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Masters Degrees (Statistical Methods)

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The course trains students from a variety of academic backgrounds to work as statisticians in various sectors including higher education, research institutions, the pharmaceutical industry, central government and national health services. Read more
The course trains students from a variety of academic backgrounds to work as statisticians in various sectors including higher education, research institutions, the pharmaceutical industry, central government and national health services. It provides training in the theory and practice of statistics with special reference to clinical trials, epidemiology and clinical or laboratory research.

The PSI Andrew Hewett Prize is founded in memory of Andrew Hewett, an alumnus of the School and awarded by the PSI (Statisticians in the Pharmaceutical Industry) to the best student on the course.
Duration: one year full-time or part-time over two years. Modes of study explained.

- Full programme specification (pdf) (http://www.lshtm.ac.uk/edu/qualityassurance/ms_progspec.pdf)

Visit the website http://www.lshtm.ac.uk/study/masters/msms.html

For the MSc Medical Statistics it is preferred that students should normally have obtained a mathematically-based first degree which includes some statistics. Graduates from other fields who have quantitative skills and some familiarity with statistical ideas may also apply.

Any student who does not meet the minimum entry requirement above but who has relevant professional experience may still be eligible for admission. Qualifications and experience will be assessed from the application.

Intercalating this course

(http://www.lshtm.ac.uk/study/intercalate)

Undergraduate medical students can take a year out either to pursue related studies or work. The School welcomes applications from medical students wishing to intercalate after their third year of study from any recognised university in the world.

Why intercalate with us?:
Reputation: The School has an outstanding international reputation in public health & tropical medicine and is at the forefront of global health research. It is highly rated in a number of world rankings including:

- World’s leading research-focused graduate school (Times Higher Education World Rankings, 2013)
- Third in the world for social science and public health (US News Best Global Universities Ranking, 2014)
- Second in UK for research impact (Research Exercise Framework 2014)
- Top in Europe for impact (Leiden Ranking, 2015)

Highly recognised qualification: possessing a Master's from the School will give you a focused understanding of health and disease, broaden your career prospects and allow you to be immersed in research in a field of your choice.

Valuable skills: you will undertake an independent research project (summer project) in your chosen topic, equipping you with research skills that will distinguish you in a clinical environment. While your medical qualification will give you a breadth of knowledge; undertaking an intercalated degree will allow you to explore your main area of interest in greater depth.

Alumni network: the School has a strong international and diverse alumni community, with more than 20,000 alumni in over 180 countries.

MSc vs. BSc: undertaking an MSc is an excellent opportunity to develop in-depth specialist knowledge in your chosen topic and enhance your skills in scientific research. Postgraduate qualifications are increasingly sought after by clinicians and possessing a Masters qualification can assist you in your future career progression.

Objectives

By the end of this course students should be able to:

- select appropriate study designs to address questions of medical relevance

- select and apply appropriate statistical techniques for managing common types of medical data

- use various software packages for statistical analysis and data management

- interpret the results of statistical analyses and critically evaluate the use of statistics in the medical literature

- communicate effectively with statisticians and the wider medical community, in writing and orally through presentation of results of statistical analyses

- explore current and anticipated developments in medical statistics

Structure

Term 1:
All students take five compulsory modules:
- Foundations of Medical Statistics
- Introduction to Statistical Computing (Stata/SAS/R)
- Clinical Trials
- Basic Epidemiology
- Robust Statistical Methods

Terms 2 and 3:
Students take a total of five modules, one from each timetable slot (Slot 1, Slot 2 etc.). The list below shows recommended modules. There are other modules which can only be taken after consultation with the course director.

*Recommended modules

- Slot 1:
Generalised Linear Models (compulsory)

- Slot 2:
Statistical Methods in Epidemiology (compulsory)

- Slot 3:
Analysis of Hierarchical & Other Dependent Data*
Epidemiology of Non-Communicable Diseases
Modelling & the Dynamics of Infectious Diseases
Social Epidemiology

- Slot 4:
Survival Analysis and Bayesian Statistics (compulsory)

- Slot 5:
Advanced Statistical Modelling*
Advanced Statistical Methods in Epidemiology*

Further details for the course modules - http://www.lshtm.ac.uk/study/currentstudents/studentinformation/msc_module_handbook/section2_coursedescriptions/tmst.html

Project Report

During the summer months (July - August), students complete a research project, for submission by early September. This usually consists of analysing a set of data and writing a report, but methodological research can also be undertaken.

Find out how to apply here - http://www.lshtm.ac.uk/study/masters/msms.html#sixth

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This programme aims to provide animal health specialists, scientists and public health specialists with an understanding of the conceptual basis of veterinary epidemiology and public health. Read more
This programme aims to provide animal health specialists, scientists and public health specialists with an understanding of the conceptual basis of veterinary epidemiology and public health. Optional modules make the studies suitable for people from a range of professional backgrounds.

Our Veterinary Epidemiology course graduates find that the international recognition and prestige of their degree opens doors and creates opportunities in their careers.

Modules are designed for self-study using materials provided and with full support from RVC academic experts. Everything you require will be mailed to you, including textbooks and reading material. A Virtual Learning Environment and an on-line library are also available.

Under the microscope

The programme aims to provide you with an understanding of the role of veterinary epidemiology and economics in the design and delivery of effective livestock services, and knowledge of risk analysis approaches in food safety and how human health can be protected through control of zoonotic diseases. The programme also aims to equip you with skills in basic and advanced statistical methods in order to undertake epidemiological investigations and disease modelling.

The course

The course modules provide an essential introduction to a variety of approaches, methods and subjects. These modules are designed to equip you with the preliminary practical and intellectual skills necessary for progression to the next level.

Within the Postgraduate Diploma and the MSc, there is a natural progression from the core modules to the optional modules. Within the selection of optional modules, there is an element of choice in subject matter and disciplinary areas of study. Although the optional modules may not in themselves be more difficult, students will develop a greater understanding and a sophistication of thinking as they work through the modules.

You are required to study three compulsory core modules:
- Epidemiology and animal health economics
- Statistical methods in veterinary epidemiology
- Veterinary public health

Plus four further optional modules selected from:
- Advanced statistical methods in veterinary epidemiology
- Developing and monitoring of livestock production systems
- Economics for livestock development and policy
- Management of infectious disease outbreaks in animal populations
- Research, design, management and grant application writing
- Surveillance and investigation of animal health
- Research project in veterinary epidemiology and public health (MSc only)

How will I learn?

