowledge, but essential skills development too. You’ll be assessed using a range of techniques including exams, group projects, written assignments and essays, in-course assessment, group and individual presentations and reports.

Career opportunities

As a graduate of this course you will be equipped with advanced skills in consumer analytics and marketing strategy, ideal for those wishing to pursue a career in consumer data analytics, marketing and/or management.

Due to the digital revolution, companies from around the world and in many industrial sectors have access to greater amounts of data.

The most progressive companies in the world are particularly interested in marketing graduates with strong analytical skills, and typical roles could include marketing or consumer data analyst, direct marketing manager, marketing manager, retail manager, or marketing or management consultant.

Careers support

As a masters student you will be able to access careers and professional development support, which will help you develop key skills including networking and negotiating, and put you in touch with potential employers.

Our dedicated Professional Development Tutor provides tailored academic and careers support to marketing students. They work in partnership with our academics to help you translate theory into practice and develop your interpersonal and professional business skills.

You can expect support and guidance on career choices, help in identifying and applying for jobs, as well as one-to-one coaching on interpersonal and communication skills.

Read more about careers support at the Business School.

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Gain in-depth knowledge of analytical and predictive modelling skills as well as management, communication and research skills.

In the era of Big Data, analytics is becoming a strategic necessity in virtually all areas of business and is an essential tool to drive real-time decisions, foster evidence-based decision-making and sustain competitive advantage. According to a recent ranking by US News and World Report, Market Research Analyst and Operations Research Analyst are in the top four Best Business Jobs of 2015, and Harvard Business Review claims Data Scientist is the 'sexiest job of the 21st century' with practitioners having rare and highly sought-after skills.

To meet the growing demand for graduates with analytics capabilities, the MSc in Business Analytics degree equips you with the latest analytics tools to analyse and interpret data, forecast future trends, automate and streamline decisions, and optimise courses of action. Emphasis is placed on learning fundamental analytics techniques, such as statistical analysis, data mining, forecasting and regression, optimisation, simulation and spreadsheet modelling among others.

You will learn how to apply descriptive, predictive and prescriptive modelling techniques to help organisations improve performance, explore alternatives, and anticipate and shape business outcomes in a rapidly changing environment. Upon graduation, you will be ready to start a fast-track career in a variety of industries and sectors including airlines, manufacturing companies, energy, healthcare delivery, banking, marketing and government.

Students enrolled in the programme have the opportunity to work for real organisations, improve their consultancy skills and enhance their employability through the Student Implant Scheme, which bridges the gap between classroom learning and the real world. Students are also involved in a variety of activities, including case studies, team project work, guest lectures and industry visits.

Software demonstration workshops supported by IBM/ILOG are regularly organised to support the teaching of state-of-the-art analytics packages including IBM Watson Analytics, R, SPSS, Weka, MS Excel and VBA, as well as optimisation packages including Optimization Programming Language, IBM/ILOG, CPLEX and Simul8.

This programme is ideal for graduates with a good background in a quantitative area who are seeking to gain an in-depth knowledge of analytical and predictive modelling skills as well as management, communication and research skills.

Visit the website https://www.kent.ac.uk/courses/postgraduate/292/business-analytics

About Kent Business School

Kent Business School has over 25 years’ experience delivering business education. Our portfolio of postgraduate programmes (http://www.kent.ac.uk/kbs/courses/msc/index.html) demonstrates the breadth and depth of our expertise. Academic research and links with global business inform our teaching, ensuring a curriculum that is relevant and current. We are ranked (http://www.kent.ac.uk/kbs/whychooseus/rank-accred.html) as a top 30 UK business school for the standard of our teaching and student satisfaction. We also hold a number of accreditations (http://www.kent.ac.uk/kbs/whychooseus/rank-accred.html?tab=accreditations-and-professional-bodies) by professional bodies.

