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Masters Degrees (Data Analytic)

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We offer a suite of Masters programmes at Stirling. This is a one year, full time taught MSc. designed to lead to a job in data science or analytics. Read more

Introduction

We offer a suite of Masters programmes at Stirling.
This is a one year, full time taught MSc. designed to lead to a job in data science or analytics.
Big Data skills are in high demand and they attract high salaries. The MSc Big Data at the University of Stirling is a taught advanced Master's degree covering the technology of Big Data and the science of data analytics.
The course is taught in the beautiful Stirling campus in the heart of Scotland with support from companies who recruit data scientists.
The course covers Big Data technology, advanced analytics and industrial and scientific applications. The syllabus includes:
- Mathematics for Big Data
- Python scripting
- Big Data theory and computing foundations
- Big databases and NoSQL
- Analytics, machine learning and data visualisation
- Optimisation and heuristics for big problems
- Hadoop and MapReduce
- Scientific and commercial applications
- Student projects

Key information

- Degree type: MSc
- Duration: One year
- Start date: September
- Course Director: Kevin Swingler

Course objectives

- An understanding of the issues of scalability of databases, data analysis, search and optimisation
- The ability to choose the right solution for a commercial task involving big data, including databases, architectures and cloud services
- An understanding of the analysis of big data including methods to visualise and automatically learn from vast quantities of data
- An appreciation of the size of search spaces in large problems and the ability to choose an appropriate heuristic to find a near optimal solution
- The programming skills to build simple solutions using big data technologies such as MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for multi processor execution.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS: 6.0 with 5.5 minimum in each skill
- Cambridge Certificate of Proficiency in English (CPE): Grade C
- Cambridge Certificate of Advanced English (CAE): Grade C
- Pearson Test of English (Academic): 54 with 51 in each component
- IBT TOEFL: 80 with no subtest less than 17

For more information go to English language requirements https://www.stir.ac.uk/study-in-the-uk/entry-requirements/english/

If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View the range of pre-sessional courses http://www.intohigher.com/uk/en-gb/our-centres/into-university-of-stirling/studying/our-courses/course-list/pre-sessional-english.aspx .

Structure and content

Our Big Data MSc is a mix of practical technology such as Hadoop, NoSQL, and Map-Reduce, important maths and computing theory, and advanced computational techniques. The course will teach you what you need to know to collect, manage and analyse big, fast moving data for science or commerce

REF2014

In REF2014 Stirling was placed 6th in Scotland and 45th in the UK with almost three quarters of research activity rated either world-leading or internationally excellent.

Strengths

Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The data lab with facilitate industry involvement and collaboration and provide funding and resources for students.
The Stirling MSc in Big Data has been developed in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Big Data projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The course features a long summer project, generally in partnership with a company or technology provider, that provides students with a showcase of their skills to take to employers or launch online.
We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Career opportunities

Demand for people with big data skills is projected to grow rapidly in the coming years. Average salaries are higher in Big Data jobs than the IT average and the skills shortage will make that gap bigger.
The Stirling Big Data MSc is run in partnership with industry and is designed to produce graduates with the skills that companies need.
e-Skills UK estimate that:
- The number of Big Data jobs in the UK rose by 41% from 2012 - 2013
- By 2020 there will be 56,000 Big Data jobs in the UK alone
- Big Data professionals earn on average 31% more than other IT professionals
- 77% of companies say it is difficult to recruit people with the Big Data skill they need

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The opportunity to exploit Big Data is recognised world-wide and some countries include it in their economic strategies. The UK Government identified Big Data as one of the 8 great technologies which will have a strong impact on growth and the Scottish Government highlights it as an emerging opportunity for Scotland. Read more
The opportunity to exploit Big Data is recognised world-wide and some countries include it in their economic strategies. The UK Government identified Big Data as one of the 8 great technologies which will have a strong impact on growth and the Scottish Government highlights it as an emerging opportunity for Scotland.

Our MSc in Data Science aims to produce specialist data scientists with training in industry relevant data acquisition, storage, warehousing, analytics and visualisation tools and techniques and a good understanding of the needs of industry. The course will prepare graduates in technical disciplines for a career in the design and implementation use of computer-analytics and visualisation solutions for industry.