This course is a distance learning course, meaning that you can further your studies without attending the RVC in person.

At the start of the course, you will receive a Study Pack consisting of a study guide, reader and textbooks/CDs. Our Virtual Learning Environment (VLE) allows studies to be portable, by offering on-line access to the programme handbook, study guide, student discussion board, structured academic tutorials, as well as past exam papers/examiners reports (for the previous 2 years).

A tutorial calendar will be released at the beginning of the academic year and students are advised to plan for these sessions, as academic queries will be answered during tutorials only. Although non-compulsory, indicative study calendars are available and provide an indication of the time to spend on each section.

Examinations take place annually in October, however, the flexibility of the programme allows exams to be deferred, if necessary.

A five-year period is offered to complete the MSc degree, Postgraduate Diploma or Postgraduate Certificate, with an average completion time of three years. A two year period is offered for completion of Individual Modules.

Learning outcomes

Upon successful completion of this course, graduates will be able to -

- Improve the health and production of livestock
- Understand the interaction of livestock with people and the environment
- Gain an overview of the factors that influence livestock production (including components on nutrition, reproduction, disease, welfare and the environment)
- Implement control strategies by integrating this knowledge with the principles of epidemiology, economics and disease control within the context of management and infrastructure
- Address the interaction between livestock and the public, in terms of zoonotic disease and clean food production
- Communicate effectively on the health of animal and human populations to a range of audiences, including the general public, farmers, politicians and other key policy makers
- Comprehensive appreciation of welfare and ethical issues connected with farm animal practice
- Formulate a hypothesis and undertake a research project, analyze and present data and how to develop a grant application

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The MA in Anthropological Research Methods (MaRes) may be taken either as a free standing MA or as the first part of a PhD [e.g. as a 1 + 3 research training program]. Read more
The MA in Anthropological Research Methods (MaRes) may be taken either as a free standing MA or as the first part of a PhD [e.g. as a 1 + 3 research training program]. In either case, the student completes a program of research training that includes the Ethnographic Research Methods, Statistical Analysis and the Research Training Seminar as well as a language option. All MaRes students are assigned a supervisor at the start of the year, who will help the student choose other relevant course options. Candidates must also submit a number of research related assignments which, taken together with the dissertation, are equivalent to approximately 30,000 words of assessed work. All students write an MA dissertation, but for students progressing on to a PhD, the MA dissertation will take the form of a research report that will constitute the first part of the upgrade document for the PhD programme.

The MaRes is recognised by the ESRC.

Visit the website http://www.soas.ac.uk/anthropology/programmes/maanthresmethods/

Aims and Outcomes

The MA is designed to train students in research skills to the level prescribed by the ESRC’s research training guidelines. It is intended for students with a good first degree (minimum of a 2.1) in social anthropology and/or a taught Masters degree in social anthropology. Most students would be expected to progress to PhD registration at the end of the degree. By the end of the program students will:

- Have achieved practical competence in a range of qualitative and quantitative research methods and tools;
- Have the ability to understand key issues of method and theory, and to understand the epistemological issues involved in using different methods.

In addition to key issues of research design, students will be introduced to a range of specific research methods and tools including:

- Interviewing, collection and analysis of oral sources, analysis and use of documents, participatory research methods, issues of triangulation research validity and reliability, writing and analysing field notes, and ethnographic writing.

- Social statistics techniques relevant for fieldwork and ethnographic data analysis (including chi-square tests, the T-test, F-test, and the rank correlation test).

Discipline specific training in anthropology includes:

- Ethnographic methods and participant observation;
- Ethical and legal issues in anthropological research;
- The logistics of long-term fieldwork;
- Familiarisation with appropriate regional and theoretical literatures;
- Writing-up (in the field and producing ethnography) and communicating research results; and
- Language training.

The Training Programme

In addition to optional courses that may be taken (see below), the student must successfully complete the following core course:

- Research Methods in Anthropology (15 PAN C011).

This full unit course is composed of Ethnographic Research Methods (15 PAN H002, a 0.5 unit course) and Introduction to Quantitative Methods in Social Research (15PPOH035, a 0.5 unit course hosted by Department of Politics and International Studies).

MA Anthropological Research Methods students and first year MPhil/PhD are also required to attend the Research Training Seminar which provides training in the use of bibliographic/online resources, ethical and legal issues, communication and team-working skills, career development, etc. The focus of the Research Training Seminar is the development and presentation of the thesis topic which takes the form of a PhD-level research proposal.

Dissertation

MA/MPhil Students meet regularly with their supervisor to produce a systematic review of the secondary and regional literature that forms an integral part of their dissertation/research proposal. The dissertation, Dissertation in Anthropology and Sociology (15 PAN C998), is approximately 15,000 words and demonstrates the extent to which students have achieved the key learning outcomes during the first year of research training. The dissertation takes the form of an extended research proposal that includes:

- A review of the relevant theoretical and ethnographic literature;
- An outline of the specific questions to be addressed, methods to be employed, and the expected contribution of the study to anthropology;
- A discussion of the practical, political and ethical issues likely to affect the research; and
- A presentation of the schedule for the proposed research together with an estimated budget.

The MA dissertation is submitted no later than mid-September of the student’s final year of registration. Two soft-bound copies of the dissertation, typed or word-processed, should be submitted to the Faculty of Arts and Humanities Office by 16:00 and on Moodle by 23:59 on the appropriate day.

Exemption from Training

Only those students who have clearly demonstrated their knowledge of research methods by completing a comparable program of study in qualitative and quantitative methods will be considered for a possible exemption from the taught courses. All students, regardless of prior training, are required to participate in the Research Training Seminar.

Programme Specification 2013/2014 (msword; 128kb) - http://www.soas.ac.uk/anthropology/programmes/maanthresmethods/file39765.docx

Teaching & Learning

This MA is designed to be a shortcut into the PhD in that two of its components (the Research Methods Course and the Research Training Seminar, which supports the writing of the dissertation) are part of the taught elements of the MPhil year. Students on this course are also assigned a supervisor with whom they meet fortnightly as do the MPhil students. The other two elements of the course are unique to each student: and might include doing one of the core courses from the other Masters degrees (Social Anthropology, Anthropology of Development, Medical Anthropology, Anthropology of Media, Migration and Diaspora, or Anthropology of Food), as well as any options that will build analytical skills and regional knowledge, including language training. The MaRes can also be used to build regional expertise or to fill gaps in particular areas such as migration or development theory.