Studying at Kent Business School (KBS) gives you the opportunity to increase your employability with real-life case studies, a student council and a business society. We have strong links to local and national organisations providing opportunities for projects, internships and graduate placements. The School attracts many high-profile speakers from industry and last year included visits and lectures from staff of the Bank of England, BAE Systems, Barclays, Lloyds Insurance, Cummins, Delphi and Kent County Council.

Careers

You gain much more than an academic qualification when you graduate from Kent Business School – we enhance your student experience and accelerate your career prospects.

From the moment you start with us, our efforts are focused on helping you gain the knowledge, skills and experience you need to thrive in an increasingly competitive workplace.

In today’s business climate employers are increasingly demanding more from new employees, we are therefore proud that they continually target our graduates for their organisations across the globe. Employers respect our robust teaching and reputation for delivering international business expertise, leading global research and an outstanding international learning experience.

Recent graduates have gone on to work for Barclays Capital, British Embassy, Gray Robinson PA and Holiday Extras.

To find out more about business analytics and future career prospects, see the following links.

- OR Society: British Society of Operational Research: http://www.theorsociety.com/

- What is OR? Video and success stories: http://www.learnaboutor.co.uk/

Professional recognition

Kent Business School is a member of the European Foundation for Management Development (EMFD), CIPD, CIM and the Association of Business Schools (ABS). In addition, KBS is accredited by the Association of MBAs (AMBA).

Find out how to apply here - https://www.kent.ac.uk/courses/postgraduate/apply/

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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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This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science.

Degree information

The programme is designed to give students multidisciplinary skills in computing (i.e. programming, big data), analytics (i.e. data mining, machine learning, computational statistics, complexity), and business analysis. Emphasis will be on business problem framing, leveraging data as a strategic asset, and communicating complex analytical results to stakeholders.

Students undertake modules to the value of 180 credits. The programme consists of five core modules (90 credits), two optional modules (30 credits) and a dissertation (60 credits).

Core modules
-Programming for Business Analytics
-Data Analytics
-Information Retrieval and Data Mining
-Introduction to Supervised Learning
-Statistical NLP

Please note: the availability and delivery of modules may vary.

Optional modules
-Applied Machine Learning
-Graphical Models
-Web Economics
-Statistical Models and Data Analysis
-Statistical Design of Investigations
-Decision and Risk
-Consumer Behaviour and Behavioural Change
-Consulting Psychology
-Talent Management
-Data Science for Spatial Systems
-Group Mini Project: Digital Visualisation
-Urban Simulation
-Mastering Entrepreneurship
-Decision and Risk Analysis
-Managing Hi-Tech Organisations

Please note: the availability and delivery of modules may vary.

Dissertation/report
During the summer students will undertake a work placement with a UCL industrial partner. The research and data analysis conducted during this placement will form the basis of a 10,000-word dissertation.

Teaching and learning
The programme is delivered through a combination of lectures by world-class academics and industry leaders, seminars, workshops, tutorials and project work. The programme comprises two terms of taught material, followed by examinations and then a project. Assessment is through unseen written examinations, coursework and the dissertation.

Careers

Graduates of UCL Computer Science are particularly valued due to the department's international status, and strong reputation for leading research. Recent graduate destinations include: IBM, Samsung, Microsoft, Price Waterhouse Coopers, Citibank.

Employability
This programme is designed to satisfy the need, both nationally and internationally, for exceptional data scientists and analysts. Graduates will be highly employable in global companies and high-growth businesses, finance and banking organisations, major retail and service companies, and consulting firms. They will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset. We expect our graduates to progress to leading and influential positions in industry.

Why study this degree at UCL?

UCL Computer Science is a global leader in research in experimental computer science. The 2014 Research Excellence Framework (REF) ranked the department as first in the UK for research, with 96% regarded as internationally excellent.

The department consists of a team of world-class academics specialising in big data, computational statistics, machine learning and complexity.

The programme aims to create the next generation of outstanding academics and industry pioneers, who will use data analysis to deliver real social and business impact.

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Why this programme?

