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Masters Degrees (Data Analysis)

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How can different kinds of data inform us on economic issues? On this course you learn how economic data analysis can address practical problems within business, accounting, and development. Read more
How can different kinds of data inform us on economic issues? On this course you learn how economic data analysis can address practical problems within business, accounting, and development.

Our MSc Applied Economics and Data Analysis is run jointly between our Department of Economics and our Institute for Social and Economic Research (ISER), which specialises in the analysis of household and labour market data.

On our course you will be provided the tools for analysing and implementing some of the models that are present in theory modules. You study data-orientated, applied modules, exploring topics including:
-Techniques used in the analysis of panel data
-The specification of models and the tests of their validity
-Methods for analysing persistence over time in economic variables
-Handling different types of datasets,
-Survey methodology and sampling frames, and how to deal with problems of response rates and attrition

We are top 5 in the UK for research, with over 90% of our research rated as “world-leading” or “internationally excellent”. Much of this world-class research is related to policy, and we have particular strengths in the areas of:
-Game theory and strategic interactions
-Theoretical and applied econometrics
-Labour economics

The quality of our work is reflected in our stream of publications in high-profile academic journals, including American Economic Review, Econometrica, and Review of Economic Studies.

Our University is one of only 21 ESRC-accredited Doctoral Training Centres in the UK. This means that our course can form part of a prestigious 1+3 funding opportunity worth up to £21,575.

Our expert staff

Study and work alongside some of the most prominent economists of our time.

Our researchers are at the forefront of their field and have even received MBEs, with students coming from across the globe to study, research or work with us.

Many of our researchers also provide consultancy services to businesses in London and other major financial centres, helping us to develop research for today's society as well as informing our teaching for the future.

For a full list of research interests, see our Department’s staff pages.

Specialist facilities

Take advantage of our wide range of learning resources to assist you in your studies:
-Extensive software for quantitative analysis is available in all computer labs across the university
-Access a variety of economics databases and multiple copies of textbooks and e-books in the Albert Sloman Library

Your future

After completing your masters, you may wish to extend your knowledge with a research degree – many Essex graduates decide to stay here for further study.

Alternatively, our course also prepares you for employment; recent surveys have shown that higher degree graduates are more likely to obtain jobs at professional or managerial level.

You will develop key employability skills including analytical reasoning, mathematical techniques, model building and data analysis.

Our graduates find employment in roles such as business and financial analysts, management consultants, government officials, and economists for banks and other financial organisations.

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

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

MSc Applied Economics and Data Analysis
-Dissertation
-Applications of Data Analysis
-Mathematical Methods
-Microeconomics
-Panel Data Methods
-Banking (optional)
-Behavioural Economics I: Individual Decision Making (optional)
-Behavioural Economics II: Games and Markets (optional)
-Computational Agent-Based Macro-Economics, Financial Markets and Policy Design (optional)
-Econometric Methods (optional)
-Economic Development Theory (optional)
-Economics of Financial Markets (optional)
-Estimation and Inference in Econometrics (optional)
-Game Theory and Applications (optional)
-International Finance (optional)
-International Trade (optional)
-Macroeconomics (optional)
-Monetary Economics (optional)
-Political Economy (optional)
-Theory of Industrial Organisation (optional)
-Time Series Econometrics (optional)
-Topics in Financial Economics (optional)
-Economics of Incentives, Contracts and the Firm (optional)

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to meet the potential gap for data analysis professionals around the world,. to prepare graduates to work with data in the business environment,. Read more

Course aims

•to meet the potential gap for data analysis professionals around the world,
•to prepare graduates to work with data in the business environment,
•provide a route for students in their transition from undergraduate study to employment in data-led sectors,
•provide the opportunity to gain practical experience in databases (and achieve two professionally accredited certificates) and a rigorous understanding of applied statistics, data mining, operational research and related areas.

This is a year-long programme including two terms of taught modules, succeeded by project work during the summer term.

1st term will include course work on:
• 3 core modules --Scientific Computing, Mathematical Modelling, Programming in C++ & Advanced Algorithms AND
•1 optional module—1 out of Generalized Linear Models, Financial Mathematics, Internet & Cloud Computing).

2nd term is similar in design with:
•3 core modules--Operational Research, Data Mining & Neural Networks, Financial Services Information Systems AND
•1 optional module—1 out of Computational Methods for PDEs, Applied Statistics, Further Statistics, Game Theory, Design & Analysis of Algorithms.

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Data analytics/science is the science of extracting insight from large amounts of raw data in order to enable better understanding of the processes that created it and so help in analysis, theory exploration and decision making. Read more
Data analytics/science is the science of extracting insight from large amounts of raw data in order to enable better understanding of the processes that created it and so help in analysis, theory exploration and decision making. These techniques can be applied in the natural science, social science and business domains.

The Higher Diploma in Data Analytics is a new, purpose designed course which has been carefully designed to address industry needs. The course is a collaboration between the Departments of Mathematics & Statistics, Computer Science and the National Centre for Geocomputation.

The modules are designed to give students the knowledge and skills to collect, process, analyse and visualise data in order to extract useful information, explore statistical patterns, test hypotheses, and explore the implications of models.

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

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

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International Master's in Statistics - MSc. https://www.kent.ac.uk/courses/postgraduate/163/international-masters-statistics. Read more
International Master's in Statistics - MSc: https://www.kent.ac.uk/courses/postgraduate/163/international-masters-statistics

Overview

The International Master’s in Statistics develops your practical, statistical and computing skills to prepare you for a professional career in statistics or as a solid basis for further research in the area.

The programme has been designed to provide a deep understanding of the modern statistical methods required to model and analyse data. You will benefit from a thorough grounding in the ideas underlying these methods and develop your skills in key areas such as practical data analysis and data modelling.

It has been accredited by the Royal Statistical Society (RSS) and equips aspiring professional statisticians with the skills they need for posts in industry, government, research and teaching. It also enables you to develop a range of transferable skills that are attractive to employers within the public and private sectors.