The dissertation for the MaRes will normally be assessed by two readers in October of the following year (that is, after the September 15th due date). Students who proceed onto the MPhil course from the MA will then have the first term of the MPhil year to write a supplementary document that reviews the dissertation and provides a full and detailed Fieldwork Proposal. This, along with research report material from the original MA dissertation, is examined in a viva voce as early as November of the first term of the MPhil year by the same examiners who have read the dissertation. Successful students can then be upgraded to the PhD in term 1 and leave for fieldwork in term 2 of the first year of the MPhil/PhD programme. This programme is currently recognised by the ESRC and therefore interested students who are eligible for ESRC funding can apply under the 1+3 rubric. (ESRC)

Destinations

Students of the Masters in Anthropological Research Methods develop a wide range of transferable skills such as research, analysis, oral and written communication skills.

The communication skills of anthropologists transfer well to areas such as information and technology, the media and tourism. Other recent SOAS career choices have included commerce and banking, government service, the police and prison service, social services and health service administration. Opportunities for graduates with trained awareness of the socio-cultural norms of minority communities also arise in education, local government, libraries and museums.

For more information about Graduate Destinations from this department, please visit the Careers Service website (http://www.soas.ac.uk/careers/graduate-destinations/).

Find out how to apply here - http://www.soas.ac.uk/admissions/pg/howtoapply/

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This course provides an understanding of social research, with a particular focus on social statistics and quantitative methods. Read more

Introduction

This course provides an understanding of social research, with a particular focus on social statistics and quantitative methods. The course includes orientation material on social research methods as a whole, and detailed training on a wide range of statistical methods, with advanced modules on data management and emerging priority research areas, such as big data and social network analysis.
Teaching covers the theories behind the methods, and the practical work in using datasets and analysing them with statistical software. Students will gain a variety of highly marketable skills in the areas of social research and social statistics.

Key information

- Degree type: MSc, Postgraduate Diploma
- Study methods: Full-time, Part-time
- Start date: Full-time: SeptemberPart-time: September/JanuarySee semester dates
- Course Director: Dave Griffiths
- Location: Stirling Campus

Course objectives

This course provides an understanding of social research, with a particular focus on social statistics and quantitative methods. The course includes orientation material on social research methods as a whole, and detailed training on a wide range of statistical methods, with advanced modules on data management and emerging priority research areas, such as big data and social network analysis.
Teaching covers the theories behind the methods, and the practical work in using datasets and analysing them with statistical software. Students will gain a variety of highly marketable skills in the areas of social research and social statistics.

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: 6.5 with 6.0 minimum in each skill
- Cambridge Certificate of Proficiency in English (CPE): Grade C
- Cambridge Certificate of Advanced English (CAE): Grade B
- Pearson Test of English (Academic): 60 with 56 in each component
- IBT TOEFL: 90 with no subtest less than 20

For more information go to http://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.

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

Students will undertake four core modules, two option modules and complete a 15,000 word dissertation. In the full time programme, 3 modules are completed during the Autumn semester, 3 in the Spring, and the dissertation submitted in the summer. Module either cover wider topics in social research, or focus on understanding and implementing advanced quantitative methods.

Core modules

- Research Design and Process
- Quantitative Data Analysis
- Advanced Data Analysis
- Advanced Data Management
- Using Big Data in Social Research

Option modules
Students will also select two option modules from a range of applied social research topics. The recommended option is Social Network Analysis.

Other options include The Nature of Social Enquiry, Policy Analysis and Evaluation Research, Qualitative Analysis and Research Methods in Criminology and Socio-Legal Studies. Some of these modules will be particularly suitable for students with an interest in mixed methods research.

Delivery and assessment

Modules are generally a combination of lectures and workshops. Teaching largely takes place on Tuesdays, although some components might take place on other days. The contact hours are sympathetic to those working alongside their studies. Most teaching is performed in smaller classes, with group activities. Modules are usually assessed by an examination, software based assignments, and essays.

Why Stirling?

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.

Career opportunities

Social statistics are an important area within applied social research, offering employment opportunities within the private, public and voluntary sectors, as well as further study. Students will develop thorough knowledge of software and learn a range of sought-after technical skills, including accessing, preparing, analysing and summarising complex quantitative datasets. The course is also designed to provide the technical skill set required for further PhD study.

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Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. Read more
Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. New and exciting opportunities in industry, medicine, government, commerce or research await the graduate who has gained the quantitative skills training provided by this MSc.

Degree information

The programme uses a broad-based approach to statistics, providing up-to-date training in the major applications and an excellent balance between theory and application. It covers modern ideas in statistics including applied Bayesian methods, generalised linear modelling and object-oriented statistical computing, together with a grounding in traditional statistical theory and methods.

Students undertake modules to the value of 180 credits.

The programme consists of a foundation module, four core modules (60 credits) four optional modules (60 credits) and a research dissertation (60 credits).

Core modules
-Foundation Course (not credit bearing)
-Statistical Models and Data Analysis
-Statistical Design of Investigations
-Statistical Computing
-Applied Bayesian Methods

Optional modules
-Decision and Risk
-Stochastic Systems
-Forecasting
-Statistical Inference
-Medical Statistics I
-Medical Statistics II
-Stochastic Methods in Finance I
-Stochastic Methods in Finance II
-Factorial Experimentation
-Selected Topics in Statistics
-Bayesian Methods in Health Economics
-Quantitative Modelling of Operational Risk and Insurance Analytics

Dissertation/report
All MSc students undertake an independent research project, culminating in a dissertation of approximately 10,000–12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. External organisations deliver technical lectures and seminars where possible. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics e.g. the presentation of statistical graphs and tables.

Careers

Graduates typically enter professional employment across a broad range of industry sectors or pursue further academic study.

