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Masters Degrees (Statistical Computing)

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This is a part-time, modular based programme for health professionals who wish to strengthen their statistical skills and ability to analyse data. Read more
This is a part-time, modular based programme for health professionals who wish to strengthen their statistical skills and ability to analyse data.

Students will gain the confidence in carrying out the methods that are widely used in medical statistics, and interpreting the results for the practice of evidence-based health care. The flexible modular structure has been devised for busy professionals and to fit with the structure of specialist training. The regulations allow students to take up to four years to complete the MSc.

This is a joint programme between the Nuffield Department of Primary Care Health Sciences and the Department for Continuing Education's Continuing Professional Development Centre. The Programme works in collaboration with the renowned Centre for Evidence-Based Medicine in Oxford.

This course is designed for doctors, nurses, pharmacists, midwives and other healthcare professionals, seeking to consolidate their understanding and ability in medical statistics. Core modules introduce the students to methods for observational and clinical trials research. Optional modules offer the students skills in growth areas such as systematic review, meta-analysis, and big data epidemiology, or specialist areas such as statistical computing, diagnosis and screening research and others. Teaching is tailored to non-statisticians and delivered by an experienced team of tutors from University of Oxford who bridge the disciplines of medical statistics and evidence-based health care.

This programme guides students through core and optional modules and a dissertation to a qualification in the application of medical statistics to evidence-based health care. Compared to the main EBHC programme, this will suit those with basic statistical understanding who seek training who now seek deeper understanding on a broader base of statistical methods.

Visit the website https://www.conted.ox.ac.uk/about/msc-in-ebhc-medical-statistics

Course aims

The course aims to give healthcare professionals high competence in the concepts, methods, terminology and interpretation of medical statistics; and hence, enhance their ability to carry out their own research and to interpret published evidence.

• Gain competence in execution and interpretation of core statistical techniques used by medical statisticians (outside the context of clinical trials), particularly those used in multivariable analyses: multiple linear regression, logistic regression, and survival modelling; statistical analysis plans and statistical reporting.
• Gain competence in execution and interpretation of core statistical techniques used by medical statisticians in clinical trials.
• Gain competence in execution and interpretation of four other areas, selected by the student from the following options: meta-analysis; systematic review; big data epidemiology; statistical computing; diagnosis and screening; study design and research methods.
• Gain hands-on experience, supervised by a senior member of our medical statistics team, of the analysis or meta-analysis of healthcare data, in order to address a question in evidence-based health care.

Programme details

The MSc in EBHC Medical Statistics is a part-time course.

There are two compulsory modules, four option modules (two from group 1 and two more either from either group 1 or 2) and a dissertation.

Compulsory Modules

• Essential Medical Statistics
• Statistics for Clinical Trials

Optional Modules – 1

• Meta-analysis
• Big Data Epidemiology
• Statistical Computing with R and Stata (online)

Optional Modules – 2

• Introduction to Study Design and Research Methods
• Systematic Reviews
• Evidence-based Diagnosis and Screening

A module is run over an eight-week cycle where the first week is spent working on introductory activities using a Virtual Learning Environment, the second week is spent in Oxford for the face-to-face teaching week, there are then four post-Oxford activities (delivered through the VLE) which are designed to help you write your assignment. You then have a week of personal study and you will be required to submit your assignment electronically the following week.

Online modules are delivered entirely through a Virtual Learning Environment with the first week allocated to introductory activities. There are ten units to work through which are released week-by-week, you then have five weeks of personal study with use of a revision forum and then you will be required to submit your assignment electronically the following week.

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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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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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Machine learning, data mining and high-performance computing are concerned with the automated analysis of large-scale data by computer, in order to extract the useful knowledge hidden in it. Read more
Machine learning, data mining and high-performance computing are concerned with the automated analysis of large-scale data by computer, in order to extract the useful knowledge hidden in it. Using state-of-the-art artificial intelligence methods, this technology builds computer systems capable of learning from past experience, allowing them to adapt to new tasks, predict future developments, and provide intelligent decision support. Bristol's recent investment in the BlueCrystal supercomputer - and our Exabyte University research theme - show our commitment to research at the cutting edge in this area.

This programme is aimed at giving you a solid grounding in machine learning, data mining and high-performance computing technology, and will equip you with the skills necessary to construct and apply these tools and techniques to the solution of complex scientific and business problems.

