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Masters Degrees (Computational Statistics)

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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.

About this degree

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 two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 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 (15 credits)
  • Statistical Modelling and Data Analysis (15 credits)

Optional modules

Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.

Group One Options (15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Group Two Options (30 to 60 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)

Group Three Options (15 credits)

  • Applied Bayesian Methods (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Inference (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

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.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MSc

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.

Recent career destinations for this degree

  • Data Scientist, Interpretive
  • Software Engineer, Google
  • Data Scientist, YouGov
  • Research Engineer, DeepMind
  • PhD in Computer Science, UCL

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.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

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 the 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.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. Read more

There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. MRes graduates benefit from the department's excellent links in finding employment; this programme is also ideal preparation for a research career.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the areas of computational statistics and machine learning (CSML). Students will have the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research. They also undertake a nine-month research project which enables the department to more fully assess their research potential.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (30 credits), three optional modules (45 credits) and a dissertation (105 credits).

Core modules

  • Investigating Research
  • Researcher Professional Development

Optional modules

Student select three modules from the following:

  • Advanced Deep Learning and Reinforcement Learning
  • Advanced Topics in Machine Learning
  • Applied Bayesian Methods
  • Approximate Inference and Learning in Probabilistic Models
  • Graphical Models
  • Information Retrieval and Data Mining
  • Introduction to Deep Learning
  • Introduction to Machine Learning
  • Inverse Problems in Imaging
  • Machine Vision
  • Probabilistic and Unsupervised Learning
  • Selected Topics in Statistics
  • Statistical Computing
  • Statistical Inference
  • Statistical Models and Data Analysis
  • Supervised Learning

Dissertation/report

All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with assistance from demonstrators. Students liaise with their academic or industrial supervisor to choose a study area of mutual interest for the research project. Performance is assessed by unseen written examinations, coursework and the research dissertation.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MRes

Careers

Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, and Chicago, as well as at UCL. Similarly, CSML graduates now work in companies in Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in CSML graduates.

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, while in London there are many companies looking to understand their customers better who have hired CSML graduates. Computational statistics and machine learning skills are in particular demand in areas including finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. CSML graduates have obtained PhD positions both in machine learning and related large-scale data analysis, and across the sciences.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning, having co-ordinated the PASCAL European Network of Excellence which represents the largest network of machine learning researchers in Europe.

UCL Computer Science graduates are particularly valued by the world’s leading organisations in internet technology, finance, and related information areas, as a result of the department’s strong international reputation and ideal location close to the City of London.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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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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About the MSc programme. The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences. Read more

About the MSc programme

The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences.

You will gain insights into the design and analysis of social science studies, including large and complex datasets, study the latest developments in statistics, and learn how to apply advanced methods to investigate social science questions.  

The programme includes two core courses which provide training in fundamental aspects of probability and statistical theory and methods, the theory and application of generalised linear models, and programming and data analysis using the R and Stata packages. These courses together provide the foundations for the optional courses on more advanced statistical modelling, computational methods and statistical computing. Options also include specialist courses from the Departments of Methodology, Economics, Geography and Social Policy. 

Research Stream

The Research stream is similar to the nine-month programme, but will include a dissertation component, extending the programme to twelve months.

Graduate destinations

There is a high demand for graduates with advanced statistics training and an interest in social science applications, and students on this programme have excellent career prospects. 

Potential employers include the public sector (the Office for National Statistics, government departments, universities), market research organisations, survey research organisations and NGOs. This programme would be ideal preparation for doctoral research in social statistics or quantitative social science.

Further information on graduate destinations for this programme



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

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

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

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

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

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

Statistics at Kent provides:

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

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

- advanced and accessible computing and other facilities

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

Course structure

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

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

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

Research areas

- Biometry and ecological statistics

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

- Bayesian statistics

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

- Bioinformatics, statistical genetics and medical statistics

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

- Nonparametric statistics

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

Careers

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

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

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

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

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

Career opportunities

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

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

Programme structure

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

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

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This Masters in Advanced Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics. Read more

This Masters in Advanced Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics.