Top career destinations for this degree:
-Management Associate, HSBC
-Statistical Analyst, Nielsen
-PhD Statistics, University College London (UCL)
-Mortgage Specialist, Citibank
-Research Assistant Statistician, Cambridge Institute of Public Health

Employability
The Statistics MSc provides skills that are currently highly sought after. Graduates receive advanced training in methods and computational tools for data analysis that companies and research organisations value. For instance, the new directives and laws for risk assessments in the banking and insurance industries, as well as the healthcare sector, require statistical experts trained at graduate level. The large amount of data processing in various industries (known as "data deluge") also necessitates cutting-edge knowledge in statistics. As a result, our recent graduates have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

One of the strengths of UCL Statistical Science is the breadth of expertise on offer; the research interests of staff span the full range from foundations to applications, and make important original contributions to the development of statistical science.

London provides an excellent environment in which to study statistical science, being the home of the Royal Statistical Society as well as a base for a large community of statisticians, both academic and non-academic.

The Statistics MSc has been accredited by the Royal Statistical Society. Graduates will automatically be granted the society's Graduate Statistician status on application.

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Empower yourself as a producer and user of empirical research by developing a methodological toolkit of knowledge and skills. This programme is for students who wish to develop their knowledge and skills as a professional researcher in academia, private or public sectors. Read more
Empower yourself as a producer and user of empirical research by developing a methodological toolkit of knowledge and skills.

Who is it for?

This programme is for students who wish to develop their knowledge and skills as a professional researcher in academia, private or public sectors. It is suitable for those seeking to undertake foundational training for doctoral level research, as well as those planning to work in an environment where they might need to commission, undertake, or otherwise critically engage with empirical social research.

Students will typically have a first degree in an arts or social sciences subject. Some students come to us with prior experience of conducting empirical research, or using it, while some are new to the field – the programme thrives on the diversity of experiences and interests of its students.

Objectives

The aim of the course is to boost your understanding, appreciation and practice of qualitative and quantitative research methods. It is taught by academics in the School of Arts and Social Sciences, so whatever your academic or professional background, you will achieve a broad perspective on the production and consumption of empirical research across a range of disciplines. At the same time, you will be able to pursue your own subject specialism through elective module choices and by conducting your own original research for your dissertation.

In the course we aim to equip you with an overview of key issues in research design and philosophical foundations of social research. We offer several modules in applied quantitative and qualitative data collection and analysis. These equip you with a set of practical skills to enable you to conduct and critically read research using these methods, and provide a firm foundation from which you can pursue further specialist training.

Academic facilities

You will have the opportunity to learn a range of statistical software applications to aid data collection and analysis, such as SPSS, Stata, MatLab and R.

Teaching and learning

Teaching is delivered predominantly by lecturers and other academic staff across the School. You will experience a combination of lectures, seminars, workshops and computer lab sessions. You will be expected to read in preparation for classes, and to participate in discussions, group work, presentations and other practical activities. You will be expected to take responsibility for your own learning and to engage in independent study. You will be guided by reading lists for each module, and teaching materials will be made available via the virtual learning environment Moodle. The dissertation is a major part of your MSc work, for which you will receive individual supervision.

Assessment is by means of coursework (written assignments, essays or reports), class tests, presentations, unseen written examinations, and the dissertation. The particular assessment details vary according to the module being studied. Your overall degree result is based on your performance in the taught modules and the dissertation.

Modules

The course consists of taught modules from interdisciplinary core subjects, plus department-specific elective modules, and a research dissertation.

In full-time study you will typically take four 15-credit modules in Term 1 and four in Term 2. The balance of teaching between the terms may vary according to your module choice. Most modules are worth 15 credits each, with a few elective modules worth 30 credits. Your dissertation is worth 60 credits.

As a general guide, a 15-credit module delivered over ten weeks of teaching will typically comprise an hour-long lecture and an hour-long seminar or workshop each week. We would notionally expect you to spend 150 hours in independent study for each 15-credit module (this time includes time spent reading, working through exercises, preparing for examinations, writing coursework, using online resources, navigating Library resources, and so on.)

Core modules - you will take six core modules alongside your dissertation. Your taught core modules will be as below:
-Research design, methods and methodology (15 credits)
-Rationale and philosophical foundations of social research (15 credits)
-Qualitative research methods (15 credits)
-Applied qualitative data analysis (15 credits)
-Introduction to quantitative inference* (15 credits)

You will choose one of the following (two if you do not study quantitative interference) core quantitative analysis modules with the guidance of the Programme Director:
-Statistical models ** (15 credits)
-Applied econometric and psychological research methods (15 credits)
-Multivariate data analysis (15 credits)
-Statistical modelling ** (15 credits)
-Research methods dissertation (60 credits)

*May not be compulsory if you have prior training in quantitative methods.
**You may study Statistical models or Statistical modelling, but not both.

Elective modules - in addition, you take one or two elective modules (to the value of 30 credits) from the following list. All modules are worth 15 credits, unless otherwise stated. Some modules have a stronger methodological element, while some are more substantively focused.

Culture and Creative Industries (Sociology Department)
-Evaluation, politics and advocacy (15 credits)
-Culture (15 credits)
-Cultural policy (15 credits)

Department of Journalism
-Storytelling (30 credits)
-Literary criticism non-fiction (30 credits)

Department of Economics
-Macroeconomics (15 credits)
-Financial derivatives (15 credits)
-Corporate finance (15 credits)
-Asset pricing (15 credits)
-Econometrics (15 credits)

Department of International Politics
-Political Islam in global politics (15 credits)
-International financial institutions (15 credits)
-Understanding security in the 21st century (15 credits)
-International organisations in global politics (15 credits)
-Development and world politics (15 credits)
-Political economy of global finance (15 credits)
-The politics of forced migration (15 credits)
-Global governance (15 credits)
-International politics of the Middle East (15 credits)
-Global financial governance (15 credits)
-US foreign policy (15 credits)
-Economic diplomacy (15 credits)
-Foreign policy analysis (15 credits)

Department of Psychology
-Fundamental processes in cognitive neuroscience and neuropsychology (15 credits)
-Mental health, wellbeing & neuroscience (15 credits)
-Research methods & programming (15 credits)

Department of Sociology
-Survey research methods (15 credits)
-Transnational media and communication (15 credits)
-Developments in communications policy (15 credits)
-Political communication (15 credits)
-Democratisation and networked communication (15 credits)
-Communication, culture and development (30 credits)
-Celebrity (15 credits)
-Analysing crime (15 credits)
-Researching criminal justice (15 credits)
-Criminal minds (15 credits)
-Victims: policy and politics (15 credits)
-Crime news and media justice (15 credits)

*Please note, elective modules are run subject to minimum enrolment numbers/availability and may vary slightly from year to year.