Programme structure

Your course will cover the following core subjects:
-Introduction to Machine Learning
-Research Skills
-Statistical Pattern Recognition
-Uncertainty Modelling for Intelligent Systems

Depending on previous experience or preference, you are then able to take optional units which typically include:
-Artificial Intelligence with Logic Programming
-Bio-inspired Artificial Intelligence
-Cloud Computing
-Computational Bioinformatics
-Computational Genomics and Bioinformatics Algorithms
-Computational Neuroscience
-High Performance Computing
-Image Processing and Computer Vision
-Robotics Systems
-Server Software
-Web Technologies

You must then complete a project that involves researching, planning and implementing a major piece of work. The project must contain a significant scientific or technical component and will usually involve a software development component. It is usually submitted in September.

This programme is updated on an ongoing basis to keep it at the forefront of the discipline. Please refer to the University's programme catalogue for the latest information on the most up-to-date programme structure.

Careers

Skilled professionals and researchers who are able to apply these technologies to current problems are in high demand in today's job market.

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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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Data Science brings together computational and statistical skills for data-driven problem solving. Read more
Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

Degree information

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

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

Core modules
-Applied Machine Learning
-Introduction to Supervised Learning
-Introduction to Statistical Data Science

Optional modules - students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules:
-Cloud Computing (Birkbeck)
-Machine Vision
-Information Retrieval & Data Mining
-Statistical Natural Language Processing
-Web Economics

Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules:
-Statistical Design of Investigations
-Applied Bayesian Methods
-Decision & Risk

Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules:
-Supervised Learning
-Graphical Models
-Bioinformatics
-Affective Computing and Human-Robot Interaction
-Computational Modelling for Biomedical Imaging
-Stochastic Systems
-Forecasting

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. A thorough understanding of the fundamentals required from the best practitioners, and this programme's broad base, assists data scientists to adapt to rapidly evolving goals. This is a new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
-Google Deepmind
-Microsoft Research
-Dunnhumby
-Index Ventures
-Last.fm
-Cisco
-Deutsche Bank
-IBM
-Morgan Stanley

Why study this degree at UCL?

The 2014 Research Excellence Framework ranked UCL first in the UK for computer science. 61% of its research work is rated as world-leading and 96% as internationally excellent.

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; and exposure to interdisciplinary research spanning UCL Computer Science and UCl Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

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

About the course

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

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

Aims

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

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

Course Content

Compulsory modules:

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

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

Teaching

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

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

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

Assessment

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

Special Features

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

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

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The 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 course provides a broad grounding in advanced statistical methods, with a focus on applications in research, the NHS and the pharmaceutical industry; the course structure also allows mathematicians with some statistical experience to move into this field. Read more

Summary

This course provides a broad grounding in advanced statistical methods, with a focus on applications in research, the NHS and the pharmaceutical industry; the course structure also allows mathematicians with some statistical experience to move into this field.

Modules

Analysis of repeated measures; bayesian methods; biological assay; clinical trials; communications and research skills; computer-intensive statistical methods; design and analysis of experiments; epidemiological studies; generalised linear models; multi-level modelling; multivariate distribution theory and inference; statistical computing; statistical genetics; survival analysis; univariate theory and inference; dissertation.

Visit our website for further information...



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This MSc has a strong theoretical and methodological component to supplement a focus on applications of statistics to real life scientific problems. Read more
This MSc has a strong theoretical and methodological component to supplement a focus on applications of statistics to real life scientific problems. You can opt to follow pathways in medical, pharmaceutical or environmental statistics, depending on your field of interest. Graduates tend to enter careers as practising statisticians, university research assistants or go on to study for a PhD.

For each pathway, you will follow a set of compulsory modules covering core theory and methods, applied statistical modelling and practical skills in topics such as statistical computing, scientific writing, presentation and consultancy. You will also study optional modules tailored to your research interests and career aspirations. Your studies are completed with a supervised, in-depth, dissertation aimed at solving a substantive research question.

Modules
Compulsory modules:
• Statistics in Practice
• Likelihood Inference
• Generalised Linear Models
• Bayesian Inference
• Computational Intensive Methods

Optional modules (choose five from):
• Extreme Value Theory
• Clinical Trials
• Principles of Epidemiology
• Statistical Genetics and Genomics
• Longitudinal Data Analysis
• Pharmacological Modelling
• Survival and Event History Analysis
• Adaptive/Bayesian Methods in Clinical Research
• Environmental Epidemiology

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This unique course will give you the skills required for success in the highly competitive field of international conservation. It is taught in partnership with three of the most high-profile conservation practitioners in the UK. Read more
This unique course will give you the skills required for success in the highly competitive field of international conservation.

It is taught in partnership with three of the most high-profile conservation practitioners in the UK:

The Royal Botanic Gardens at Kew
The Institute of Zoology, the research division of the Zoological Society of London
The Durrell Wildlife Conservation Trust

You will be immersed in the ongoing conservation work of these organisations, and will be able to choose six-month research project topics linked to their conservation programmes, ensuring that your project contributes to real-world conservation.