Why this programme

  • The Statistics Group at Glasgow is a large group, internationally renowned for its research excellence.
  • Our expertise spans topics including: biostatistics and statistical genetics; environmental statistics; statistical methodology; statistical modelling and the scholarship of learning and teaching in statistics.
  • Our Statistics MSc programmes benefit from close links lecturers have with industry and non-governmental organisations such as NHS and SEPA.
  • You will develop a thorough grasp of statistical methodology, before going on to apply statistical skills to solve real-life problems.
  • You will be equipped with the skills needed to begin a career as a professional statistician; previous study of statistics is not required.
  • You will be taught by world-leading experts in their fields and will participate in an extensive and varied seminar programme, are taught by internationally renowned lecturers and experience a wide variety of projects.
  • Our students graduate with a varied skill set, including core professional skills, and a portfolio of substantive applied and practical work.

Programme structure

Modes of delivery of the Masters across the Statistics programmes include lectures, laboratory classes, seminars and tutorials and allow students the opportunity to take part in lab, project and team work.

Courses include (twelve chosen)

  • Advanced Bayesian methods
  • Advanced data analysis
  • Bayesian statistics
  • Biostatistics
  • Computational inference
  • Data analysis
  • Data management and analytics using SAS
  • Design of experiments
  • Environmental statistics
  • Flexible regression
  • Financial statistics
  • Functional data analysis
  • Generalised linear models
  • Introduction to R programming
  • Linear mixed models
  • Machine learning
  • Multivariate methods
  • Principles of probability and statistics
  • Professional skills
  • Spatial statistics
  • Statistical genetics
  • Stochastic processes
  • Time series
  • Statistics project and dissertation.

Summer (May – August)

Statistics project and dissertation (60) - assessed by a dissertation

Career prospects

Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD.



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This Masters in Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics. Read more

This Masters in Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics.

Why this programme

  • The Statistics Group at Glasgow is a large group, internationally renowned for its research excellence.
  • Our expertise spans topics including: biostatistics and statistical genetics; environmental statistics; statistical methodology; statistical modelling and the scholarship of learning and teaching in statistics.
  • Our Statistics MSc programmes benefit from close links lecturers have with industry and non-governmental organisations such as NHS and SEPA.
  • You will develop a thorough grasp of statistical methodology, before going on to apply statistical skills to solve real-life problems.
  • You will be equipped with the skills needed to begin a career as a professional statistician; previous study of statistics is not required.
  • You will be taught by world-leading experts in their fields and will participate in an extensive and varied seminar programme, are taught by internationally renowned lecturers and experience a wide variety of projects.
  • Our students graduate with a varied skill set, including core professional skills, and a portfolio of substantive applied and practical work.

Programme structure

Modes of delivery of the Masters across the Statistics programmes include lectures, laboratory classes, seminars and tutorials and allow students the opportunity to take part in lab, project and team work.

Core courses (compulsory)

  • Bayesian statistics
  • Generalised linear models 
  • Introduction to R programming 
  • Probability 1
  • Regression models 
  • Statistical inference 1
  • Statistics project and dissertation.

Optional courses (six chosen, but at least one course must be from Group 1)

Group 1

  • Data analysis 
  • Professional skills.

Group 2

  • Biostatistics 
  • Computational inference 
  • Data management and analytics using SAS 
  • Design of experiments 
  • Environmental statistics 
  • Financial statistics 
  • Functional data analysis 
  • Machine learning
  • Multivariate methods 
  • Spatial statistics 
  • Statistical genetics 
  • Stochastic processes 
  • Time series.

1 Any student who, in the course of study for his or her first degree, has already completed the equivalent of the Probability and/or Statistical inference courses can substitute these courses by any other optional course (including optional courses offered as part of the MRes in Advanced Statistics). The choice of substituting courses is subject to approval by the Programme Director.

Summer (May – August)

Statistics project and dissertation (60) - assessed by a dissertation.

Career prospects

Our graduates have an excellent track record of gaining employment in many sectors including finance, medical research, the pharmaceutical industry and government statistical services, while others have continued to a PhD.