Career prospects

Graduates from the MSc in Research Methods should find themselves well equipped for careers which require critical engagement with empirical research, whether in commissioning, designing, conducting, or making use of its results. Likely destinations include local and central government, public and private sector research organisations, companies involved in marketing, charities and non-governmental organisations. Recent graduate destinations of students studying research methods include the UK Government’s Cabinet Office; Ministry of Defence; Department of Energy and Climate Change; National Health Service; London Borough of Hammersmith and Fulham; Eurofound (EU agency); Rhetoric Solutions (market knowledge provider); Ipsos MORI (leading UK market research company); NatCen (leading social research organisation in the UK); and a range of charities and institutions.

The course is also an ideal foundation for students who wish to pursue doctoral research in social sciences.

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-Do you want a course that provides a thorough grounding in advanced quantitative methods taught within an applied social science framework?. Read more
-Do you want a course that provides a thorough grounding in advanced quantitative methods taught within an applied social science framework?
-Would you like to learn methods of data analysis, including advanced statistics for complex data?
-Would you like a skills-based course with practical training that is highly regarded for future employment within government and academia?

The course is designed to be accessible to non-statisticians, yet is more focussed than many other existing master's courses in social research methods. You'll need a base level of knowledge in undergraduate research methods which you will build on throughout the course to gain comprehensive statistical and analytical skills.

The course has a strong connection with the Cathie Marsh Institute for Social Research (CMIST), reflecting our commitment to interdisciplinary, integrated research. Research activities within the Social Statistics discipline area are both methodological and substantive. They focus on a wide range of subject areas including social inequalities, population dynamics and survey methodology. The SRMS MSc course is recognised by both the Economic and Social Research Council (ESRC) and the North West Doctoral Training Centre, from whom we receive a large number of Advanced Quantitative methods (AQM) and CASE awards each year.

The MSc course aims to develop future social scientists who will have a thorough grounding in research, and are equipped with the tools for collecting and analysing statistical data.

Those completing the MSc course are well suited to roles within central and local government, academia and commercial research and our rate of employability is especially high.

Course unit details

The SRMS course provides a thorough grounding in advanced quantitative methods, taught within an applied social science framework. Whilst the training focuses on advanced quantitative methods, the course is designed to be accessible to students coming from a broad range of disciplinary backgrounds and with varying levels of prior statistical knowledge.

The course is available full-time over one year or part-time over two-years, and may be studied as either an MSc or a Postgraduate Diploma.

All students (MSc and Postgraduate Diploma) take course units totalling 120 credits (eight 15-credit courses) over the year.

Course units typically include:
-Methodology and Research Design
-Introduction to Statistical Modelling
-Statistical Foundations
-Qualitative Research Methods
-Survey Research
-Multilevel Modelling
-Longitudinal Data Analysis
-Advanced Survey Methods
-Social Network Analysis
-Introduction to Demography
-Structural Equation Modelling

All students proceeding to MSc must complete a research dissertation of up to 15,000 words. Those on the Postgraduate Diploma may upgrade to the full MSc subject to satisfactory course performance.

Career opportunities

There is an increasing need for well-trained social scientists who are able to apply advanced methods of analysis to complex data. Graduates of our programme in Social Research Methods and Statistics are in a good position to obtain jobs in central government, including the Office for National Statistics (ONS), the academic sector, local government and within the commercial research sector. We have excellent links with ONS and government departments such as the Department for Children, Schools and Families, local authorities and many commercial organisations and thus well placed to assist students in finding jobs. A number of our students already hold research positions (typically in local government or overseas) and take the MSc as part of career development programmes. The SRMS course is ideal preparation for students wishing to pursue doctoral study, and is a formal component of our 1+3 PhD training model. CMIST usually have a number of funded PhD studentships each year and many studentships are taken up by graduates of the SRMS programme.

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Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more
Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

Degree information

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules from computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

Students undertake modules to the value of 180 credits.

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

Core modules
-Introduction to Statistical Data Science
-Introduction to Supervised Learning
-Statistical Design of Investigations
-Statistical Computing

Optional modules - st least two from a choice of Statistical Science modules including:
-Applied Bayesian Methods
-Decision & Risk
-Factorial Experimentation
-Forecasting
-Quantitative Modelling of Operational Risk and Insurance Analytics
-Selected Topics in Statistics
-Stochastic Methods in Finance I
-Stochastic Methods in Finance II
-Stochastic Systems

Up to two from a choice of Computer Science modules including:
-Affective Computing and Human-Robot Interaction
-Graphical Models
-Statistical Natural Language Processing
-Information Retrieval & Data Mining

Dissertation/report
All students undertake an independent research project, culminating in a dissertation usually comprising 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

Careers

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study.

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:
-Towers Watson, Actuary Analyst
-Proctor & Gamble, Statistician
-Ernst & Young, Audit Associate
-Collinson Group, Insurance Analyst
-UCL, PhD Statistical Science

Employability
Data science professionals will be highly sought after as the integration of statistical and computational analytical tools becomes increasingly essential in all kinds of organisations and enterprises. A solid understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should go along with statistical expertise as graduate level. Data scientists should have a broad background so that they will be able to adapt themselves to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data research.

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This programme introduces you to the methods of statistical analysis, together with the underlying theory and some of the associated mathematics. Read more
This programme introduces you to the methods of statistical analysis, together with the underlying theory and some of the associated mathematics. The graduate diploma gives you the chance to study one or more specific areas of statistics in greater depth.

You will gain an understanding of statistical methods and will be able to apply them to the analysis of real-world data sets. You will also learn how to use statistical computer packages.

Visit the website http://www.bbk.ac.uk/study/2016/postgraduate/programmes/GDGSTATI_C/

Our research

Birkbeck is one of the world’s leading research-intensive institutions. Our cutting-edge scholarship informs public policy, achieves scientific advances, supports the economy, promotes culture and the arts, and makes a positive difference to society.

Birkbeck’s research excellence was confirmed in the 2014 Research Excellence Framework (http://www.bbk.ac.uk/news/ref-results/), which placed Birkbeck 30th in the UK for research, with 73% of our research rated world-leading or internationally excellent.

Read about Birkbeck research offering insights and expertise to inform business, contribute to economic success and develop ground-breaking technologies (http://www.bbk.ac.uk/business/our-research).