Visit the website: http://www.rgu.ac.uk/computing/study-options/postgraduate/masters-in-data-science

Course detail

The course will focus on satisfying industry’s demand for data scientists who have the ability to:

• Apply appropriate data science tools and techniques to industry’s data in order to uncover important, previously unknown information only implicit in the data.
• Relate a company’s key performance indicators to a data science problem area in order to focus a data science task.
• Handle large amounts of real-time, non-persistent, data.
• Contribute to business decision-making by effectively communicating (potentially large volumes of) key data visually.
• Understand, clean up, summarise, interpret and manage data.
• Grasp key knowledge about new problem areas in order to communicate with end-users; understand key business needs and processes and identify added value through data analytics.
• Provide user-centred data analytics at an appropriate level.
• Protect and share data as appropriate.

The course will emphasise Big Data, covering not only traditional data management systems but also systems where data and/or its storage is unstructured.

Format

Throughout the course, content is complemented by practical work, allowing you to support your theoretical knowledge with practical experience in data storage, mining, warehousing, visualisation and analysis as well as transferrable skills. You will be taught through a mixture of lectures, tutorials, labs. You will be invited to attend talks presented by highly-experienced researchers, speakers from industry, and members of the BCS (British Computer Society) on a wide range of industry-related topics. You will also be supported through our online virtual learning environment where you can access a wide variety of resources and other support materials.

The individual project provides an opportunity for applying specialist knowledge together with analytic, problem-solving, managerial and communication skills to a particular area of interest within data science. Working with the full support and guidance of an allocated project supervisor, you will be given the opportunity to propose, plan, specify, develop, evaluate, and present a substantial project.

Placements and accreditation

Students who perform particularly well during their first semester of studies will be invited to apply for a 45-week internship.

Careers

The course prepares you for a career in Data Science. Job openings include: Data Scientist, Data Analyst, Data Visualisation Specialist, Data Manager, Database Designer/Manager, Data Mining Expert and Big Data Scientist.

Aberdeen is home to many multinational oil and gas companies and associated suppliers such as mainstream software houses, IT providers to major oil-related companies, specialist software consultancies, and venture capital start-ups.

The university is involved in a number of commercial collaborations on a local, national and international scale with organisations such as BP, British Geological Survey, Wood Group PSN, Accenture, WIPRO and many Aberdeen-based software development companies.

The course also prepares students for research careers by providing the skills necessary of an effective researcher. Suitable MSc graduates may continue to PhD programmes within the school.

How to apply

To find out how to apply, use the following link: http://www.rgu.ac.uk/applyonline

Funding

For information on funding, including loans, scholarships and Disabled Students Allowance (DSA) please click the following link: http://www.rgu.ac.uk/future-students/finance-and-scholarships/financial-support/uk-students/postgraduate-students/postgraduate-students/

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Businesses now frequently possess and want to exploit huge, high volume, varied dynamic data sets, known as big data. Analytics is a subset of what has become to be called Business Intelligence. Read more

About the course

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

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

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

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

Reasons to study:

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

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

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

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

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

Course Structure

Modules

First semester (September to January)

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

Second semester (February to May)

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

Third semester (June to September)

• Individual project

Teaching and Assessment

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

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

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

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

Contact and learning hours

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

Industry Association

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

To find out more

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

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

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

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This programme is ideal if you wish to use your analytic skills to derive and obtain useful insights from large amounts of data. Read more
This programme is ideal if you wish to use your analytic skills to derive and obtain useful insights from large amounts of data. By equipping you with the rigorous modelling and consulting skills needed to understand, manage and communicate useful insights from ‘big data’, it prepares you to inform business decisions or government policies.

Taught modules are delivered by our group of internationally recognised management scientists who are actively working with business, government and non-profit organisations to tackle routine, strategic or policy problems.

Our industry advisory board ensures that the focus of our taught modules is of both academic and practical relevance. IBM, our partner, has jointly developed with us two modules (Customer Analytics and Leading Analytics Initiatives), and sponsors a student prize.

During the summer, you will undertake a supervised consulting or research project. This will give you the opportunity to apply powerful tools such as data mining, forecasting, optimisation, simulation and decision analysis to a particular area of business or policy, equipping you with skills highly prized by employers.

Core study areas include consulting for analytics, discovery analytics, decision analytics, managing big data, customer analytics, leading analytics initiatives, operations analytics, policy and strategy analytics, and a consulting or research project.

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/business-economics/business-analytics-consulting/

Programme modules

Semester 1:
- Consulting for Analytics
You will learn the craft and skills required by analytic consultants, and which employers look for but often find lacking. It will cover process aspects of analytics projects, as well as skills in client interaction, problem structuring and data elicitation (with individuals and/or groups, and with hard/soft data), presenting data-driven analyses, report writing, and developing simple bespoke decision support systems.