◾The University of Glasgow’s School of Mathematics and Statistics is ranked 3rd in Scotland and 11th in the UK (Complete University Guide 2017).
◾The Statistics Group at Glasgow is the largest statistics group in Scotland and internationally renowned for its research excellence.
◾Statistics obtained a 100% overall student satisfaction in the National Student Survey 2016. The subject continues to exceed student expectations by combining both teaching excellence and a supportive learning environment.

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.

The programme can be taken alongside full-time employment (around 10 hours of study per week are recommended).

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.

Online Distance Learning

Online distance learning at the University of Glasgow allows you to benefit from the outstanding educational experience that we are renowned for without having to relocate to our campus. You do not need to have experience of studying online as you will be guided through how to access and use all of our online resources.

Virtual Learning

Your courses will consist of rich interactive reading material, tutor-led videos and computer-led programming sessions. You will be provided with access to electronic articles and books for background reading. Regular online assessments will allow you to monitor your progress.

Collaborative learning

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.

Requirements

All you need to participate in this online programme is a computer and internet access.

On-campus examinations

In the first year of the programme you will need to take three paper-based examinations, held on May 2018 (preliminary dates: May 7th to 9th, 2018). UK-based students will haver 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 Online MSc in Data Analytics covers 18 5-credit modules, two per semester over 9 semesters or 3 years, of which the Online Professional Diploma in Data Analytics covers the first 4. This first year is designed to introduce you to statistical and mathematical concepts in Data Analytics and Data Mining, and to get you started on programming with data. The second year is split between understanding the theory behind statistical and mathematical models for data via predictive analytics, and dealing with data sets at scale using Python and multivariate techniques. The final year covers some advanced methods: Monte Carlo, Bayesian Analysis, Time Series Data, and Complex Stochastic models. A provisional list of topics is as follows:

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This professional degree is for people who are passionate about drawing meaningful knowledge from data to drive business decision-making or research output.
Data is a vital asset to an organisation. It can provide valuable insights into areas such as customer behaviour, market intelligence and operational performance. Data scientists build intelligent systems to manage, interpret, understand and derive key knowledge from this data.
For those with strong mathematical or quantitative backgrounds, this degree will develop your analytical and technical skills to use data science to guide strategic decisions in your area of expertise. It offers the flexibility to tailor your learning to your professional and personal interests.
Leveraging the University’s research strengths, you will explore the latest in data mining, machine learning and data visualisation, while developing the skills to effectively communicate data insights to key stakeholders.
For those with qualifications in other areas such as health and education, a Graduate Certificate in Data Science can provide you with data science capability to complement your existing skills and provide a pathway to the master’s program.
Course structureThe course comprises core units, elective units and a capstone project where you will apply your skills to a real-world data science problem. You can tailor your degree by selecting elective units and a project that complement your particular interests, background and qualifications.
Core units for the Master of Data Science include Principles of Data Science, Computational Statistical Models, Visual Analytics, and Knowledge Discovery and Data Mining.
For the Graduate Certificate in Data Science, core units include Principles of Data Science, Algorithms, Database Management Systems and Introduction to Statistics.
You can select elective units from the following data science subjects, or from other disciplines relevant to your background and qualifications.
Data science electives include:
Advanced Data Models
Cloud Computing
Multimedia Retrieval
Data Analytics and Business Intelligence
Information Security Management
Statistical Learning and Data Mining
Statistical Natural Language Processing
Predictive Analytics.We also offer a pathway for eligible candidates planning to pursue a research degree.

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Companies need people who can take data and transform it into a powerful strategic asset. This specialisation provides a rigorous, practical foundation in the key skills needed to unlock the value of data, and an in-depth understanding of how companies can use data to make decisions and improve business performance.

Degree information

Business Analytics (with specialisation in Management Science) is taught by UCL School of Management (in conjunction with UCL Computer Science). It combines modules that explore how data and analytics are transforming key areas of business (decision-making, strategy, marketing, operations) with modules that provide the mathematical and computational skills needed to make effective use of the latest business analytics tools.