Why study this course at Birkbeck?

- Provides an introduction to the main methods of statistical analysis used in business and scientific research.

- Ideal as a way to top up existing knowledge, as preparation for further graduate study or as a stand-alone course.

- Watch videos of our postgraduate students discussing their experience of studying at Birkbeck (http://www.bbk.ac.uk/mybirkbeck/get-ahead-stay-ahead/student-experience-videos).

Course structure

You take 2 compulsory year-long modules, which form the Graduate Certificate in Statistics, designed to give you a thorough grounding in mathematical and statistical methods as a basis for the postgraduate study of statistics.

Then you take 2 further modules, including at least 1 module from: Statistical Modelling; or Probability Models and Time Series.

Compulsory modules:
Advanced Mathematical Methods
Statistics: Theory and Practice

Option modules:
Probability Models and Time Series
Statistical Modelling

Teaching and assessment

Teaching
Mainly by lectures, but you will take part in practical sessions using a statistical package as part of the Statistics: Theory and Practice module.

Assessment
Coursework makes up 20% of the assessment of all modules. The rest of the assessment is by examinations taken in the summer term.

Careers and employability

Graduates can pursue careers in data collection, research, and analysis, modelling and forecasting. Possible professions include statistician, operational researcher, or research scientist (maths). This degree may also be useful in becoming a forensic statistician or high education lecturer.

Find out more about these professions (http://www.prospects.ac.uk/options_with_your_subject.htm).

Find out more about the destinations of graduates in this subject (http://www.bbk.ac.uk/prospective/careers-and-employability/department-of-economics-mathematics-and-statistics).

We offer a comprehensive Careers and Employability Service to help you advance your career, while our in-house, professional recruitment consultancy, Birkbeck Talent, works with London’s top employers to help you gain work experience that fits in with your evening studies.

Find out how to apply here - http://www.bbk.ac.uk/prospective/postgraduate/apply

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Specialised statistical methods are hugely important in dealing with particular problems of economic data. Read more
Specialised statistical methods are hugely important in dealing with particular problems of economic data. For instance, time series econometrics provides methods for analysing the dynamic processes that are often found in macroeconomics, while other techniques are required for analysing the stock market and other financial data.

Econometrics can be described as the application of statistics in an economic context so this course will interest you if your first degree included some training in both statistics and economics.

You study topics including:
-Methods of linear regression and hypothesis testing
-Bayesian statistical modelling and methods
-Actuarial modelling and time series models
-Applied statistics
-Game theory

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

You are also taught within our Department of Economics, who are rated consistently highly for student satisfaction and are Top 5 in the UK for research, with over 90% of their research rated as ‘world-leading’ or ‘internationally excellent’ (REF 2014).

This course can also be studied to a PGDip level - for more information, please view this web-page: http://www.essex.ac.uk/courses/details.aspx?mastercourse=PG00807&subgroup=2

Our expert staff

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

Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology, and Journal of Statistical Applications in Genetics and Molecular Biology.

The academic staff in our Department of Economics are also exceptionally well-regarded; our researchers are at the forefront of their field and have even received MBEs.

Many of our researchers in economics 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.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-Extensive software for quantitative analysis is available in all computer labs across the university
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society where you can explore your interest in your subject with other students
-Alternatively, our Economics Society is an active and social group

Your future

Our graduates are sought after by employers in banking, investment and forecasting, local government and other fields.

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

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

Example structure

-Modelling Experimental Data (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)
-Applied Statistics (optional)
-Bayesian Computational Statistics (optional)
-Research Methods
-Dissertation
-Mathematics of Portfolios (optional)
-Financial Derivatives (optional)
-Partial Differential Equations (optional)
-Econometric Methods (optional)
-Economics of Financial Markets (optional)
-Game Theory and Applications (optional)
-Time Series Econometrics (optional)
-Panel Data Methods (optional)

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

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

Choose from two track options:

Track 1: Operations Management

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

Required Courses:

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

Track 2: Decision Analytics

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

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

- Required Courses:

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

- Two OM Elective Courses:

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

*Choose two courses from this set of four courses.

- One Restricted Elective Course:

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

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

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

Fund your studies

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

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Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Read more
Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Data scientists help organisations make sense of their data. As data is collected and analysed in all areas of society, demand for professional data scientists is high and will grow higher. The emerging Internet of Things, for instance, will produce a whole new range of problems and opportunities in data analysis.

In the Data Science master’s programme, you will gain a solid understanding of the methods used in data science. You will learn not only to apply data science: you will acquire insight into how and why methods work so you will be able to construct solutions to new challenges in data science. In the Data Science master’s programme, you will also be able to work on problems specific to a scientific discipline and to combine domain knowledge with the latest data analysis methods and tools. The teachers of the programme are themselves active data science researchers, and the programme is heavily based on first-hand research experience.

Upon graduating from the Data Science MSc programme, you will have solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills. In particular, you will be able to:
-Understand the general computational and probabilistic principles underlying modern machine learning and data mining algorithms.
-Apply various computational and statistical methods to analyse scientific and business data.
-Assess the suitability of each method for the purpose of data collection and use.
-Implement state-of-the-art machine learning solutions efficiently using high-performance computing platforms.
-Undertake creative work, making systematic use of investigation or experimentation, to discover new knowledge.
-Report results in a clear and understandable manner.
-Analyse scientific and industrial data to devise new applications and support decision making.

The MSc programme is offered jointly by the Department of Computer Science, the Department of Mathematics and Statistics, and the Department of Physics, with support from the Helsinki Institute for Information Technology (HIIT) and the Helsinki Institute of Physics (HIP), all located on the Kumpula Science campus. In your applied data science studies you can also include multidisciplinary studies from other master's programmes, such as digital humanities, and natural and medical sciences.

The University of Helsinki will introduce annual tuition fees to foreign-language Master’s programmes starting on August 1, 2017 or later. The fee ranges from 13 000-18 000 euros. Citizens of non-EU/EEA countries, who do not have a permanent residence status in the area, are liable to these fees. You can check this FAQ at the Studyinfo website whether or not you are required to pay tuition fees: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

Programme Contents

The Data Science MSc programme combines elements from computer science and mathematical sciences to provide you with skills in topics such as machine learning, distributed systems and statistical methods. You might also find that knowledge in a particular scientific field is useful for your future career. You can obtain this through minor studies in the MSc programme, or it might already be part of your bachelor-level degree.