- Discovery Analytics
You will be introduced to common statistical methods to explore and visualise cross sectional and temporal data. You will also learn about the design and conduct of data collection efforts, together with methods for dealing with data outliers and missing data. Industry-leading tools that are in high demand from employers (e.g. SAS and SPSS) will be used.

- Decision Analytics
Your will be introduced to common operational research techniques to help determine the best course of action for a given decision or problem. Topics covered include optimisation, simulation and decision and risk analysis.

- Managing Big Data
Your will learn about the challenges and opportunities derived from the increased volume, variety, velocity and value of data that is available today. A range of big data topics will be covered including data type, data integration, data technologies, and data security.

Semester 2:
- Customer Analytics
You will focus on analytics techniques that can help organisations gain a deeper insight into customers’ behaviour and attitudes towards their products and services. It will cover approaches designed to provide a profile of customer segments, such as those grounded in data mining and multivariate statistical analysis. Industry-leading tools that are in high demand from employers (e.g. SAS and SPSS) will be used. There is an IBM sponsored student prize on this module.

- Leading Analytics Initiatives
You will learn about the issues associated with implementing an analytics capability in organisations. It will cover topics on how to develop an analytics strategy, how to embed analytics in organisational processes to ensure they deliver value, and how to deploy analytics throughout the organisation to improve decision making. There is an IBM sponsored student prize on this module.

- Operations Analytics
You will focus on analytics techniques that can help organisations to develop a better understanding of operational processes, and identify efficiency and cost reduction opportunities. Topics covered include advanced optimisation and simulation techniques.

- Policy and Strategy Analytics
You will focus on analytics techniques designed to tackle complex policy and strategic issues. It will cover approaches designed to explain the behaviour of complex social systems or assess the consequences of complex decisions, in order to provide the levers for policy and strategy making in a variety of sectors.

Summer:
- Consulting or Research Project

Assessment

Taught modules are assessed by a mixture of coursework and examinations.
The summer project is assessed via a written dissertation.

Careers and further study

Business analytics is a new and rapidly developing field, and individuals with analytics skills are in short supply.
Graduates from this programme can expect to work as management consultants, business analysts, policy analysts, marketing researchers, operations researchers, and data scientists.
We have developed two modules - Customer Analytics and Leading Analytics Initiatives - in close collaboration with our partner IBM, who also sponsor a student prize.

Why choose business and economics at Loughborough?

Loughborough’s School of Business and Economics is a thriving forward-looking centre of education that aims to provide an exceptional learning experience.

Consistently ranked as a Top-10 UK business school by national league tables, our graduates are highly employable and enjoy starting salaries well above the national average.

The rich variety of postgraduate programmes we offer ranges from taught masters, MBA and doctoral programmes, to short courses and executive education, with subjects spanning Management, Marketing, Finance and Economics, Work Psychology, Business Analytics, International Crisis Management and Information Management. New for 2016, we are also launching two exciting new programmes in Human Resource Management. All of this contributes to a lively and supportive learning environment within the School.

- Internationally Accredited
The School of Business and Economics is one of less than 1% of business schools in the world to have achieved accreditation from all three major international accrediting bodies: The Association to Advance Collegiate Schools of Business (AACSB International), EQUIS accreditation from the European Foundation for Management Development (EFMD) and the Association of MBAs (AMBA).

- Career Prospects
Our graduates are in great demand. Over 94% of our postgraduate students were in work and/or further study six months after graduating.* As such, you will be equipped with skills and knowledge that will serve you well in your career or enable you to pursue further study and research.

*Source: DLHE

Find out how to apply here http://www.lboro.ac.uk/study/postgraduate/programmes/departments/business-economics/business-analytics-consulting/

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‌Policy-making in all sectors has become more data driven and advanced training in issues of data use, data sharing, transparency, and accountability are important for both the public and private sectors. Read more
‌Policy-making in all sectors has become more data driven and advanced training in issues of data use, data sharing, transparency, and accountability are important for both the public and private sectors. ‌ Unlocking the potential of collecting, sharing and analysing massive amounts of administrative and economic, social and political information to bring economic and social benefits requires individuals trained in both policy analysis and data analytics.

The MSc in Policy Analytics provides training for graduates who want to develop the ability to apply data analysis techniques to a range of substantive and policy related questions.

The MSc in Policy Analytics offers rigorous data analytic training alongside a specialisation in a policy subfield (e.g. social and family policy, economic and public policy, environment, criminal justice, security). It equips students with the technical understanding of a range of data analytic techniques and the practical software and programming skills to implement these methods to address their own research questions. The programme is designed to enable students to carry out their own research and to equip them to pursue other professional research activities subsequently.