Students undertake modules to the value of 180 credits. The programme consists of six core modules (90 credits), two optional modules (30 credits) and a dissertation/report (60 credits).

Core modules
-Business Strategy and Analytics
-Marketing Analytics
-Mathematical Foundations for Business Analytics
-Programming for Business Analytics
-Predictive Analytics
-Operations Analytics

Optional modules - students take two optional modules from a selection of elective modules offered by the UCL School of Management and the UCL Department of Computer Science. Possible electives, subject to agreement, will cover topics including software engineering, practical machine learning, project management and social network analytics.

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, tutorials and project work. Assessment is through unseen written examinations, coursework and the dissertation.

Careers

The world is changing: more than 30 billion pieces of content are shared on Facebook every month; and companies are capturing trillions of bytes of information about their customers, suppliers, and operations. This explosion of data is disrupting industries and creating new opportunities.

Companies need people who can take data, understand it, process it, extract value from it, visualise it, and communicate it.

They need people who deeply understand data, its potential and its limitations, who can frame business problems, analyse data with statistical techniques, develop and maintain predictive models, and communicate analytics results to business executives, partners and customers.

Employability
Graduates from this new programme will be highly employable in global companies and high-growth businesses, finance and banking organisations, and consulting firms.

Students will develop strong quantitative and analytical skills, an in-depth understanding of how companies use data to make decisions and improve business performance, and practical experience with leading business analytics tools. They will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset.

Why study this degree at UCL?

Business Analytics requires a combination of management insight, strong quantitative and analytical skills, and an understanding of the technology required to handle data at scale.

UCL School of Management offers innovative undergraduate, postgraduate, and doctoral programmes to prepare people for leadership roles in the next-generation of innovation-intensive organisations. The department works closely with global companies and high-growth businesses at the cutting-edge of management practice. UCL Computer Science is a global leader in research in experimental computer science.

Students on the Business Analytics (with specialisation in Management Science) MSc will benefit from the extensive industry networks of both departments.

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In partnership with CEFRIEL

The International Master in Business Analytics and Big Data is an innovative programme based on a holistic educational experience, where theory and practice are fully interlaced through the continuous support of companies and international partners. Furthermore, being in constant relationship with the Big Data Analytics & Business Intelligence Observatory of Politecnico di Milano, the course is at the forefront of research on ICTs for big data analysis and management. This truly unique programme is jointly offered by MIP and CEFRIEL - Politecnico di Milano and it is in partnership with IBM.

The programme provides young and highly motivated candidates with the knowledge, skills and competencies required to meet the accelerating demand for data scientists. The course is designed to enhance the managerial talent by harnessing the power of data and turning data into knowledge and business value.

The course is divided into six major modules: General Management, Data Management and Warehousing, Fundamentals of Statistics and Data Visualization, Predictive Analytics and Business Applications, Analytics for Management and Elective courses. The educational programme requires the completion of a final project work, which consists either of a curricular in-company internship, supervised by a faculty member, or of a master thesis that will be defended as final exam.

Students will have the opportunity to specialize their master by following elective concentrations on topics such as: Analytics for Finance, Analytics on customer experience, Social Media Marketing, Analytics for Supply Chain Management. Participants may follow elective courses also in one or more business schools in the network of international MIP partners.

Admissions Deadlines

Applications have to be submitted online on: http://www.applyformasters.net.

Candidates applying before 31st December 2015 will be eligible for the Early-Bird tuition fee waiver amounting to € 1000.
For more information and application assistance, please write to [email protected]

MIP Politecnico di Milano presents the new #growingleader

The blog dedicated to MIP international programs is ready to inspire a new generation of students helping them to grow as leaders. Click here to find out more: http://www.growingleader.com/

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Program overview

The MS program in applied statistics is available to both full- and part-time students with courses available both on-campus and online. Cooperative education is optional. The program is intended for students who do not wish to pursue a degree beyond the MS. However, a number of students have attained doctorate degrees at other universities.

Plan of study

The program requires 30 credit hours and includes four core courses, five electives, and a capstone or thesis.