Studies in the Data Science MSc programme include both theoretical and practical components, including a variety of study methods (lectures, exercises, projects, seminars; done both individually and in groups). Especially in applied data science, we also use problem-based learning methods, so that you can address real-world issues. You will also practise academic skills such as scientific writing and oral presentation throughout your studies. You are encouraged to include an internship in your degree in order to obtain practical experience in the field.

Minor studies give you a wider perspective of Data Science. Your minor subject can be an application area of Data Science (such as physics or the humanities), a discipline that supports application of Data Science (such as language technology), or a methodological subject needed for the development of new Data Science methods and models (such as computer science, statistics, or mathematics).

Selection of the Major

You can specialise either in the core areas of data science -- algorithms, infrastructure and statistics -- or in its applications. This means that you can focus on the development of new models and methods in data science, supported by the data science research carried out at the University of Helsinki; or you can become a data science specialist in an application field by incorporating studies in another subject. In addition to mainstream data science topics, the programme offers two largely unique opportunities for specialisation: the data science computing environment and infrastructure, and data science in natural sciences, especially physics.

Programme Structure

You should be able to complete the MSc Programme in Data Science of 120 credits (ECTS) in two years of full-time study. The programme consists of:
-Common core studies of basic data science courses.
-Several modules on specific topics within data science algorithms, data science infrastructures and statistical data science, and on data science tools.
-Seminars and colloquia.
-Courses on academic skills and tools.
-Possibly an internship in a research group or company.
-Studies in an application domain.
-Master’s thesis (30 credits).

Career Prospects

Industry and science are flooded with data and are struggling to make sense of it. There is urgent demand for individuals trained to analyse data, including massive and heterogeneous data. For this reason, the opportunities are expected to grow dramatically. The interdisciplinary Data Science MSc programme will train you to work in data-intensive areas of industry and science, with the skills and knowledge needed to construct solutions to complex data analysis problems.

If you are focusing on the core areas of data science, you will typically find employment as a researcher or consultant, sometimes after taking a PhD in Computer Science or Statistics to deepen your knowledge of the field and research methods. If your focus is on the use of data science for specific applications, you will typically find work in industry or in other fields of science such as physics, digital humanities, biology or medicine.

Internationalization

The Data Science MSc is an international programme, with students from around the world and an international research environment. All of the departments taking part in the programme are internationally recognised for their research and a significant fraction of the teaching and research staff come from abroad.

The departments participate in international student exchange programmes and offer you the chance to include international experience as part of your degree. Data Science itself is an international field, so once you graduate you can apply for jobs in any country.

In the programme, all courses are in English. Although the Helsinki area is quite cosmopolitan and English is widely spoken, you can also take courses to learn Finnish at the University of Helsinki Language Centre. The Language Centre also offers an extensive programme of foreign language courses for those interested in learning other languages.

Research Focus

The MSc programme in Data Science is offered jointly by three departments and two research institutes. Their research covers a wide spectrum of the many aspects of data science. At a very general level, the focal areas are:
-Machine learning and data mining
-Distributed computation and computational infrastructures
-Statistical modelling and analysis
-Studies in the programme are tightly connected to research carried out in the participating departments and institutes.

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◾This is the first fully online postgraduate qualification designed specifically for RVNs currently being delivered in Scotland. Read more

Why this programme

◾This is the first fully online postgraduate qualification designed specifically for RVNs currently being delivered in Scotland. The part-time and online nature of the programme means it is ideally suited to individuals who are in full-time employment.
◾The University of Glasgow’s School of Veterinary Medicine is ranked 2nd in the UK (Complete University Guide 2016).
◾The University of Glasgow ranked top amongst UK Vet Schools in the National Student Survey (2016) with 98% overall student satisfaction, and the School of Veterinary Medicine combines both teaching excellence and a supportive learning environment.
◾The programme reflects the need for tomorrow’s RVNs to be involved in lifelong self-directed learning. It supports RVNs to become adaptive to the dynamic care environments that they work in, supports their delivery of quality care, and promotes the use of best practice techniques.
◾The programme offers specialist education in veterinary nursing which encompasses a core set of specialised skills, knowledge and competencies, supplemented by a choice of additional specialised areas including research, education, business management, ethics and best practice.
◾Close involvement of experts from different fields of veterinary nursing and the wider veterinary industry in the planning and delivery of courses ensures that the programme is current and relevant.

Programme structure

The MSc Advanced Practice in Veterinary Nursing comprises three components:

[[Three core courses (Year 1) ]]
◾Introduction to research and evidence based veterinary nursing
◾Clinical governance in veterinary medicine
◾Developing evidence informed practice through independent learning

Three optional courses (Year 2)

◾Promoting best practice in veterinary nursing
◾Animal and veterinary ethics
◾Introduction to teaching and assessment in veterinary nursing
◾Introduction to statistical methods
◾Introduction to veterinary business studies

Dissertation (Year 3)

The programme is delivered fully online using a range of learning and teaching approaches including lectures, seminars, tutorials, work-based learning and project work. You will have the flexibility to tailor the subject of many of your assessments and final dissertation to disciplines or specialisms that are relevant and of interest to you and/or your future career.

The online and part-time nature of this programme, and the flexibility this route offers, makes it ideally suited to individuals in full-time employment.

Core Courses:

Introduction to research and evidence based veterinary nursing

This course will introduce students to the concepts of theoretical and practical research. It will cover what research is and why it is carried out, the basic elements of the research process, different types of research (quantitative and qualitative) using relevant examples from veterinary nursing/medicine. Following on from this, students will be shown how to access research, how to find and evaluate evidence, carry out literature searches, utilise evidence in their own writing / studying and how to develop their own research questions.

Clinical governance in veterinary medicine

This course will enable veterinary nurses to learn from an industry expert to develop their knowledge of the key concepts underlying clinical governance in veterinary practice. Students will learn skills than can be directly applied to practice including how to perform audits and monitor performance and outcomes in their own clinical environments and how these skills can be applied if their practice undergoes accreditation or awards assessment.

Developing evidence informed practice through independent learning

This course enables veterinary nurses to learn theoretical frameworks of reflection and develop practical skills in personal reflection. Such skills allow the student to identify any deficits in an area of individual interest or specialism, which can then be addressed through independent learning using evidence informed practice.