Students whose mathematical and statistical background is insufficient for direct entry on to the appropriate programme, may apply for this course. The first year of the programme gives you a strong background in statistics, including its mathematical aspects, equivalent to the Graduate Diploma in Statistics. This is followed by the MSc in Statistics.

International Master's in Statistics with Finance - MSc: https://www.kent.ac.uk/courses/postgraduate/164/international-masters-statistics-finance

Overview

This programme, accredited by the Royal Statistical Society (RSS), equips aspiring professional statisticians with the skills they will need for posts in industry, government, research and teaching. It is suitable preparation too for careers in other fields requiring a strong statistical background.

Students whose mathematical and statistical background is insufficient for direct entry on to the appropriate programme, may apply for this course. The first year of the programme gives you a strong background in statistics, including its mathematical aspects, equivalent to the Graduate Diploma in Statistics. This is followed by the MSc in Statistics with Finance.

About the School of Mathematics, Statistics and Actuarial Science (SMSAS)

The School has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. We encourage all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes.

The Statistics Group is forward-thinking, with varied research, and received consistently high rankings in the last two Research Assessment Exercises.

Statistics at Kent provides:

- a programme that gives you the opportunity to develop practical, mathematical and computing skills in statistics, while working on challenging and important problems relevant to a broad range of potential employers

- teaching and supervision by staff who are research-active, with established reputations and who are accessible, supportive and genuinely interested in your work

- advanced and accessible computing and other facilities

- a congenial work atmosphere with pleasant surroundings, where you can socialise and discuss issues with a community of other students.

Research areas

Biometry and ecological statistics
Specific interests are in biometry, cluster analysis, stochastic population processes, analysis of discrete data, analysis of quantal assay data, overdispersion, and we enjoy good links within the University, including the School of Biosciences and the Durrell Institute of Conservation and Ecology. A recent major joint research project involves modelling the behaviour of yeast prions and builds upon previous work in this area. We also work in collaboration with many external institutions.

Bayesian statistics
Current work includes non-parametric Bayes, inference robustness, modelling with non-normal distributions, model uncertainty, variable selection and functional data analysis.
Bioinformatics, statistical genetics and medical statistics
Research covers bioinformatics (eg DNA microarray data), involving collaboration with the School of Biosciences. Other interests include population genetics, clinical trials and survival analysis.

Nonparametric statistics
Research focuses on empirical likelihood, high-dimensional data analysis, nonlinear dynamic analysis, semi-parametric modelling, survival analysis, risk insurance, functional data analysis, spatial data analysis, longitudinal data analysis, feature selection and wavelets.

Careers

Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.

Recent graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.

Professional recognition

The taught programmes in Statistics and Statistics with Finance provide exemption from the professional examinations of the Royal Statistical Society and qualification for Graduate Statistician status.

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

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This one year MSc programme in Statistics and Computational Finance aims to train students to work as professional statisticians, not only at the interface between statistics and finance, but to provide skills applicable in sociology, health science, medical science, biology, and other scientific areas where data analysis is needed. Read more
This one year MSc programme in Statistics and Computational Finance aims to train students to work as professional statisticians, not only at the interface between statistics and finance, but to provide skills applicable in sociology, health science, medical science, biology, and other scientific areas where data analysis is needed.

The emphasis of the programme is on data analysis. It equips students with contemporary statistical ideas and methodologies as well as advanced knowledge, which will make students very competitive to industry, academic and governmental institutions. There are excellent career prospects for employment in industry and the public sector for our graduates. An MSc degree in Statistics and Computational Finance provides attractive employment opportunities in financial industries, government, consultancy companies, research centres, and other industries where data analysis is needed. Students with an interest in academic work may also decide to continue on a PhD programme in Statistics or a related field, for which the MSc in Statistics and Computational Finance provides a sound foundation.

Career opportunities

There are excellent career prospects for students with a background in statistics and data analysis. The programme is designed to equip students with contemporary statistical ideas and methodologies which makes our students very competitive when seeking employment in industry and governmental institutions, as well as in academic careers. The skills taught are applicable in sociology, health science, medical science, biology and other related disciplines where data analysis is needed.

Recent destinations of graduates from the MSc in Statistics and Computational Finance have included:
-PhD in the Department of Mathematics at the University of York (Non-parametric modelling in high dimensional data analysis)
-PhD at Florida State University
-Modelling Analyst (automotive data provider)
-Graduate Technical Analyst (HSBC)
-Research and Development in a Property and Casualty Insurance company, specialising in catastrophe insurance
-Mainframe Software Solution Sales in a major IT brand
-Data Analyst in a health data company
-Trainee Chartered Accountant

Programme structure

To achieve an MSc degree students must complete modules to the value of 180 credits, including 100 credits of core taught modules, 20 credits chosen among the optional taught modules, and a 60-credit dissertation.

Students who successfully complete 60 credits of taught modules may be eligible for the award of a Postgraduate Certificate.

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This Postgraduate Certificate course in Data Visualisation and Modelling provides graduates with a comprehensive understanding of the mathematical, statistical and data visualisation techniques needed to investigate problems in a wide range of applications. Read more
This Postgraduate Certificate course in Data Visualisation and Modelling provides graduates with a comprehensive understanding of the mathematical, statistical and data visualisation techniques needed to investigate problems in a wide range of applications.

With recent developments in digital technology, society has entered the era of ‘Big Data’. However, the explosion and wealth of available data gives rise to new challenges and opportunities in all disciplines – from science and engineering to biology and business.

A major focus is on the need to take advantage of an unprecedented volume of data in order to acquire further insights and knowledge.

The flexibility of this course makes it particularly suitable for students in employment.

See the website http://www.brookes.ac.uk/courses/postgraduate/data-visualisation-and-modelling/

Why choose this course?

- A flexible approach to study enables participants to complete the Postgraduate Certificate course in between 1 and 5 years (part-time).

- Use of SPSS.

- A course designed to increase employability in a high-demand field of work.

- Develop your critical skills in the application of visualisation techniques for understanding and presenting the results of analysis.

- Join a supportive and close-knit community of teachers, support staff and learners.