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The scheme is designed to introduce key, practice-based skills in statistics for Computational Biology. You will contribute knowledge to the design of Biological experiments to ensure that appropriate statistical analysis of experimental data is possible. Read more

About the course

The scheme is designed to introduce key, practice-based skills in statistics for Computational Biology. You will contribute knowledge to the design of Biological experiments to ensure that appropriate statistical analysis of experimental data is possible.

You will learn how to critically evaluate the application of specific statistical techniques to research problems in Computational Biology and then effectively interpret and report the results of analyses.

This master’s degree is all about computational biology and statistics and will be of interest to students that are looking for the minimum entry-level qualification for many excellent employment opportunities in pharmaceuticals, advanced agriculture and in public health.

The course is a collaboration between the departments of Computer Science, Maths and also the Institute of Biological Environmental and Rural Science. The study scheme will bring the departments together in research-led teaching in these areas and you will benefit from expertise and insight from these highly specialised departments. In the most recent Research Excellence Framework assessment (2014) it was found that 95% of the universities research was of an internationally recognised standard or higher.

Course structure and content

The duration of the course is twelve months full-time or 24 months part time. The academic year (September to September) is divided into three semesters: September to January; January to June; June to September. The course is available as a postgraduate certificate or diploma and can be taken part-time. Students must contact the department to discuss these options.

Core modules:

Frontiers in the Biosciences
Programming for Scientists
Research Skills and Personal Development for Scientists
Statistical Concepts, Methods and Tools
Machine Learning for Intelligent Systems
Research Skills and Personal Development for Scientists (1520)
Statistical Techniques for Computational Biology

Optional modules:

Dissertation

Contact Time

Approximately 10-14 hours a week in the first two semesters. During semester three you will arrange your level of contact time with your assigned supervisor.

Assessment

The programme comprises 180 credits. There are 120 credits of taught modules completed during Semester 1 and Semester 2. This is followed by a research dissertation (60 credits) in semester 3.

This degree will suit you:

- If you already have a background in one of biology, maths or computing and now want training in this exciting interdisciplinary area to enhance your current skills.

- If you have a high 2:2 degree or higher in a related discipline

- If you wish to gain academic expertise and practical experience in Computational Biology.

- If you wish to enter a career in Statistics for Computational Biology with opportunities to work in pharmaceuticals, advanced agriculture and public health.

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About the MSc programme. The MSc Statistics provides intensive training in statistics applicable to the social sciences, economics and finance. Read more

About the MSc programme

The MSc Statistics provides intensive training in statistics applicable to the social sciences, economics 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 to analyse and critically interpret data, build statistical models of real situations, and use programming tools and statistical software packages. 

The compulsory course will provide you with comprehensive coverage of fundamental aspects of probability and statistical methods and principles. It 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. 

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

MSc Statistics (Research)

The research stream is similar to the MSc Statistics nine-month programme but involves a compulsory dissertation which replaces one unit's worth of optional courses and extends the length of the programme to 12 months. 

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.

Further information on graduate destinations for this programme



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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. The quantitative skills training provided by this MSc can lead to new and exciting opportunities in industry, medicine, government, commerce or research.

About this degree

The programme takes 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.

Further information on modules and degree structure is available on the department website: Statistics MSc

Careers

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

Recent career destinations for this degree

  • Data Analyst, Bupa
  • Quantitative Risk Analyst, Santander
  • PhD in Statistics, UCL
  • Management Associate, HSBC
  • Statistical Analyst, Nielsen

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.



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

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

About this degree

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

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (45 credits), four or five optional modules (60 to 75 credits), up to one elective module (15 credits) and a dissertation (60 credits).

Core modules

  • Business Strategy and Analytics (15 credits)
  • Data Analytics (15 credits)
  • Programming for Business Analytics (15 credits)

Optional modules

Students must choose a minimum of 60 and a maxuimum of 75 credits from Optional modules. A maximum of 15 credits may be taken from Electives.