Optional Courses:

Promoting best practice in veterinary nursing

This course allows students to develop best practice in a particular area of interest or specialism through independent learning by reviewing and assimilating the appropriate literature, which will then be disseminated to others using written and verbal techniques. This course will develop students’ knowledge and understanding of the different techniques that can be utilised to disseminate best practice. They will also learn skills in how to disseminate best practice such as writing for journal publications and delivering oral presentations.

Animal and veterinary ethics

This course will enable students to develop their knowledge of key concepts underlying animal and veterinary ethics. Participants will also improve their ethical reasoning skills by learning to utilise a logical approach to decision making when faced with ethical dilemmas.

Introduction to teaching and assessment in veterinary nursing

This course aims to provide veterinary nurses with the knowledge and skills to create an effective learning environment for individuals within the veterinary nursing profession. Students will analyse strategic learning theories and models of teaching in order to synthesise knowledge and adapt one's own teaching practice. The course will cover delivery of teaching, evaluation of competencies, and self-reflection allowing for research to be utilised and adapted to create a teaching, learning and assessment plan. Individuals will also cover key transferable skills which will influence teaching practice. A proportion of the course will focus on the teaching and assessment of clinical skills, and how it can be practically implemented and utilised within one’s own practice, and in an academic environment.

Introduction to statistical methods

This course assumes no prior knowledge of statistics. It covers graphical and numerical methods of displaying and summarising data along with the use and interpretation of confidence intervals, significance tests (t tests, chi-square tests, etc.), correlation and linear regression. Students get hands on experience of using appropriate statistical software to carry out these analyses.

Introduction to veterinary business studies

This course will enable students to develop their knowledge of core business concepts and how they apply to the veterinary industry. Participants will learn skills that can be utilised in practice from a variety of areas, including business strategy, marketing, finance and human resources.

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Statistical Sciences involves the study of random phenomena and encompasses a broad range of scientific, industrial, and social processes. Read more
Statistical Sciences involves the study of random phenomena and encompasses a broad range of scientific, industrial, and social processes. As data become ubiquitous and easier to acquire, particularly on a massive scale, models for data are becoming increasingly complex. The past several decades have witnessed a vast impact of statistical methods on virtually every branch of knowledge and empirical investigation.

The Master of Financial Insurance (MFI) is a full-time professional program based on three pillars: statistical methods, financial mathematics, and insurance modelling. This program is appropriate for students with backgrounds in statistics, actuarial science, economics, and mathematics. Students with a quantitative background (such as physics and engineering) and sufficient statistical training are also encouraged to apply.

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This is Europe's only graduate programme in demography with an emphasis on health and social epidemiology, and is designed for those interested in acquiring a technical understanding of the structure and dynamics of population change, its causes and consequences. Read more
This is Europe's only graduate programme in demography with an emphasis on health and social epidemiology, and is designed for those interested in acquiring a technical understanding of the structure and dynamics of population change, its causes and consequences. The curriculum includes advanced training in the theories and methods of the population sciences, statistics, epidemiology, and research methods.

The course teaches research skills which are highly valued in the job market generally and are welcomed in a wide variety of research fields. The teaching draws on several related disciplines within the School and the modular approach can be adapted (within reason) to suit different needs.

The course is recognised by both the MRC and ESRC as providing high quality research training and a small number of scholarships from these bodies (including 1+3 scholarships) are available to UK or EU residents. These are advertised each year with the School scholarships information.

Graduates have careers in public health, academic research of a very wide nature, NGOs, reproductive health programmes, health services, government statistical offices, policy and planning. The Selwyn-Clarke Prize is awarded for the best project of the year.

- Full programme specification (pdf) (http://www.lshtm.ac.uk/edu/qualityassurance/dh_progspec.pdf)
- Intercalating this course (http://www.lshtm.ac.uk/study/masters/intercalating/index.html)

Visit the website http://www.lshtm.ac.uk/study/masters/msdh.html

Objectives

By the end of this course students should be able to:

- demonstrate advanced knowledge and understanding of scientific, evidence-based approaches to the study of population issues

- critically assess and apply these approaches to inform development, health and population programmes

- formulate research questions and use demographic and health data, and appropriate methods of analysis, to address them

- identify causes and consequences of population change and relate these to underlying population dynamics

- demonstrate advanced knowledge and understanding of demographic behaviour in social, economic and policy contexts

- critically assess and apply findings of population studies to health and social policy

- demonstrate advanced knowledge and understanding of major population trends, including historical trends, in developed and developing countries

Structure

Term 1:
Students take the following compulsory modules:

- Demographic Methods
- Basic Epidemiology
- Population Studies
- Principles of Social Research
- Statistics for Epidemiology and Population Health

Terms 2 and 3:

Students take a total of five study modules, one module from each timetable slot (Slot 1, Slot 2 etc.). Students are expected to take modules related to demography for at least two of their other four choices.

*Recommended modules

- Slot 1:

Research Design & Analysis*
Designing Disease Control Programmes in Developing Countries
Health Care Evaluation
Sociological Approaches to Health

- Slot 2:

Family Planning Programmes*
Population, Poverty and Environment*
Conflict and Health
Design and Analysis of Epidemiological Studies
Statistical Methods in Epidemiology

- Slot 3:

Social Epidemiology*
Current Issues in Safe Motherhood & Perinatal Health
Epidemiology of Non-Communicable Diseases
Medical Anthropology and Public Health
Modelling & the Dynamics of Infectious Diseases
Spatial Epidemiology in Public Health

- Slot 4:
Population Dynamics & Projections (compulsory)

- Slot 5:

AIDS*
Analysing Survey & Population Data*
Advanced Statistical Methods in Epidemiology
Proposal Development

Further details for the course modules - http://www.lshtm.ac.uk/study/currentstudents/studentinformation/msc_module_handbook/section2_coursedescriptions/tdhe.html

Project Report:
During the summer months (July - August), students complete a research project to enable them to acquire personal experience of the process of contributing to knowledge in any of the fields covered by the course, for submission by early September. Acceptable types of project are: data analysis; a project proposal; an original literature or policy review.

Students normally remain in London for the preparation of their project report. Exceptionally, and only if appropriate, part of the project period may be spent away from the School, whether in the UK or abroad. Arrangements for this must be discussed and agreed with the Course Director.

Find out how to apply here - http://www.lshtm.ac.uk/study/masters/msdh.html#sixth

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