This course in detail

Advanced Statistical Modelling - This module introduces a broad class of linear and non-linear statistical models and the principles of statistical inference to a variety of commonly encountered data analysis problems. The software package SPSS will be used as a tool for statistical analysis with the goal of enabling students to develop their critical thinking and analytical skills. The emphasis, however, is very much on the practical aspect of the methodology and techniques with the theoretical basis kept at a minimum level.

Modelling and Data Analysis using MATLAB - This module gives depth of knowledge in advanced modelling techniques and breadth of analysis by virtue of its general application to any field of engineering and data analysis. In this module students learn to build computer models, present and analyse data using the facilities of MATLAB. Some mathematics is taught as relevant to data interpolation, optimisation and/or choosing solvers for models featuring differential equations.

Data Visualisation and Applications - This module provides a general but broad grounding in the principles of data visualisation and its applications. It covers an introduction to perception and the human visual system, design and evaluation of visualisation techniques, analysing, organising and presenting information visually, using appropriate techniques and visualisation systems.

Teaching and learning

The programme follows a supportive teaching and learning strategy based on active student engagement.

Modules offer a variety of teaching methods, and feature a selection of critical appraisal reports, the use of software applications for data analysis, presentations and case studies.

Learning methods include blended learning, formal lectures and problem solving practicals, but also guided independent learning, use of the virtual learning environment Moodle, independent research, software data analyses, and experiments.

Approach to assessment

Due to the data analysis and the interpretive nature of the course content, the high level industrial participation, and the authentic nature of the assessment, all modules are assed entirely by coursework which includes in-class tests. The assessment regime is selected according to what is appropriate for the material covered.

Attendance pattern

Students will study one twelve-week module per semester, attending campus one day per week for six weeks for each module. A typical module delivery structure is as follows.
- Face to face lectures will take place in weeks 2-5. Each face to face session is three hours, and there will be two face-to-face sessions per day.

- A two-hour class test and individual discussion of mini-projects will take place in week 6.

- An online surgery is available to support guided self-study in weeks 7-11.

- E-learning materials will be available throughout the semester as required on Moodle.

- Weekly exercises for formative feedback will be submitted into a drop box for each module.

- Mini-projects will be due at the end of week 12.

Careers

Currently, global demand for combined statistical, mathematics and computing expertise outstrips supply, with evidence-based predictions suggesting a major shortage in this area for at least the next 10 years.

For graduates in data visualisation and modelling this shortage presents opportunities to enhance career progression in one of the most crucial areas of modern science.

Free language courses for students - the Open Module

Free language courses are available to full-time undergraduate and postgraduate students on many of our courses, and can be taken as a credit on some courses.

Please note that the free language courses are not available if you are:
- studying at a Brookes partner college
- studying on any of our teacher education courses or postgraduate education courses.

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The MRes Clinical Research provides training in quantitative and qualitative research methods and applied data analysis for application in clinical settings. Read more
The MRes Clinical Research provides training in quantitative and qualitative research methods and applied data analysis for application in clinical settings.

Who is it for?

This course has been designed for students who wish to develop their quantitative and qualitative research methods and applied data analysis, from basic to advanced levels to advance their careers and become leaders in their clinical field.

Objectives

Knowledge and critical understanding of clinical research methods are becoming increasingly important skills for all professionals in the health, social care and private sectors, where an evidence based approach, supported by academic rigour is crucial to decision making, clinical practice and delivery of integrated services.

The MRes Clinical Research will provide you with training in quantitative and qualitative research methods and applied data analysis from basic to advanced levels as well as provide opportunities to apply this research knowledge to clinical settings.

We will enable you to produce high quality, publishable research and give you the skills and knowledge to develop your clinical academic research career to become a leader in your clinical field.

You will learn from experts in clinical research who are renowned nationally and internationally.

Placements

Students undertake a work-based research placement with a research centre/ unit/ project team of their choosing. The purpose of the placement is to enable the student to develop and refine awareness, knowledge, understanding, experience, and skills in undertaking research in clinical practice. Students identify their own research site, and negotiate mutually beneficial learning objectives for the time period in placement.

Teaching and learning

Teaching is conducted via a mixture of lectures, class discussions and seminars, student presentations, poster presentations, analysis of case studies, worked examples, interactive computer-based exercises, an online VLE and self-directed reading.

Assessment

Formal assessments will be conducted via: essays, a systematic review, a research proposal, critical reviews, written examinations and a research project on an approved topic.

In addition, there are short practical assignments throughout the course during sessions.

Modules

Postgraduate students are expected to allocate an average of 150 hours of taught and self-directed learning per 15-credit module. Alternatively students can take modules from this Masters degree as standalone CPPD (Continuing Personal and Professional Development) courses. In this case, course costs might vary.

Core modules
-Introduction to research methods and applied data analysis (30 credits)
-Advanced research methods (15 credits)
-Advanced research methods for applied data analysis (15 credits)
-Work-based clinical, research placement (15 credits)
-The application of research in clinical settings (15 credits)
-Clinical research dissertation (90 credits)

Other admission details

Applicants should also be registered with a relevant professional body, have at least one year of experience working as a clinician in the health sector and be currently working in a clinical healthcare environment. Applicants who do not have relevant employment experience but have plans for a future career path in clinical academic research may also be considered. Other Suitable Qualifications: We also consider applications from capable individuals who may not have prior experience of working within the health sector but have clear plans for a future career path in clinical academic research.

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

Who is it for?

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

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

Objectives

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

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

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

Placements

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

Academic facilities

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

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

Scholarships

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

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

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

Teaching and learning

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

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

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

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

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

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

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

Course content

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

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

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

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

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

Individual project - (60 credits)

Career prospects

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

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

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The MA Methods of Social Research provides essential training for employment where an understanding of social research is important, as well as for further academic research in a social science discipline. Read more
The MA Methods of Social Research provides essential training for employment where an understanding of social research is important, as well as for further academic research in a social science discipline.

This MA programme at Kent exposes students to a wide range of thinking and approaches in social science research presented in a multi-disciplinary context and at an advanced level.