The course provides a strong quantitative basis for conservation work, including decision theory, conservation planning, statistical computing and modelling.

By learning to collect, analyse and use both socioeconomic and biological information, you will gain a truly interdisciplinary understanding of the theory and practice of conservation.

By the end of the course you will not only have developed an ability to analyse conservation issues, but you will also know how to put this understanding into action, implementing successful conservation projects.

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This programme provides intensive training in statistics applicable to the social sciences, econometrics and finance. The aim of the programme is to foster an interest in theoretical and applied statistics and equip you for work as a professional statistician. Read more

About the MSc programme

This programme provides intensive training in statistics applicable to the social sciences, econometrics and finance. The aim of the programme is to foster an interest in theoretical and applied statistics and equip you for work as a professional statistician. You will learn how to analyse and critically interpret data, build statistical models of real situations, and use programming tools and statistical software packages.

The compulsory course in Statistical Inference: Principles, Methods and Computation will provide you with comprehensive coverage of fundamental aspects of probability and statistics methods and principles. This course provides the foundations for the optional courses on more advanced statistical modelling, computational methods, statistical computing and advanced probability theory. Options also include specialist courses from the Departments of Methodology, Management, Mathematics, Economics and Social Policy. Students on the taught master’s programme will take optional courses to the value of three units, while those on the research track will substitute one unit with a dissertation, making the research track a 12 month programme.

Graduates of the programme are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.

Graduate destinations

Students on this programme have excellent career prospects. Former students have taken up positions in consulting firms, banks and in the public sector. Many go on to take higher degrees. Graduates of the MSc are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.

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This MSc teaches advanced analytical and computational skills for success in a data rich world. Read more
This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

Degree information

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

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 project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules
-Supervised Learning
-Statistical Modelling and Data Analysis
-Graphical Models or Probabilistic and Unsupervised Learning
Plus one of:
-Applied Bayesian Methods
-Statistical Design of Investigations
-Statistical Computing
-Statistical Inference

Optional modules - students select 60 credits from the following list:
-Advanced Topics in Machine Learning
-Affective Computing and Human-Robot Interaction
-Applied Bayesian Methods
-Approximate Inference and Learning in Probabilistic Models
-Computational Modelling for Biomedical Imaging
-Information Retrieval and Data Mining
-Machine Vision
-Selected Topics in Statistics
-Optimisation
-Statistical Design of Investigations
-Statistical Inference
-Statistical Natural Language Programming
-Stochastic Methods in Finance
-Stochastic Methods in Finance 2
-Advanced Topics in Statistics
-Mathematical Programming and Research Methods
-Intelligent Systems in Business

Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include; finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Top career destinations for this degree:
-Statistical and Algorithm Analyst, Telemetry
-Decision Scientist, Everline
-Computer Vision Researcher, Slyce
-Data Scientist, YouGov
-Research Engineer, DeepMind

Employability
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

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There is currently a worldwide shortage in graduates qualified in Bioinformatics and the skills to interpret the data that is going to underpin advances in biology and medicine in 21st Century. Read more
There is currently a worldwide shortage in graduates qualified in Bioinformatics and the skills to interpret the data that is going to underpin advances in biology and medicine in 21st Century. With the advent of Personalised Medicine, the demand for specialists in Computational Biology and Bioinformatics will further increase. This gives you the opportunity to build your transferable skill set across a range of cutting edge technologies and start building a career in this central facet of modern biology.

Students completing the MSc course in Bioinformatics and Computational Genomics will have the necessary skills and knowledge to undertake research and development in industry (Biotechnology, Pharmaceutical, Diagnostic companies), in medical research centres and in academic institutions worldwide.

Computational, statistical and machine learning methods form an integral part of modern research in Molecular Biology, Cell Biology, Pharmacology, Public Health Care and in Medicine. The past decade has seen enormous progress in the development of molecular and biomedical technologies. Today’s high-throughput array and sequencing techniques produce data in the range of terabytes on a daily basis and new technologies continuously emerge. This will further increase the stream of data available for biomedical research. For this reason analyzing, visualizing and managing this huge amount of data is a challenging task. The Queen’s MSc course in Bioinformatics and Computational Genomics targets these data-driven challenges of modern science. The course is open to graduates in computer science, life sciences, physics or statistics.

The programme will consist of an Introductory short course (two weeks) in Cell Biology, followed by modules in:

• Genomics & Genetics
• Analysis of Gene Expression
• Scientific Programming & Statistical Computing
• Algorithmic Biology
• Statistical Biology
• Bioimaging Informatics
• Research project : MSc dissertation

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Logistics and supply chain management bring together the business skills to manage the activities and flows of information between suppliers, manufacturers, logistics service providers, retailers and consumers. Read more

Why take this course?