This MSc is an extension of the successful Q-Step programme training social scientist undergraduates in quantitative methods and is one of three data science MSc delivered at Exeter.

Employability and work placements

All students enrolled on our Q-Step undergraduate and postgraduate programmes have the opportunity to undertake a work placement with one of our industry partners. A work placement will allow you to get hands-on analysis experience for a period of 2 to 11 weeks.

Work placements students will also receive a bursary of up to £2,000 to support them during the placement.

There are wide variety of placements to choose from locally, nationally and in Europe with a variety of public sector organisations, non-governmental organisations (NGOs) and industry.

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The Medicine MRes is a strongly research-based programme, which gives you the training and opportunity to develop as a scientist or scientifically-literate clinician. Read more

The Medicine MRes is a strongly research-based programme, which gives you the training and opportunity to develop as a scientist or scientifically-literate clinician.

It can help you to gain:

  • a comprehensive understanding of the concepts and techniques relevant to medical research
  • the ability to critically and creatively evaluate current issues in medicine and health.

You’ll get experience in formulating new hypotheses and exploring the causes and consequences of diseases by conducting rigorous scientific research in a laboratory or with patients.

A nine-month research project helps you to develop specialised knowledge, as well as design and undertake a substantial piece of publishable research. You’ll be based in one of our internationally-renowned institutes and be supervised by leading experts in their field. You can choose from a range of research opportunities in applied health, cancer and pathology, cardiovascular, genes and development, medical education and musculoskeletal topics.

We invite you to view our list of research projects 2017-18. Please contact the supervisory team before applying to the course and in your application state your three preferred research projects.

More information

The School of Medicine is a major international centre for research and education. Our ambition is to improve health and reduce health inequalities, locally and globally, through excellent research and its translation into healthcare practice, and the education of future scientific and clinical leaders who will advocate and practise an evidence-based approach.

Course content

The taught modules are designed to stimulate a deep and critical knowledge of research. The optional modules allow you to develop a comprehensive knowledge of different approaches to medical research.

The Paper Criticism module enables you to develop subject-specific skills, such as an understanding of the ethical issues of medicine and knowledge of the current requirements for the governance of medical research and its publication. You apply your knowledge of research methods to published papers and enhance your critical skills.

The Analytic Research module provides a critical awareness of research planning and methods and develops your research skills. It includes topics on the structure of analytic research investigations; the analysis of the data obtained in analytic studies, especially the metrics used; the problems resulting from bias and confounding and how they are dealt with; basic statistics of precision and comparison;dealing with unequal duration of follow-up in cohort studies; and critical appraisal of published research.

The Capturing and Handling Data in Research module is an introduction to the collection and handling of health research data. It will include topics on: social inclusion in research; sampling from populations; types of data; collecting data through questionnaires; how scales and tests are used to collect data; and how data are collected and described using various fractions such as rates, ratios, risks and odds; recording quantitative and qualitative data in suitable formats; using computers in the analysis of data; the importance of the statistics that summarise quantitative data; and an introduction to the analysis of quantitative and qualitative data. Critical appraisal of published research will underpin theory.

Course structure

Compulsory modules

  • Analytic Research 15 credits
  • Intervention Research 15 credits
  • Capturing and Handling Data in Research 15 credits
  • Research Project in Medicine 120 credits

For more information on typical modules, read Medicine MRes in the course catalogue

Learning and teaching

There are few formal lectures in the MRes programme. Most of your time is devoted to planning and conducting the research project, usually working with a small team of researchers or healthcare professionals.

Interactive tutorial sessions are shared with students on other Masters programmes in the School of Medicine, intercalating medical students and health professionals.

Assessment

There is one examination in May for the Paper Criticism module. Other modules are assessed by the submission of coursework, workbooks, reports and reviews.

Exit awards of Postgraduate Diploma in Medical Research (120 credits) or Postgraduate Certificate in Medical Research (60 credits) are available for this programme.

Career opportunities

The Master of Research in Medicine is for people who want to pursue a lifelong career in academic medicine research.

For medical students, the addition of the Medicine MRes on your CV is an advantage when applying for Academic Foundation Posts and Specialist Training Posts in the NHS.

Careers support

We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.

The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.



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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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This exciting new course is the first in Scotland to be run in partnership with SAS, a global leader in business analytic solutions. Read more

This exciting new course is the first in Scotland to be run in partnership with SAS, a global leader in business analytic solutions. It has been developed in close collaboration with leading financial services organisation HSBC and The Data Lab, to produce graduates with the skills that industry needs.