The focus of the programme is on developing practical skills in data collection, in data analysis and interpretation, and in the presentation of research findings so that students gain insight into the research process from design to the production of new knowledge.

More generally students will broaden their understanding of the philosophical, theoretical and ethical issues that matter in research, and will become aware of debates about the relationship between theory and research and between research and policy/practice.

Visit the website https://www.kent.ac.uk/courses/postgraduate/119/methods-of-social-research

About the School of Social Policy, Sociology and Social Research (SSPSSR):
SSPSSR has a long and distinguished history, and is one of the largest and most successful social science research communities in Europe. It has received top ratings in Research Assessment Exercises, and most recently had 70% of its work judged as either “world-leading” or “internationally excellent” in terms of its “originality, significance and rigour”.

The School supports a large and thriving postgraduate community and in 2010 distributed in excess of £100,000 in Economic and Social Research Council (ESRC) quota awards, and in University and SSPSSR bursaries and scholarships to new students.

Academic staff specialise in research of international, comparative and theoretical significance, and we have collective strengths in the following areas: civil society, NGOs and the third sector; cross-national and European social policy; health, social care and health studies; work, employment and economic life; risk, ‘risk society’ and risk management; race, ethnicity and religion; social and public policy; sociology and the body; crime, culture and control; sociological theory and the culture of modernity.

Course structure

Master's (MA):
The MA programme consists of:

- four compulsory one-unit modules (total of 80 credits, 40 ECTS)
- two optional one-unit modules or one two-unit module (total of 40 credits, 20 ECTS)
- and a supervised dissertation (60 credits, 30 ECTS).

The compulsory modules are:

- Qualitative Research
- Quantitative Data Analysis
- Critical Social Research: Truth, Ethics and Power
- Design of Social Research

The dissertation will be on a topic of your choosing and involves carrying out original empirical research using the research methods covered in modules.

Postgraduate Certificate/Diploma (PCert/PDip):
There is the option to take this programme as a Postgraduate Certificate, where you just take the four compulsory modules (80 credits). The Certificate is offered to all registered PhD students (part-time or full-time) within the Faculty of Social Sciences (not only to SSPSSR students) free of charge subject to supervisors’ consent.

You can also take it as a Postgraduate Diploma, where you take the four compulsory modules and two optional modules but without taking the dissertation (120 credits).

Full time or part time?:
The Programme can be taken either full-time over one year or part-time over two years. For part-time students, in the first year you take 'Design of Social Research' and 'Qualitative Data Analysis.' In the second year, you take 'Quantitative Research' and 'Using Research – Advanced Critical Skills'. Additional credits will be obtained from optional modules offered within the Faculty.

Assessment

Teaching for the MA will take a variety of forms, including lectures, tutor-led seminars, student-led seminars, small group work, workshops on data analysis, guided search of on-line data sources, and self-directed learning based on the University Library.

Students will be assessed in each module and on a 12,000-15,000 word dissertation on a topic of their choice.

Module assessments vary. Some require either one 5,000-word or two 2,500-word essays; others require more of a portfolio of work, including in-class tests. The individual module outlines contain the necessary information on assessment.

This programme aims to:

•provide appropriate methods training for students preparing an MA dissertation or MPhil/DPhil theses, or for students going on to employment involving the use of social science research

•introduce you to a variety of different approaches to social science research, presented in a multidisciplinary context

•cover the principles of research design and strategy, including formulating research questions or hypotheses and translating these into practicable research designs

•make you aware of the range of secondary data available and equip you to evaluate their utility for research

•develop skills in searching for and retrieving information, using library and internet resources in a multidisciplinary context

•introduce you to the philosophical, theoretical and ethical issues surrounding research and the debates about the relationship between theory and research, the problems of evidence and inference, and the limits of objectivity

•develop skills in the use of SPSS and other statistical techniques of data analysis, including multivariate analysis

•develop skills in writing, the preparation of a research proposal, the presentation of research results, and verbal communication

•help you to prepare your research results for wider dissemination, such as seminar papers, conference presentations, reports and publications to a range of audiences, including academics, policymakers, professionals, service users and the general public

•give you an appreciation of the potential and problems of social research in local, regional, national and international settings.

Careers

Building on Kent’s success as the region’s leading institution for student employability, we place considerable emphasis on you gaining specialist knowledge in your chosen subject alongside core transferable skills.

We ensure that you develop the skills and competences that employers are looking for including: research and analysis; policy development and interpretation; independent thought; writing and presentation, as well as time management and leadership skills.

You also become fully involved in the professional research culture of the School. A postgraduate degree in the area of social and public policy is a particularly flexible and valuable qualification that can lead to many exciting opportunities and professions.

Recent graduates have pursued careers in academia, journalism, local and central government, charities and NGOs.

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

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These programmes offers the opportunity to begin or consolidate your research career under the guidance of internationally renowned researchers and professionals in the School of Mathematics, Statistics and Actuarial Science (SMSAS). Read more
These programmes offers the opportunity to begin or consolidate your research career under the guidance of internationally renowned researchers and professionals in the School of Mathematics, Statistics and Actuarial Science (SMSAS).

Research interests are diverse and include: Bayesian statistics; bioinformatics; biometry; ecological statistics; epidemic modelling; medical statistics; nonparametric statistics and semi-parametric modelling; risk and queueing theory; shape statistics.

Visit the website https://www.kent.ac.uk/courses/postgraduate/169/statistics

About the School of Mathematics, Statistics and Actuarial Science (SMSAS):

The School has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. We encourage all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes.

The Statistics Group is forward-thinking, with varied research, and received consistently high rankings in the last two Research Assessment Exercises.

Statistics at Kent provides:

- a programme that gives you the opportunity to develop practical, mathematical and computing skills in statistics, while working on challenging and important problems relevant to a broad range of potential employers

- teaching and supervision by staff who are research-active, with established reputations and who are accessible, supportive and genuinely interested in your work

- advanced and accessible computing and other facilities

- a congenial work atmosphere with pleasant surroundings, where you can socialise and discuss issues with a community of other students.

Course structure

The research interests of the group are in line with the mainstream of statistics, with emphasis on both theoretical and applied subjects.