Logistics and supply chain management bring together the business skills to manage the activities and flows of information between suppliers, manufacturers, logistics service providers, retailers and consumers.

This course focuses on the integration of analytical techniques for optimisation with the decision issues and technology relating to logistics and supply chain management.

This course is one of a small number selected as part of the HEFCE PEP Scholarship programme for 2014. Please visit the HEFCE PEP page to see full details of the scheme, eligibility criteria and how to apply.

What will I experience?

On this course you can:

Have access to ultra-modern computing facilities, and use specialist mathematical and statistical computing packages
Participate in practical sessions to solve real-life case studies using our simulation software
Develop the problem-solving, decision-making and interpersonal abilities essential to professional roles in this field

What opportunities might it lead to?

Logistics analysis is critical to success in both manufacturing and service industries. Competitive advantage will increasingly come from the supplier's ability to rapidly respond to changing customer needs, for which effective logistics are of prime importance. This means that there will be a range of companies and organisations in both the public and private sector, demanding for your skills and expertise.

Here are some routes our graduates have pursued:

Production management
Transportation management
Quality control
Distribution
Facilities planning
Supply chain management
Passenger transportation

This course is accredited by the Chartered Institute of Logistics and Transport (CILT). Students studying CILT accredited courses receive exemptions from the academic requirements for membership. Graduates of the MSc Logistics and Supply Chain Management course at the University of Portsmouth with an overall average pass mark above 50% are eligible to apply for Chartered Membership (CMILT) once they have gained the necessary experience.

Module Details

Supply chain management is a philosophy, the implementation process and the control of this process through which different entities within a supply chain aim to streamline their activities to improve the overall effectiveness and efficiency of meeting final customer requirements. A variety of different techniques will be investigated, ranging from conceptual frameworks, analytical approaches, to computer-based models.

Here are the units you will study:

Supply Chain Management: This unit enable you to develop advanced skills so that you can deal with problems of supply chain management across different products, locations, and companies. The types of problems studied in this course are encountered in industry (e.g. retail, discrete or continuous production and logistics service providers) as well as in service organisations (e.g. banks, hospitals and law firms). Managers dealing with such problems are known under various titles, including production, operations, supply (chain), inventory, purchasing, distribution or logistics managers.

Logistics Modelling: Most problems arising in the fields of logistics and supply chain management have sufficient complexity and detail that they require the use of sophisticated modelling techniques. This unit looks at two of the most commonly used methodologies for modelling and solving logistics problems: simulation and heuristic techniques. In both cases a computer package is used to assist solution. The techniques will be demonstrated with a range of case studies drawn from the field of logistics including transportation, supply chain configuration and management, warehouse design and layout, container port layout, production planning and vehicle routing.

Operations Management: This unit teaches operations management techniques that are relevant to logistics. The commonly used techniques of linear and integer programming will be taught using Microsoft Excel based methods for solution. You will look at case studies covering production planning, transportation, logistics planning and supply chain configuration. You will also be taught about locating facilities such as factories, distribution centres, cross docking centres and retail outlets. The effective scheduling of labour force and machines will be demonstrated, and current state-of-the art production planning models will be covered.

Strategic Logistics: This unit looks at the field of logistics from a strategic point of view. A number of quantitative techniques for strategic decision making such as decision analysis, multi-criteria decision analysis, data envelopment analysis and queuing theory are introduced in the context of logistics applications. The topic of strategic decisions in transportation modelling is then covered. The unit is completed by the analysis of a number of case studies relating to different applications of logistics with respect to financial, environmental, societal and economic objectives.

Project (Masters Logistics): This unit allows you to conduct research into a larger scale or challenging logistics problem. The project may be practical or theoretical and may arise either from the supervisor's research interests or from your own desire to study a particular topic or situation. Typical areas of logistics in which the project will be conducted include (but are not limited to) transportation, supply chain configuration and management, warehouse design and layout, container port layout, production planning, green logistics, facility location and vehicle routing.

Programme Assessment

Our enthusiastic team of lecturers have a wide range of industrial and research experience, ensuring that you graduate with cutting-edge knowledge. You will be taught through a mixture of lectures, seminars, tutorials, practical computer-based sessions, laboratory and project work.

We assess you in a several ways, but a large portion of the assessment is based on a major project at the end of the year. Here’s how we assess your work:

Examinations
Coursework
Laboratory assignments
A dissertation

Student Destinations

Logistics and transportation are important to any firm where customer service is a strategic objective – whether its core focus is on products or services.

When you graduate from this course you could find employment in a wide range of logistics-related careers. Not only in the traditional areas of manufacturing logistics, distribution and supply chain management, but also postal and express delivery, the fire and rescue emergency operations and even the military and defence industry.

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