The course addresses the acknowledged shortage of business leaders and managers with a detailed working knowledge of data analytics. It provides students with a deep understanding of the industrial and scientific relevance of advanced analytics and their application in strategic and operational decision making. In doing so it develops graduates with highly sought after blend of data analytics, business acumen and advanced management skills.

The course gives you the opportunity to develop specialist skills by choosing elective modules from finance, marketing, data science and management. You’ll work in real life consultancy-style projects alongside industry leaders in data analytics. Our extensive network of external organisations across all sectors provides opportunities to build your knowledge, skills and experience for a successful career in business analytic roles.

Key information

-Degree type: MSc

-Study methods: Full time, Campus based

-Duration: 12 months

-Start date: September

-Location: Stirling Campus

Structure and content

Begin your course in the best possible way, with our unique Flying Start Leadership programme. It’s exclusively for new students about to start one of our postgraduate courses. You’ll take part in a wide range of group and individual activities, workshops and information sessions to help you prepare for the year ahead, and make the most of your time at the University of Stirling Management School.

Attend the Flying Start Leadership programme and you will:

-Discover more about the exciting period of learning ahead.

-Understand what is expected of you in your course.

-Get to know the teaching and support staff.

-Learn more about a diverse range of approaches to learning.

-Work as part of a successful team.

-Develop your personal goals for the year ahead.

Activities range from practical skills – such as effective public speaking – to developing ways to work in groups with other students. The programme is also a chance to discover all that the University and the vibrant city of Stirling has to offer, and to make new friends.

About the Faculty

The University of Stirling Management School is recognised as a centre of academic excellence that offers innovative scholarship and widespread engagement with a variety of stakeholders to improve and transform business, society and lives.

The rigour and relevance of the School’s research is represented in the way projects are sponsored and funded by leading domestic research institutes as well as international bodies who value the research expertise offered.

Other admission requirements

If you don’t meet the required criteria for this course, you can complete the Graduate Diploma in Business, Finance and Sport to gain a guaranteed entry onto this Master's degree.

INTO University of Stirling offers a Graduate Diploma for those students who do not meet the required criteria for this course. If you successfully complete the Graduate Diploma in Business, Finance and Sport and meet the required progression grades, you will be guaranteed entry onto this Master's degree.

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:

-IELTS: 6.0 with 5.5 minimum in each skill.

-Cambridge Certificate of Proficiency in English (CPE): Grade C or above.

-Cambridge Certificate of Advanced English (CAE): Grade C or above.

-Pearson Test of English (Academic): 54 with 51 in each component.

-IBT TOEFL: 80 with no subtest less than 17.



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This Research Methods in Psychology (RMP) Master’s programme was developed to conform to the guidelines for ESRC recognition of postgraduate training. Read more
This Research Methods in Psychology (RMP) Master’s programme was developed to conform to the guidelines for ESRC recognition of postgraduate training.

The programme provides advanced training in a variety of research methods and data analytic techniques.
The main aim is to equip you to conduct research in your chosen field that is of high quality and impact. In order to promote self-motivated learning we have designed a programme in which you apply the skills you are taught to your own research questions. Therefore, in addition to classroom instruction, you will receive structured support as you develop your research questions, plan and execute the research, analyze the data, and prepare reports describing your findings.

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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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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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The focus of this programme is on contemporary substantive issues in criminology and criminal justice and on criminological research methods. Read more
The focus of this programme is on contemporary substantive issues in criminology and criminal justice and on criminological research methods. It is particularly appropriate for those engaged in criminal justice policy analysis and development or similar work in allied fields.

The programme develops a theoretical, policy and technical understanding of key issues within criminology, criminal justice and research methods. More specifically, it aims to develop an advanced understanding of the complex nature of crime, harm and victimisation together with an appreciation of the role of the state/criminal justice system in the regulation of human behaviour, deviance and crime. The programme will equip you to design and implement social scientific research using a broad range of methodologies, consider research ethics, analyse and present the material such research generates.

Through combining criminology and research methods, the programme enables you to think logically and in an informed manner about criminological issues. The programme fosters a critical awareness of the relationship between theory, policy and practice and enables you to utilize your research knowledge of research skills and translate these into research practice in the field of criminology and broader social science research professions.