There are strong connections with a number of prestigious research universities such as Texas A&M University, the University of Texas, the University of Otago, the University of Sydney and other research institutions at home and abroad.

The group regularly receives research grants. The EPSRC has awarded two major grants, which support the National Centre for Statistical Ecology (NCSE), a joint venture between several institutions. A BBSRC grant supports stochastic modelling in bioscience.

Research areas

- Biometry and ecological statistics

Specific interests are in biometry, cluster analysis, stochastic population processes, analysis of discrete data, analysis of quantal assay data, overdispersion, and we enjoy good links within the University, including the School of Biosciences and the Durrell Institute of Conservation and Ecology. A recent major joint research project involves modelling the behaviour of yeast prions and builds upon previous work in this area. We also work in collaboration with many external institutions.

- Bayesian statistics

Current work includes non-parametric Bayes, inference robustness, modelling with non-normal distributions, model uncertainty, variable selection and functional data analysis.

- Bioinformatics, statistical genetics and medical statistics

Research covers bioinformatics (eg DNA microarray data), involving collaboration with the School of Biosciences. Other interests include population genetics, clinical trials and survival analysis.

- Nonparametric statistics

Research focuses on empirical likelihood, high-dimensional data analysis, nonlinear dynamic analysis, semi-parametric modelling, survival analysis, risk insurance, functional data analysis, spatial data analysis, longitudinal data analysis, feature selection and wavelets.

Careers

Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.

Recent graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.

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

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

Healthcare, with an already established strong relationship with Information & Communication Technologies (ICT), is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.

Processing this data to extract valuable information about a population (epidemiological applications) or the individual (personalised healthcare applications) is the work of health data scientists. Their work has the potential to improve quality of life on a large scale.

Swansea University is the first institution in the UK to offer this taught master's programme in Health Data Science designed to develop the essential skills and knowledge required of the Health Data Scientist.

Key Features of the Health Data Science Programme

- A one year full-time taught master's programme designed to develop the essential skills and knowledge required of the Health Data Scientist.
- The Health Data Science course is also available for three years part-time study.
- An integrated programme of studies tailored to the essential skill set required for Data Scientists operating within healthcare organisations covering key topics in computation, data modeling, visualisation, machine learning and key methodologies in the analysis of linked health data.
- Hands on experiential learning from the professionals behind the Secure Anonymised Information Linkage (SAIL) Databank, a UK-exemplar project for the large scale mining of healthcare data within a secure environment.
- Strong collaboration links with colleagues from the Centre for Health Services Research of the University of Western Australia, a group of leading experts in the analysis of linked health data.
- The Health Data Science course is based within the award winning Centres for Excellence for Administrative Data and eHealth Research of Swansea University, awarded by the Economic and Social Research Council (ESRC) and Medical Research Council (MRC), enhancing the quality of the course.

Who should study MSc Health Data Science?

The Health Data Science course is suitable for those working in healthcare with roles involving the analysis of health data and also computer scientists with experience in working with data from the healthcare domain, as well as biomedical engineers and other similar professions.

Course Structure

Students must complete 6 modules of 20 credits each and produce a 60 credits dissertation on a Health Data Science project. Each module of the programme requires a short period of attendance that is augmented by preparatory and reflective material supplied via the course website before and after attendance.

Attendance Pattern

Health Data Science students are required to attend the University for 1 week (5 consecutive days) for each module in Part One. Attendance during Part Two is negotiated with the supervisor.

Modules

Modules on the Health Data Science programme typically include:

Scientific Computing and Health Care
Health Data Modelling
Introductory Analysis of Linked Health Data
Machine Learning in Healthcare
Health Data Visualisation
Advanced Analysis of Linked Health Data

Professional Development

The College of Medicine offers the modules on the Health Data Science course as standalone opportunities for prospective students to undertake continued professional development (CPD) in the area of Health Data Science.

You can enroll on the individual modules for the Health Data Science programme as either an Associate Student (who will be required to complete the module(s) assessments) or as a Non-Associate Student (who can attend all teaching sessions but will not be required to complete any assessments).

For information and advice on applying for any of the continuing education opportunities, please contact the College directly at .

Employability

Postgraduate study has many benefits, including enhanced employability, career progression, intellectual reward and the opportunity to change direction with a conversion course.

From the moment you arrive in Swansea, specialist staff in Careers and Employability will help you plan and prepare for your future. They will help you identify and develop skills that will enable you to make the most of your postgraduate degree and enhance your career options. The services they offer will ensure that you have the best possible chance of success in the job market.

The student experience at Swansea University offers a wide range of opportunities for personal and professional development through involvement in many aspects of student life.

Co-curricular opportunities to develop employability skills include national and international work experience and study abroad programmes and volunteering, together with students' union and athletic union societies, social and leisure activities.

For the MSc Health Data Science course, we are in the process of identifying opportunities for our students to complete volunteering placements with a number of our collaborative partners.

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On this established and well respected course, you gain the knowledge, skills and attributes needed to be an effective sport and exercise science practitioner. Read more
On this established and well respected course, you gain the knowledge, skills and attributes needed to be an effective sport and exercise science practitioner. You develop strong technical, analytical, practical and professional skills, alongside specialist skills in biomechanics and performance analysis, physiology and nutrition, and strength and conditioning.

The course enables you to:
-Develop your understanding of science.
-Develop your ability to apply theory to practice in sport and exercise.
-Work towards British Association of Sport and Exercise Science (BASES) accreditation, (at the discretion of BASES, graduates are able to apply for exemption from some elements of the BASES supervised experience accreditation scheme).
-Conduct independent research.
-Gain experience as a sport or exercise science consultant.

We offer a first-class suite of research and teaching laboratories alongside excellent facilities offered by our partnership venue at the English Institute of Sport, Sheffield. Our laboratories are all British Association of Sport and Exercise Science (BASES) accredited.
The four overarching study themes are:
-Analysis of performance.
-Improving performance.
-Research methods and data analysis in both research and applied practice.
-Professional practice.