Core courses

There are four required core courses. Students, in conjunction with their advisers’ recommendations, should take the core courses early in the program.

Curriculum

Applied statistics, MS degree, typical course sequence:
First Year
-Statistical Software
-Fundamentals of Statistical Software
-Regression Analysis
-Foundations of Experimental Design
-Electives
Second Year
-Electives
-Capstone

Concentration areas

-Predictive Analytics
-Data Mining/Machine Learning
-Industrial
-Biostatistics
-Theory

Electives, capstone or thesis

Elective courses are chosen by the student with the help of their adviser. These courses are usually department courses but may include (along with transfer credits) up to 6 credit hours from other departments that are consistent with students’ professional objectives. The capstone course is designed to ensure that students can integrate the knowledge from their courses to solve more complex problems. This course is taken near the end of a student’s course of study. Students, with adviser approval, may write a thesis as their capstone.

Other admission requirements

-Have a satisfactory background in mathematics (one year of university-level calculus) and statistics (preferably two courses in probability and statistics).
-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit a current resume.
-Submit two letters of recommendation, and complete a graduate application.
-International students whose native language is not English must submit scores from the Test of English as a Foreign Language (TOEFL).
-Scores from the Graduate Record Exam (GRE) are not required, however submitting scores may support the admission of an applicant who is deficient in certain admission requirements.

Additional information

Grades:
Students must attain an overall program grade-point average of 3.0 (B) for graduation.

Maximum time limit:
University policy requires that graduate programs be completed within seven years of the student's initial registration for courses in the program. Bridge courses are excluded.

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Programme description

Given the ever-increasing volume of data that businesses have access to today, there is a demand for employees who have computational and analytical skills and who can help inform business decisions to increase efficiency and ultimately build a competitive edge. Our Masters in Business Analytics has been developed with just that in mind and aims to train students to understand the variety of business environments as well as importance and relevance of decision problems facing organisations and ways of solving these using a variety of techniques.

Students will learn how to apply descriptive, predictive, and prescriptive analytics of big data concepts and techniques to generate valuable insight that can assist with decision making. Last, but not least, the Masters will provide students with hands-on experience in applying these concepts and using these techniques and most of the popular analytics software through their projects including industry projects. In sum, the programme aims to equip students with the skills required to interpret, conceptualise and convert Big Data into useful information and thus enabling students to make better informed decisions as future business managers.

Programme structure

All students attend and complete four core courses in Semester 1 and four option courses in Semester 2. The programme concludes with a 60-credit Dissertation. Details of core and option courses are shown below.

Core courses - Semester 1

• Principles of Data Analytics
• Predictive Analytics and Modelling of Data
• Prescriptive Analytics with Mathematical Programming
• Business Analytics with Heuristics

Option courses - Semester 2

• Performance Analytics with DEA
• Media and Web Analytics
• Industrial Analytics
• Business Analytics with Simulation
• Inventory Analytics
• Supply Chain Analytics with Games

The Dissertation: Each student conducts an individual research project on a subject within business analytics, chosen in consultation with an academic supervisor. MSc candidates write up their work as a Dissertation (maximum 15,000 words with a maximum of 10% margin either way), which is submitted by the end of August. Some Dissertation topics are offered that involve working with business, NGOs or other organisations.

Learning outcomes

General Educational aims are to:

• Enable all participants to recognise, understand and apply the language, theory and models of the field of business analytics.
• Foster an ability to critically analyse, synthesise and solve complex unstructured business problems.
• Encourage an aptitude for business improvement, innovation and entrepreneurial action.
• Encourage the sharing of experiences to enhance the benefits of collaborative learning.
• Instil a sense of ethical decision-making and a commitment to the long-run welfare of both organisations and the communities that they serve.

Specific Educational aims are to:

• Understand and critically apply the concepts and methods of business analytics.
• Identify, model and solve decision problems in different settings
• Interpret results/solutions and identify appropriate courses of action for a given managerial situation whether a problem or an opportunity.
• Create viable solutions to decision making problems.