Modules

You'll undertake modules from a broad base of subject areas including:

Criminological theory
Crime, harm and victimisation
Responding to crime: justice, social control and punishment
Criminological research in practice
Methods for social research and evaluation: philosophy, design and data collection
Data analytic techniques for social scientists
Dissertation

Study hours

Year 1 class contact time is typically 6 hours per week part time and 12 hours per week full time plus individual tutorial and independent study.

Placements

Our criminology programme also has a strong voluntary work scheme.You're encouraged to undertake voluntary work in a variety of criminal justice related agencies. Recent positions have been within the police service, the prison service, legal advice, victim support, domestic violence and child abuse agencies and youth offending and youth mentoring schemes.

Professional links

The Crime and Criminal Justice Research Group, (CCJRG), at LSBU has developed a strong national and international reputation for delivering high quality and real life impact research. It has worked closely with a range of government agencies, including the Office for Criminal Justice Reform (Ministry of Justice); Government Office for London; the Scottish Executive, Northern Ireland Office and the Equalities and Human Rights Commission. It has also undertaken extensive research in collaboration with various London local authorities together with a range of voluntary and charity-based agencies.

Employability

This MSc will enable you to pursue a range of professional careers in criminal justice related work in statutory, commercial or community voluntary sectors and operating at central, regional and local government levels, for example, the Home Office; police forces; local government; crime and disorder reduction partnerships and their equivalencies throughout the world.

The acquisition of specific criminological and research methods knowledge will also enhance the career opportunities if you are currently working in the field. The specialist focus on research methods also offers an excellent foundation for those interested in undertaking subsequent doctoral research in the field.

LSBU Employability Services

LSBU is committed to supporting you develop your employability and succeed in getting a job after you have graduated. Your qualification will certainly help, but in a competitive market you also need to work on your employability, and on your career search. Our Employability Service will support you in developing your skills, finding a job, interview techniques, work experience or an internship, and will help you assess what you need to do to get the job you want at the end of your course. LSBU offers a comprehensive Employability Service, with a range of initiatives to complement your studies, including:

• Direct engagement from employers who come in to interview and talk to students
• Job Shop and on-campus recruitment agencies to help your job search
• Mentoring and work shadowing schemes.

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This Masters in Social Policy and Social Research Methods is particularly significant if you are currently working in local authorities or the voluntary sector. Read more
This Masters in Social Policy and Social Research Methods is particularly significant if you are currently working in local authorities or the voluntary sector. The skills you learn will progress your career in social welfare policy development, delivery or research. Or it is also relevant if you are thinking of starting a career related to social policy in the public, voluntary or private sectors.
The focus of this course is on contemporary substantive issues in social policy development and delivery, and social policy research methods. You'll develop your theoretical, policy and technical understanding of key issues related to policy-making, social welfare delivery, equality and social justice, and research methods.

You'll gain an advanced understanding of national and international factors influencing policy development and implementation. The changing relationship between the State, voluntary sector and private sector in terms of social welfare delivery. You'll also explore how ideas of equality, diversity, justice and human rights shape institutions and the programmes they offer.

You'll engage with recent research linked to changing family forms and how family policy impacts on children and families. You'll be equipped to design and implement social scientific research using a broad range of methodologies, consider research ethics then analyse and present the material such research generates.

The course fosters a critical awareness of the relationship between theory, policy and practice and enables you to utilise your research knowledge and research skills and translate these into research practice in the field of social policy and broader social science research professions.

Flexible modes of study

You can choose between three modes lasting one, two or three years allowing you to study whilst maintaining other life commitments.

Modules

Social policy analysis
The voluntary sector and the state: protagonist or partner
Methods for social research and evaluation: philosophy, design and data collection
Approaches to social change: equality, social justice and human rights
Family policy
Data analytic techniques for social scientists
Dissertation

Teaching and learning

Modules are assessed by coursework. There are different kinds of writing required which include: a critical reading log, a self-reflective essay, a methodological critique of a research article, a research proposal, extended essays, an evaluation of social change and a dissertation.

Modules are supported by Moodle, the LSBU virtual learning environment where most course reading will be made available. The classroom is envisaged as a core learning environment where you can discuss new ideas but also to think how they can be applied to previous or current work or voluntary experiences. Attendance is crucial for building your knowledge and skills. You'll be making use of computer laboratories in order to develop your use of a range of programmes that can be used to analyse quantitative and qualitative methods.

Timetable

Full-time:
Eight or nine hours of scheduled teaching per week with an additional 25 hours of independent study.

Part-time:
Three hours of scheduled teaching per week with an additional six hours of independent study per module.