Many of the teaching staff support elite athletes as part of their work in the Centre for Sport and Exercise Science (CSES). The team for sport performance have worked successfully with athletes competing at the Olympics, Paralympics, and Winter Olympics. They have provided, or are currently providing, sport science research and consultancy services at elite level for the
-Amateur Boxing Association
-Amateur Swimming Association (diving and swimming)
-British Cycling
-British Speed Skating Association
-British Skeleton-Bob Team
-English Bowls Association
-English Golf Union
-Royal Yachting Association
-GB table tennis
-GB volleyball

You benefit from CSES' activities as they allow us to keep course content at the cutting edge, based on our knowledge and experience of sport and exercise science delivery. You can also benefit from a work-based learning programme to help develop your experience of working in multidisciplinary teams, supporting athletes and coaches.

During the course you use a mix of traditional and online learning resources to ensure the course is flexible and can fit in with your existing commitments. The quality of our provision was rated 24/24 by the Higher Education Council.

Sheffield Hallam are a Skills Development Partner of the Chartered Institute for Managing Sport and Physical Activity.

For more information, see the website: https://www.shu.ac.uk/study-here/find-a-course/mscpgdippgcert-applied-sport-and-exercise-science

Course structure

Full time – 1 year
Part time – typically 2 years
Starts September

The masters award is achieved by successfully completing 180 credits.

Core modules
-Analysis and evaluation of performance: technical and tactical (15 credits)
-Analysis and evaluation of performance: functional and metabolic (15 credits)
-Inter-professional practice in sport and exercise science (15 credits)
-Work-based learning in sport and exercise science (15 credits)
-Research methods (15 credits)
-Data analysis (15 credits)
-Project (60 credits)

Optional modules
30 credits from:
-Improving performance: strength and conditioning (15 credits)
-Improving performance: physiology and nutrition (15 credits)
-Applied performance analysis (15 credits)
-Applied movement analysis (15 credits)
-Human factors in sports engineering (15 credits)

Assessment
Assessments may include:
-Laboratory reports
-Project/ethics proposal
-Needs analysis
-Qualitative data analysis
-Managing projects
-Problem solving exercises
-Group work
-Oral presentations
-Poster presentations
-Case study defence or report
-Quantitative data analysis examination
-Project file
-Abstract writing
-Article prepared for publication (MSc only)
-Action plan
-Organisational report
-Technology-based communication package

Other admission requirements

We designed this course to continue specialist studies at masters level for students who already possess a relevant first degree. You may also have an appropriate combination of other subject specific qualifications and relevant practical experience.

The course leader interviews applicants with non-standard qualifications.

If English is not your first language you will need an IELTS score of 6.0 with a minimum of 5.5 in all skills, or a recognised equivalent. If your level of English language is currently below IELTS 6.0 we recommend you consider an appropriate Sheffield Hallam University Pre-sessional English course which will enable you to achieve the required level of English.

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Surrey’s highly regarded Department of Sociology specialises in pioneering research methods and offers a stimulating study environment for our highly sought-after graduates. Read more
Surrey’s highly regarded Department of Sociology specialises in pioneering research methods and offers a stimulating study environment for our highly sought-after graduates.

The MSc Social Research Methods programme is backed by decades of experience: we were the first in the UK to run this type of programme in 1974.

PROGRAMME OVERVIEW

Social researchers employ a constantly evolving range of qualitative and quantitative methods to explore attitudes and experiences, and to understand patterns of social behaviour.

This programme won't just train you in the application of specific research techniques: it will illuminate the connections between sociological theory and empirical research, and relate research to the development of public policy and the analysis of substantive social issues.

Wider issues of the social research process are also covered and include: the planning and management of research projects; the methodological, theoretical, philosophical and ethical aspects of research; and the presentation and publication of research findings.

PROGRAMME STRUCTURE

This programme is studied full-time over one academic year and part-time students must study at least two taught technical modules per academic year. 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
-Documentary Analysis and Online Research
-Field Methods
-Principle of Survey Design
-Research: From Design to Dissemination
-Evaluation Research
-Statistical Modelling
-Theory and Method
-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 on to employment involving the use of social science research
-Introduce students to a variety of different approaches to social science research at an advanced level
-Cover the principles of research design and strategy, including formulating research questions or hypotheses and translating these into practicable research designs
-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
-Introduce students to the philosophical, theoretical and ethical issues surrounding research and to debates about the relationship between theory and research, about problems of evidence and inference, and about the limits of objectivity
-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 of research 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, policy makers, professionals, service users and the general public

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:
-Formulate, design, plan, carry out and report on a complete research project
-Use the range of research techniques commonly employed in sociological research, from survey research to field methods
-Collect or generate quantitative and qualitative data through an array of techniques, and select techniques of data generation on appropriate methodological bases
-Analyse: quantitative data using basic and more advanced skills; qualitative data from both ‘real world’ and ‘virtual world’ environments
-Employ a quantitative and qualitative software package to manage and analyse data
-Apply critical reflection skills to the methodological, theoretical, ethical, and philosophical aspects of social research practice
-Plan, manage and execute research as part of a team and as a sole researcher
-Present research findings to differing audiences
-Have an understanding of the contribution social research makes to social policy formulation and the evaluation of planned social interventions

Knowledge and understanding
-Appreciate the epistemological and ontological questions that underpin social research
-Show critical awareness and understanding of the methodological implications of a range of sociological theories and approaches
-Show systematic knowledge of basic principles of research design and strategy
-Understand the use and value of a wide range of different research approaches across the quantitative and qualitative spectra
-Show advanced knowledge of techniques, and appropriate use, of quantitative and qualitative data analysis
-Recognise the significance of social/political contexts and uses of research
-Show engagement with innovations and developments in social research
-Demonstrate a comprehensive understanding of research ethics

Intellectual / cognitive skills
-Systematically formulate researchable problems; analyse and conceptualise issues; critically appreciate alternative approaches to research; report to a range of audiences
-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
-Use methodological, theoretical, ethical, and philosophical knowledge about social research practice to address complex issues creatively
-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 research techniques commonly employed in sociological research
-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
-Present research findings to differing audiences in both written and oral formats, as appropriate