  • Consulting Psychology (15 credits)
  • Consumer Behaviour (15 credits)
  • Data Science for Spatial Systems (15 credits)
  • Decision and Risk (15 credits)
  • Decision and Risk Analysis (15 credits)
  • Group Mini Project: Digital Visualisation (30 credits)
  • Introduction to Machine Learning (15 credits)
  • Mastering Entrepreneurship (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Models and Data Analysis (15 credits)
  • Talent Management (15 credits)
  • Urban Simulation (15 credits)
  • Web Economics (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

Dissertation/report

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

Teaching and learning

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

Further details are available on UCL Computer Science website.

Further information on modules and degree structure is available on the department website: Business Analytics (with specialisation in Computer Science) MSc

Careers

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

Employability

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

Why study this degree at UCL?

UCL Computer Science is a global leader in research in experimental computer science. The department scored highest among UK universities for the quality of research in Computer Science and Informatics in the Research Excellence Framework (REF2014), with 96% regarded as 'world-leading' or 'internationally excellent'.

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

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

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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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.

About this degree

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 in 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 Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At 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 of 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.

Further information on modules and degree structure is available on the department website: Data Science MSc

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:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough 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 should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt 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.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Statistical Science

82% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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During the last decade, there has been a dramatic increase in the size and complexity of data collected by both Industry and the Academic community. Read more

During the last decade, there has been a dramatic increase in the size and complexity of data collected by both Industry and the Academic community. Examples include genomic and proteomic data on one hand, and social media and electronic commerce data on the other. The availability of such vast quantities of data is having a large impact on both academic research and business practices. In particular, it is changing the nature and focus of the statistical profession.

The Department of Statistics at the University of Haifa is dedicated to providing its students with a rigorous, modern and up to date education in this exciting era for our profession. We focus on fundamentals while teaching the newest methods and applications. We emphasize computational know-how, the understanding of statistical ideas, and the ability to apply them. We train our graduates to analyze and draw meaningful conclusions from data, a skill that is becoming ever more important. Our goal is to see out graduates assume leading roles, within the profession, whatever their career choices may be.

What you will study

The International Master of Arts program in Statistics at the University of Haifa in Israel is dedicated to providing you with a rigorous, modern, and up-to-date education in Statistics. You will be trained to analyze and draw meaningful conclusions from data collected in a variety of application areas. This includes clinical trials, finance, genomics, and social media and web/mobile applications. The ability to reason with data is a skill that is becoming ever more important in the current age of Big Data, Data Science, and the Digital Economy.

The cutting-edge curriculum emphasizes computational know-how and the fundamental and core themes of the statistical sciences. At the same time it is also mathematically rigorous and challenging. The program will prepare you for further academic studies as well as for data related leadership roles in various industries including High-Tech, E-commerce, Healthcare, Research and Development and more.

The one-year program (non-thesis track) is taught in English over 3 consecutive semesters. A thesis track is also offered to those students who are interested in continuing to Ph.D. Your academic studies at the University of Haifa will be enhanced with networking opportunities with students from Israel and all over the world as well as by vibrant life on and off campus, which will acquaint you with Israeli culture, language, and start-up mentality.

Career

We are entering a golden age for the statistical sciences and profession. Companies large and small have come to realize that their data is among their most important assets, an asset they should analyze and learn from. Consequently, the demand for statistical expertise, and for statisticians, is rapidly growing. In addition to the traditional jobs in academia, government and the pharmaceutical industry, there is today a huge demand in modern applications such as finance, e-commerce, and social media to name a few. In some of these settings, statisticians go by the name of data scientists. The data explosion is also fueling research in new methods and models.

Graduates of our program have found employment in a variety of settings. Some have gone to assume academic positions. The majority, however, have found work in either industry and/or government. This includes large companies such as the Israeli Electric Company, the Israeli Ministry of Health, and Pharmaceutical companies such as Teva. Other graduates have preferred to work for smaller companies in various industries including the biomedical arena and as consultants in such companies as Boston Consulting. Recent graduates have found work as data scientists in the high-tech sector with transnational companies such as Intel and Microsoft.