Career opportunities

It is anticipated that the demand for data analytics employees will continue and indeed grow as analytics is viewed as a key competitive resource by many companies. If market forecasts are anything to go by then the Big Data technology and services market will grow at a 26.4% annual growth rate to $41.5 billion through 2018, or about 6 times the growth rate of the overall information technology market (Source: IDC). This will inevitably lead to further growth of the market for employees in the area of data analytics.

The new MSc in Business Analytics will offer students from a range of degree backgrounds the opportunity to equip themselves with an artillery of concepts, methods and applications of business analytics along with hands-on and practical experience in applying them to industry projects in different business settings. It is not just about being able to analyse and digest the data available but to then translate this into effective decision-making. In sum, we expect the new programme to open a range of career pathways in analytics for our graduates including business consultants, business analysts, business intelligence & analytics consultants, metrics & analytics specialists, analytics associates, data analysts, solution architects, business process analysts, management consulting associates, and operational research consultants.

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Since the world went online, information has grown rapidly in volume and become infinitely more accessible. At the same time, information science and systems have been converging towards a common focus on information discovery, organisation, and management. Information management is essential in libraries, archives, museums and business, and is a much sought-after skill in careers spanning the sectors for example, in governmental, legal, financial, media and publishing organisations. Meanwhile, owners and users of information need to be able to access and evaluate information in faster and more intuitive ways.

Key benefits

This course is accredited by The Chartered Institute of Library and Information Professionals (CILIP).

Course detail

The MSc Information Management is vocational and practice-oriented, designed to support information and knowledge managers. The course provides an excellent balance of traditional information management and library science, informed by cutting edge developments in information architecture and data management. It's an important route for anyone seeking professional chartership or progress to management roles.

Modules

• Information Contexts (30 credits)
• Knowledge Organisation (30 credits)
• Information and Digital Literacy (15 credits)
• Personal and Organisational Management (15 credits)
• Information and Knowledge Management
• Data Management
• Designing The User Experience
• Big Data
• Cloud Computing
• Linked, Open Data and The Internet of Things
• Machine Learning and Predictive Analytics
• Social Media and Web Science
• Dissertation

Format

You'll learn through lectures, discussions, tutorials, practical exercises and independent reading, as well as working together in small groups.

The course has a virtual learning environment online that supports you throughout your studies. It's a useful way to communicate with fellow students and teaching staff, find administrative details about the modules, and access course materials.

We regularly welcome specialist tutors to the department to contribute to specific modules.

Assessment

Assessment in most modules is through written coursework, portfolios, presentations and written exams. The supervisor and second marker will assess your dissertation.

Careers / Further study

This qualification is an excellent route to range of careers, and as a complement to existing career skills and professional development for example, for those moving into managerial roles. Our graduates have gone on to successful careers in a wide range of sectors, including educational, public sector and museum archivist roles, plus a variety of consultancy and professional services positions.

Alumni have prominent roles in local library services, university libraries in Bristol and Bath, with the government, and in records management roles in public and private sectors.

For anyone looking to pursue PhD research positions, this course is considered a highly valuable preparatory route.

How to apply

Information on applications can be found at the following link: http://www1.uwe.ac.uk/study/applyingtouwebristol/postgraduateapplications.aspx

Funding

- New Postgraduate Master's loans for 2016/17 academic year –

The government are introducing a master’s loan scheme, whereby master’s students under 60 can access a loan of up to £10,000 as a contribution towards the cost of their study. This is part of the government’s long-term commitment to enhance support for postgraduate study.

Scholarships and other sources of funding are also available.

More information can be found here: http://www1.uwe.ac.uk/students/feesandfunding/fundingandscholarships/postgraduatefunding.aspx

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The MSc Information Technology prepares you for the intellectual, analytical and practical challenges of a career in IT. You will develop the knowledge and skills necessary to collate information, define, design and build or select the most appropriate IT solutions and develop a deeper understanding of how those solutions apply to professional contexts.