Key / transferable skills
-Communicate complex ideas, principles and theories by oral, written and visual means
-Work to deadlines and within work schedules
-Work independently and self-organise
-Apply computing skills for research instrument design, data analysis, and report writing and presentation
-Formulate and solve problems, both individually and as part of a team
-Demonstrate experience of a work environment

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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The College of Social Sciences welcomes all postgraduates to the recently redesigned MA in Social Research programme which continues to enjoy full RT (research training) recognition by the Economic and Social Research Council (ESRC). Read more
The College of Social Sciences welcomes all postgraduates to the recently redesigned MA in Social Research programme which continues to enjoy full RT (research training) recognition by the Economic and Social Research Council (ESRC). This programme aims to provide students with a sound background in social research design and the most up-to-date training in methods of data collection and analysis. The combination of core modules and short courses on more advanced topics provides maximum flexibility for taught postgraduate and research students throughout their study.

The core elements of the programme are delivered by staff across the entire College, many of whom are engaged in cutting-edge research in their own fields. Students will benefit by undertaking the modules with others from different departments within the School of Government and Society, eg, Political Science and International Studies; the Centre for Russian and East European Studies; the Institute for Applied Social Studies; and within the wider College. Students will also receive training on more discipline-specific research elements, as well as dissertation supervision, provided by individual departments. On completion of this MA, many students continue their PhD studies or pursue a career in research in the public, private or voluntary sector.

Programme content
Term 1:

Introduction to Social Research (20)
Research Design (20)
Thesis-related preparation
Information Skills for Social Sciences
University Programme of Skills Training (as necessary)
Dissertation-related preparation
Term 2:

Social Research Methods I (20)
Social Research Methods II (20)
Thesis-related preparation
Summer Term:

Four Short courses (10)
Dissertation (60)
All students registered on the MA in Social Research will take:

1) Four core modules:

Introduction to Social Science Research (20 credits)
Research Design (20 credits)
Social Research Methods I (20 credits)
Social Research Methods II (20 credits)


2) Four elective modules (10 credits each) from the short course programme below
3) A 14,000 word dissertation (60 credits)

Short courses
All short courses run as 2-day intensive workshops from 10–4pm with breaks. This list is updated regularly as new courses are approved so do check this website from time to time to see what is on offer.

These short courses are open to all research students in the College (and some departments in other Colleges, such as Geography, subject to the discretion of the Programme Team). However, places on each course are limited and priority will be given to MA Social Research students.

These short courses are also open to all staff in the University who may wish to attend without completing the assessments. However, all doctoral researchers and staff who wish to to so will be placed on a waiting list. Confirmation will be sent a week before the course dates.

Short course programmes
From Multiple linear to Logistic regression
Narrative Research
Analyzing Hierarchical and Panel Data
Visual Research Methods
Linguistic Ethnography
Documentary Research in Education, History and the Social Sciences
Researching Disability
Approaches to Research on Discourse
Policy Evaluation
Advanced Qualitative Data Analysis (using NVivo)
Secondary Research Data Analysis in Social Research
Applications of Geographic Information Systems in Social Science
Overseas Research
Q Methodology – A Systematic Approach for Interpretive Research Design
Activity Theory and its research applications
Some courses have pre-requisites, eg, to register on Multiple Linear and Logistic Regression, Factor Analysis and Narrative Research; you will need to have passed Data Analysis (20 credits module) or equivalent. For the latter, you will need to provide evidence that you have passed a similar course on quantitative/qualitative data analysis where appropriate.

Please be aware that some of these courses run on the same dates. Make sure you have not picked courses that clash with each other. For further details or to sign up for these short courses, please email the course names, your name, student ID and your programme to |.

Skills and attributes gained
Students will have acquired a solid foundation of a broad range of research methods that are widely used in the social sciences and will have developed:

A sound understanding of the methodological debates
An overview of the philosophy of social science and how this informs research design, methods chosen of data collection and analysis
An ability to use a range of research techniques appropriate to their subject area
Competence in the representation and presentation of information and data
An ability to communicate research findings effectively to a wider range of audiences
An appreciation of the potential use and impact of their research within and beyond academia
An ability to engage with relevant users at all points in the research process, from devising and shaping research questions through to enhancing practice
Learning and teaching
Students are expected to engage in high-level discussion during all sessions. Teaching will be delivered by a combination of lectures, seminars and computer workshops. Some fieldwork involving primary data collection is required where appropriate.

Careers
Many students go on to do a PhD after completing this MA. Others have followed a career in local authorities, government departments, health authorities, management consultancy, media, the voluntary sector and so on.

Assessment
All core modules are assessed by a 4000-word essay or report. On most short courses, a 3000-report is usually required. The dissertation length is 14,000 words and students are expected to utilise the knowledge and skills they learned from the taught elements in this programme.

Explore postgraduate study at Birmingham at one of our on-campus open days (Friday 13 November 2015 and Friday 4 March 2016). Register to attend at: http://www.birmingham.ac.uk/pgopendays

If you can’t make it to one of our on-campus open days, our virtual open days run regularly throughout the year. For more information, please visit: http://www.pg.bham.ac.uk

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Are you keen to develop your existing engineering skills and knowledge to master’s level?. The MSc Engineering Management course has been specifically designed to allow you to update, extend and deepen your understanding of engineering and management. Read more
Are you keen to develop your existing engineering skills and knowledge to master’s level?

The MSc Engineering Management course has been specifically designed to allow you to update, extend and deepen your understanding of engineering and management.

You will build on your current knowledge of subjects such as solid modelling and prototyping, computer aided design and engineering data analysis, whilst developing management and entrepreneurial skills that will enhance your career opportunities within engineering and the broader business environment.

In the second year, for one semester, you’ll undertake an internship, study in another country or join a research group. This valuable experience will enhance your employability and further develop your theoretical and practical skills.

Internship

This option offers the opportunity to spend three months working full-time in one of the many companies/industries with which we have close links. You may be able to extend this over more than one semester in cases where it is adjacent to a vacation period. We will endeavour to help those who prefer this option to find and secure a suitable position but ultimately we are in the hands of the employers who are free to decide who they take into their organisation.