Placements

If you are not already working in an environment which is linked to social welfare you'll be encouraged to undertake voluntary work which will give you useful experience alongside the degree. In addition it may become used as a location where you can undertake primary research for your master's dissertation. The Employability team at LSBU can help students find voluntary placements.

Employability

This MSc will enable you to pursue a range of professional careers in areas linked to social policy and social welfare. You'll be able to access work in the statutory, commercial or voluntary sectors and operating at central, and local government levels, for example, local government; MORI, NSPCC and DEMOS. The acquisition of specific social policy and research methods knowledge will also enhance your career opportunities if you are currently working in the field in social policy development and delivery or in undertaking social policy related research. The specialist focus on research methods also offers an excellent foundation for those interested in undertaking subsequent doctoral research in the field.

LSBU Employability Services

LSBU is committed to supporting you develop your employability and succeed in getting a job after you have graduated. Your qualification will certainly help, but in a competitive market you also need to work on your employability, and on your career search. Our Employability Service will support you in developing your skills, finding a job, interview techniques, work experience or an internship, and will help you assess what you need to do to get the job you want at the end of your course. LSBU offers a comprehensive Employability Service, with a range of initiatives to complement your studies, including:

• Direct engagement from employers who come in to interview and talk to students
• Job Shop and on-campus recruitment agencies to help your job search
• Mentoring and work shadowing schemes.

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At the local, national and global level, we are witnessing an intense period of social transformation and fragmentation. Within this context, there is growing political and policy recognition of the need to better understand and thereby address social inequalities. Read more

At the local, national and global level, we are witnessing an intense period of social transformation and fragmentation. Within this context, there is growing political and policy recognition of the need to better understand and thereby address social inequalities. The social sciences have an important role to play in mapping and understanding how inequalities arise and in tackling their causes and consequences. Innovative developments in the social sciences are offering new methodological, theoretical and empirical insights into entrenched and emerging inequalities of status, resource, outcome and opportunity. This has inspired us to create an interdisciplinary programme focusing on inequality in all its forms and its social, political and economic implications.

This Masters programme equips students with the necessary knowledge and skills to engage in and contribute towards work that tackles the realities and effects of social inequality. Capitalising on academic and applied expertise in the School of Sociology and Social Policy and the Leeds Inequalities Research Network, this programme harnesses leading analytical approaches combining qualitative, quantitative and data analytic methods (in close collaboration with the School of Geography).

In addition to offering an advanced understanding of rising material inequality, the programme encourages an intersectional approach to understanding socio-economic stratification and how this links with physical (dis) ability, race, ethnicity, nationality, gender, sexuality, class and age. It provides a stimulating intellectual environment and cutting edge methodological approaches to comparing and contrasting the formation and consequences of inequalities across a range of national and international contexts. Through an examination of geopolitical and socioeconomic shifts, such as urbanisation and globalisation, students are actively supported to critically interrogate the contemporary character and extent of social inequality.

Research insight

Whilst undertaking this programme, students will join a vibrant and dynamic research led teaching and learning environment in the School of Sociology and Social Policy. You will benefit from the interdisciplinary expertise and extra-curricular activities hosted by the School and its research centres including those in Disability Studies, Ethnicity and Racism Studies, Interdisciplinary Gender Studies and Research into Families, the Life Course and Generations. You will also access events through the Leeds Social Sciences Institute (LSSI), which fosters cross-departmental collaboration, learning and impact, Students will also benefit from workshops on global inequalities by academic leaders from across campus and research seminars with external speakers; along with career development opportunities and events. As such, students can take advantage of academic and applied expertise both within and beyond the University whilst also developing specialist knowledge and transferable skills for their future career development in the public, private or third sector.

Course content

The programme bridges disciplinary divides to provide a detailed understanding of the ways in which social inequality manifests across diverse communities and contexts at the national and international level. It offers insight into the character, causes and consequences of social inequality, as well as forms of resistance and policy responses. It has a strong and innovative methodological focus, including traditional qualitative and quantitative approaches to the social analysis of inequality, as well as new approaches to data visualisation and analytics from across the social sciences. The programme uses a range of teaching methods, including lectures, seminars and workshops, complemented by a range of co-curricular activities partly facilitated through the Leeds Inequalities Research Network.

Course structure

The core modules of the programme introduce students to contemporary research on global inequalities of social difference and disadvantage, emphasizing a diversity of theoretical and research design strategies, including international evidence surrounding the shifting nature and extent of inequality. Students are able to tailor the programme according to their interests and needs by choosing from a specially selected range of optional modules, which address major social and economic inequalities across diverse social science subjects and substantive issues. As such, students can choose to develop in-depth specialist knowledge on a particular area and/or focus more generally on the social processes and arrangements that give rise to inequalities.