You can study part-time whilst in full-time employment, as there are few pre-requisites for most modules.

Key benefits

The British Computer Society's Professional Graduate Diploma (PGD) is accepted as an appropriate entry qualification for this course. The PGD is also accepted as advanced standing to IT-related undergraduate degrees at UWE Bristol.

Course detail

Upon graduation, you will be able to critically evaluate developments and new applications of information and communication technology systems, and relate them to the roles and uses of IT in different business settings. You will also have an enhanced understanding of system problems and how to choose the right methods and approaches to develop systems. From engaging in primary research for the dissertation and studying relevant literature, you will learn to recognise what constitutes suitable research questions or hypotheses and the appropriate perspectives for analysis.

Structure

The full Masters course is made up of 180 credits divided into three 60 credit stages: Postgraduate Certificate, Postgraduate Diploma and Masters. You will work incrementally through the three stages and need to pass all modules at each stage to be able to progress onto the next.

The MSc route is structured so that three quarters of the taught course comprises core modules, while you choose the rest to fit your personal and professional aspirations and career goals. In the first term, there are four core modules, followed by one core module and two optional ones in the second term.

Modules

Core modules:

• Professionalism And Governance In IT (15 credits)
• Project Management (15 credits)
• Information Security (15 credits)
• Digital Design and Development (15 credits)
• Group Software Development Project (30 credits)

Optional modules:

• Information and Knowledge Management
• Data Management
• Designing The User Experience
• Cloud Computing
• Linked, Open Data and The Internet of Things
• Machine Learning And Predictive Analytics
• Social Media And Web Science
• Big Data

Format

You learn through taught classes supported by independent study, and we provide considerable material online, via UWE Bristol's virtual learning environment, Blackboard.

Assessment

Assessment is through coursework and exams, and the dissertation.

Careers / Further study

UWE Bristol monitors its employment trends closely, and since 1986, we have ensured graduates of this course are equipped for the demands of the real world and are highly regarded by potential employers.

There is a growing need for creative IT graduates who can work with an ever-widening range of technologies and can meet organisational needs in business, education and health. This newly designed course tackles the challenges of technology in modern business and society, head on.

How to apply

Information on applications can be found at the following link: http://www1.uwe.ac.uk/study/applyingtouwebristol/postgraduateapplications.aspx

Funding

- New Postgraduate Master's loans for 2016/17 academic year –

The government are introducing a master’s loan scheme, whereby master’s students under 60 can access a loan of up to £10,000 as a contribution towards the cost of their study. This is part of the government’s long-term commitment to enhance support for postgraduate study.

Scholarships and other sources of funding are also available.

More information can be found here: http://www1.uwe.ac.uk/students/feesandfunding/fundingandscholarships/postgraduatefunding.aspx

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Business Intelligence is basically about concepts and methods to improve business decision making by using fact-based support systems. Business Intelligence as a discipline is made up of several related activities, including data mining, analytical processing and business process improvement.

The programme provides you with in-depth knowledge of methods for analysing data to support decision-making and for improving business processes on the basis of business analytics.

The programme provides you with an in-depth knowledge about

- Methods for analysing data to support decision making
- How to improve business processes on the basis of business analytics

The courses of the programme will provide you with analytical skills to identify new business opportunities or identify inefficient business processes. The teaching form of the program encourages student participation and this in combination with the final thesis work will provide you with self-management and communication skills.

PROGRAMME STRUCTURE

PREREQUISITE COURSES

In the first semester you follow the prerequisite courses that form the methodological and academic basis for the further study programme.

In Business Analytics gives the student a set of tools and models that are essential for the design and evaluation of empirical investigations that can support decisions in the business intelligence area. The course will cover major research tools including research design, experiments,response models and forecasting.

IS Development & Implementation in a Business Context introduces a range of methods and techniques that can be used to understand, plan and execute the processes in which information systems are developed, implemented, evaluated and modified to enable the student to participate in the development, acquisition and implementation of information systems.