Research

If you take this option, you will be assigned to our Engineering, Physics and Materials Research Group. There is every possibility that you may contribute to published research and therefore you may be named as part of the research team, which would be a great start to a research career.

Study Abroad

We have exchange agreements with universities all over the world, including partners in Europe, Asia, the Americas and Oceania. If you take the Study Abroad option you will spend a semester at one of these partners, continuing your studies in English but in a new cultural and learning environment. Please note that this option may require you to obtain a visa for study in the other country.

With the increasing complexity of the engineering sector there is a requirement for engineering managers to be specialised not just in engineering, but also in wider business and management. This course has been specifically designed to meet the demands of today’s employers and provide a solid foundation for you to progress to management level.

Learn From The Best

You’ll be taught by tutors who have many years of experience in the various aspects of the engineering industry. Their experience, combined with their on-going active research, will provide an excellent foundation for your learning.

The quality of their research has put Northumbria University among the UK’s top 25% of universities for the percentage of research outputs in engineering that are ranked as world-leading or internationally excellent. (Research Excellence Framework 2014.)

Our reputation for quality is reflected by the range and depth of our collaborations with industry partners. We’ve built up numerous industrial links during the 50+ years that we’ve been offering engineering courses. These links help ensure high quality placements and collaborative projects.

Northumbria has the advantage of being located in the North East of England, which is a centre of manufacturing and technical innovation. As well as Nissan, the region’s #1 company, there is a strong concentration of automotive, engineering, chemicals, construction and manufacturing companies.

Teaching And Assessment

The structure of this course has been designed to focus on engineering issues and processes, and how they apply to those in management positions.

This course incorporates six taught modules: research methods, project, programme and portfolio management; project change, risk and opportunities management; technology entrepreneurship and product development; engineering management data analysis and sustainable development for engineering practitioners.

Throughout the duration of this course you will build core skills in key areas such as management, business, finance and computing, providing you with a strong understanding of the day-to-day processes that underpin the smooth running of a successful organisation.

This course is primarily delivered by lectures and supporting seminars such as guided laboratory workshops or staffed tutorials. Assessments are undertaken in the form of exams, assignments, technical reports, presentations and project work. The Advanced Practice semester will be assessed via a report and presentation about your internship, study abroad or research group activities.

On completion of all taught modules you will undertake a substantial piece of research around a subject of particular interest to you and your own career aspirations.

Module Overview
Year One
KB7030 - Research Methods (Core, 20 Credits)
KB7031 - Project, Programme and Portfolio Management (Core, 20 Credits)
KB7033 - Project change, risk and opportunities management (Core, 20 Credits)
KB7040 - Sustainable Development for Engineering Practitioners (Core, 20 Credits)
KB7044 - Engineering Management Data Analysis (Core, 20 Credits)
KB7046 - Technology Entrepreneurship & Product Development (Core, 20 Credits)

Year Two
KB7052 - Research Project (Core, 60 Credits)
KF7005 - Engineering and Environment Advanced Practice (Core, 60 Credits)

Learning Environment

Throughout the duration of your course you will have access to our dedicated engineering laboratories that are continuously updated to reflect real-time industry practice.

Our facilities include mechanical and energy systems experimentation labs, rapid product development and performance analysis, materials testing and characterisation, 3D digital design and manufacturing process performance.

You will be given the opportunity to get hands-on with testing, materials processing, moulding, thermal analysis and 3D rapid manufacture to help you create the products and systems required for the projects you will work on during your course.

Your learning journey will also be supported by technology such as discussion boards and video tutorials. You will also participate in IT workshops where you will learn how to use the latest industry-standard software.

Videos of lectures will on many occasions be made available through Panopto video software to further support teaching delivery.

You will also have access to all Northumbria University’s state-of-the-art general learning facilities such as dedicated IT suites and learning areas.

Research-Rich Learning

When studying at Northumbria University you will be taught by out team of specialist staff who boast a wealth of multi-dimensional expertise. The programme is designed to be research-led, delivering up-do-date teaching that is often based on current research undertaken by our team.

Our teaching team incorporates a dynamic mix of research-active industrial practitioners, renowned researchers and technologists, whose combined knowledge ensures you leave with an in-depth understanding of key engineering management practice and research.

You will be encouraged to undertake your own research–based learning, where you will evaluate and critique scientific papers and write research-based reports based on the information gathered.

The department of Mechanical and Construction Engineering is a top-35 Engineering research department with 79% of our outputs ranked world-leading or internationally excellent according to the latest UK-wide research assessment exercise (REF2014, UoA15). This places us in the top quartile for world-leading publications among UK universities in general engineering.

Give Your Career An Edge

With the increasing complexity of the engineering industry there is a requirement for managers to be specialised not just in engineering, but also the general business and management aspects of a company.

This course has been specifically designed to allow you to update, extend and deepen your knowledge to further enhance your career opportunities in both industry and entrepreneurship.

The MSc Engineering Management course will equip you with skills, tools, techniques and methods that are applicable to engineering companies and many other businesses in the UK and abroad.

The Advanced Practice semester will help you develop a track record of achievement that will help you stand out from other job applicants.

A two-year master’s course, like this one, will carry particular weight with employers. They’ll understand that you’ll have a deeper understanding of topics as well as more hands-on practical experience.

On completion of this course you will possess a deep understanding of engineering data analysis, research and project management, programme and portfolio management, project risk management and technology entrepreneurship.

Industry practice and subject benchmarking have strongly influenced the design of this course to ensure you will leave equipped with the skills that are required by today’s employers.

Your Future

The broad range of subjects covered on this course will prepare you for an array of careers within the engineering sector or a general business environment.

You may decide to pursue a career within general engineering, or a more specialised engineering sector.

This course emphasises entrepreneurship and enterprise, developing and enhancing the management and strategic skills that will prepare you for running your own business, should this be your aspiration. These core business skills will also prepare you for management jobs within engineering or another sector.

This course also sets a solid foundation for those wishing to pursue further study or a career within research or teaching.

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