Compulsory modules

  • Inequalities: Exploring causes, Consequences and Interventions 30 credits
  • Geographic Data Analysis and Visualisation 15 credits
  • Dissertation 60 credits

PLUS TWO OF THE BELOW:

  • Quantitative Research Methods 15 credits
  • Qualitative Research Methods 15 credits
  • Applied Population and Demographic Analysis

For more information on typical modules, read Inequalities and Social Science MSc Full Time in the course catalogue

Learning and teaching

We use a range of teaching and learning methods including presentations, seminars, workshops, tutorials and lectures. However, independent study is crucial to this degree – it allows you to prepare for taught sessions, develop your research interests and build a range of skills. This is particularly the case for the dissertation/applied project module of this programme.

Supported through workshops and supervision, students develop their research dissertation or an applied project in partnership with external organisations. This offers students an exciting opportunity to gain experience of applying their knowledge and skills to policy and practice.

Assessment

Your core modules will be assessed using essays. Optional modules may use other forms of assessment that reflect the diversity of the topics you can study, including presentations, book and literature reviews, research proposals and reports among others.

Career opportunities

This programme prepares students for policy, research and applied careers across the private, public and third sectors. The interdisciplinary and dynamic nature of the programme equips students with the critical, analytical and methodological skills to deploy their specialist expertise in a clear, efficient and effective manner. You will develop transferable skills in research, analysis and communication, as well as in-depth knowledge that can be applied across a range of domains and contexts.

Due to the rigorous and applied nature of our teaching, graduates might pursue careers across a diverse range of organisational settings such as in government, NGOS, charities, think tanks, social enterprises and business. The programme also offers excellent development opportunities to pursue a career in social research or undertake research at PhD level.

Irrespective of your future career intentions, we offer tailored guidance and support through ESSL Faculty staff and the Leeds Careers Centre.



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Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions. Read more

Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions.

This programme will take your career to the next level and develop your ability to confidently make high impact businesses decisions that are driven by data.

The programme focuses on two key areas: analysing business data, and solving business challenges analytically. Optional modules allow you to further specialise in areas such as the economic, managerial or finance aspects of the subject.

Furthermore, you will benefit from hands-on experience of a wide range of analytics software such as simulators and mathematical tools.

Programme structure

The programme is studied full-time over one academic year. It consists of eight taught modules and a dissertation.

Example module listing

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.

Career prospects

Business analytics students often pursue careers as consultants, researchers, managers, and analysts.

Software

You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:

  • Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
  • COGNOS and SQL Server for Business Intelligence for analytical processing
  • Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
  • SAP for Enterprise Resource Planning
  • R, SPSS and EViews for coding, statistics and forecasting
  • ILOG’s Optimisation Studio (Cplex) for optimisations
  • Matlab for algorithms and programming and Simulink (SimEvents) for simulations
  • Arena (or Simul8) for Discrete Event Simulations

Who we work with

This programme is run in cooperation with IBM.

Educational aims of the programme

The programme’s aim is to provide a high quality education that is both intellectually rigorous and at the forefront of management science research, relevant for problem solving and decision making by managers.

It will respond to the emergent needs of corporations and academia for professionals who are able to work with analytical tools to generate value from available Information depots and take advantage of the vast amounts of data now provided by the modern ICT and ERP systems, which underlie the operations of modern corporations.

The programme will implant understanding of the theoretical base around knowledge management and knowledge work, practical skills and experience in using analytical software tools.

It will allow future professional managers and consultants to cope with an increasingly complex and global operational environment of the modern corporation.

Completion of the programme will provide a sound foundation for those considering continuing their academic development towards a PhD degree in the management disciplines.

The programme is structured in a way that would provide students with a choice between a more quantitative intensive track of modules or a qualitative analytic (business development track) which would reflect students’ personal strengths and preferences and match future career aspirations.

The compulsory modules provide a sound foundation which builds an analytical skillset using relevant statistical and management theories, and supports the development of practical hands-on experience applying the theoretical aspects using real-world data to address corporate challenges and find solutions to actual problems.

The readings in the module will build a sound basis which would allow students to access and understand the academic literature and undertake empirical investigations in the areas of decision modelling and business development.

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.

Professional recognition

Surrey Business School is accredited by the Association to Advance Collegiate Schools of Business (AACSB) and by the Association of MBAs (AMBA).



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