Data Warehousing provides the student with knowledge about the wide variety of database management systems available for a data warehouse solution and how to choose a solution that is relevant for the business intelligence project in question.

SAS and SQL for Business Analytics provides the student with skills to conduct proper data analysis using some of the most flexible environments available. Focus will be on data management and data manipulation with the purpose to prepare for a statistical analysis.

SPECIALISATION COURSES

In the second semester you follow the specialisation courses of the programme.

Data Mining for Business Decisions teaches students how to work with large datasets and how relationships in such data can be detected with the purpose to transform data into knowledge. Business applications cover a broad range from marketing to accounting, logistics and supply chain management.

In Advanced Market Research the focus is on analytical customer relationship management. The course is devoted to customer base analysis and predictive modelling with a primary focus on customer lifetime value and customer retention.

Supply Chain Management aims to provide an introduction and a framework of the design and operations of performance management in contemporary supply chains.

Project Management aims to introduce the contents of general project management competences and provide the students with skills to manage a BI project.

In the third semester you can choose elective courses within your areas of interest. The courses can either be taken at the school during the semester, at the Summer University or at one of our more than 200 partner universities abroad. You can also participate in internship programmes either in Denmark or abroad.

The fourth semester is devoted to the final thesis. You may choose the topic of the thesis freely and so get a chance to concentrate on and specialise in a specific field of interest. The thesis may be written in collaboration with another student or it may be the result of your individual effort. When the thesis has been submitted, it is defended before the academic advisor as well as an external examiner.

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On our MSc Algorithmic Trading, we equip you with the core concepts and quantitative methods in high frequency finance, along with the operational skills to use state-of-the-art computational methods for financial modelling.

We enable you to attain an understanding of financial markets at the level of individual trades occurring over sub-millisecond timescales, and apply this to the development of real-time approaches to trading and risk-management.

The course includes hands-on projects on topics such as order book analysis, VWAP & TWAP, pairs trading, statistical arbitrage, and market impact functions. You have the opportunity to study the use of financial market simulators for stress testing trading strategies, and designing electronic trading platforms.

In addition to traditional topics in financial econometrics and market microstructure theory, we put special emphasis on areas:
-Statistical and computational methods
-Modelling trading strategies and predictive services that are deployed by hedge funds
-Algorithmic trading groups
-Derivatives desks
-Risk management departments

Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation. We are supported by Essex’s highly rated Department of Economics, School of Computer Science and Electronic Engineering, and Essex Business School.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

Professional accreditation

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

This course is taught by experts with both academic and industrial expertise in the financial and IT sectors. We bring together leading academics in the field from our departments of economics, computer science and business.

Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management.

More broadly, our research covers a range of topics, from materials science and semiconductor device physics, to the theory of computation and the philosophy of computer science, with most of our research groups based around laboratories offering world-class facilities.

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-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
-Students 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

Your future

We have an extensive network of industrial contacts through our City Associates Board and our alumni, while our expert seminar series gives you the opportunity to work with leading figures from industry.

Our recent graduates have gone on to become quantitative analysts, portfolio managers and software engineers at various institutions, including:
-HSBC
-Mitsubishi UFJ Securities
-Old Mutual
-Bank of England

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-CCFEA MSc Dissertation
-Big-Data for Computational Finance
-High Frequency Finance and Empirical Market Microstructure
-Introduction to Financial Market Analysis
-Professional Practice and Research Methodology
-Quantitative Methods in Finance and Trading
-Trading Global Financial Markets
-Cloud Technologies and Systems (optional)
-Constraint Satisfaction for Decision Making (optional)
-Creating and Growing a New Business Venture (optional)
-Digital Signal Processing (optional)
-Evolutionary Computation and Genetic Programming (optional)
-Financial Engineering and Risk Management (optional)
-High Performance Computing (optional)
-Industry Expert Lectures in Finance (optional)
-Learning and Computational Intelligence in Economics and Finance (optional)
-Mathematical Research Techniques Using Matlab (optional)
-Programming in Python (optional)
-Text Analytics (optional)

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