Courses

The Non-Thesis Track lasts for three consecutive semesters over 12 months. In addition to a non-thesis track, a Thesis Track is offered, which takes five semesters over 24 months. The courses include:

  • Probability
  • Applied statistics I
  • Algorithms for data analysis
  • Stochastic process with applications
  • Statistical Inference
  • Applied statistics II
  • Statistical learning theory
  • The analysis of large and high dimensional data sets
  • Statistical Consulting Project

Note: Internship/Statistical Practice may be included for outstanding students. For more details on the courses, please click here

Faculty

The faculty members of the Department of Statistics have broad international experience. Some were born outside of Israel, others have studied abroad and/or hold doctorate degrees from foreign universities. All of the faculty members either have been post-doctoral fellows or have held visiting positions at top universities in Europe, East Asia or the US. Our faculty members regularly publish research in the highly ranked international journals and lecture at the top conferences of the profession. 

The Head of the program, Professor Ori Davidov, has been a faculty member in Haifa since October 1999. Professor Davidov's research has been supported by the Israeli Science Foundation, the Binational Science Foundation, and the USA’s National Institute of Health. Professor Davidov has published the results of his research work in the top Academic Journals and has had the opportunity to present his research at numerous international venues. He has twenty years of teaching experience at the undergraduate and graduate level. Professor Davidov has taught courses in Israel, the United States, Europe, and Thailand and is an active participant in the Inter-European Network of researchers. In addition, professor Davidov has consulted pharmaceutical companies, venture capital, and software firms. 

A full list of faculty staff can be found here

Scholarships

Scholarships are available for exceptional applicants. International school may offer scholarships for early applicants. Learn about application deadlines here.

Information on other scholarships and financial aid can be found here.



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This course provides specialist skills in core systems biology with a focus on the development of computational and mathematical research skills. Read more

This course provides specialist skills in core systems biology with a focus on the development of computational and mathematical research skills. It specialises in computational design, providing essential computing and engineering skills that allow you to develop software to program biological systems.

This interdisciplinary course is based in the School of Computing Science and taught jointly with the Faculty of Medical Sciences and the School of Mathematics and Statistics. The course is ideal for students aiming for careers in industry or academia. We cater for students with a range of backgrounds, including Life Sciences, Computing Science, Mathematics and Engineering.

Computational Systems Biology is focused on the study of organisms from a holistic perspective. Computational design of biological systems is essential for allowing the construction of complex and large biological systems.

We provide a unique, multidisciplinary experience essential for understanding systems biology. The course draws together the highly-rated teaching and research expertise of our Schools of Computing Science, Mathematics and Statistics, Biology, and Cell and Molecular Biosciences. The course also has strong links with Newcastle's Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN).

Our course is designed for students from both biological and computational backgrounds. Prior experience with computers or computer programming is not required. Students with mathematical, engineering or other scientific backgrounds are also welcome to apply.

The course is part of a suite of related programmes that also include:

-Bioinformatics MSc

-Synthetic Biology MSc

-Computational Neuroscience and Neuroinformatics MSc

All four programmes share core modules, creating a tight-knit cohort. This encourages collaborations on projects undertaking interdisciplinary research.

Project work

Your five month research project gives you a real opportunity to develop your knowledge and skills in depth in Systems Biology. You have the opportunity to work closely with a leading research team in the School and there are opportunities to work on industry lead projects. You will have one-to-one supervision from an experienced member of the faculty, supported with supervision from associated senior researchers and industry partners as required.

The project can be carried out:

-With a research group at Newcastle University

-With an industrial sponsor

-With a research institute

-At your place of work

Placements

Students have a unique opportunity to complete a work placement with one of our industrial partners as part of their projects.

Previous students have found placements with organisations including:

-NHS Business Services Authority

-Waterstons

-Metropolitan Police

-Accenture

-IBM

-Network Rail

-Nissan

-GSK

Accreditation

We have a policy of seeking British Computer Society (BCS) accreditation for all of our degrees, so you can be assured that you will graduate with a degree that meets the standards set out by the IT industry. Studying a BCS-accredited degree provides the foundation for professional membership of the BCS on graduation and is the first step to becoming a chartered IT professional.

The School of Computing Science at Newcastle University is an accredited and a recognised Partner in the Network of Teaching Excellence in Computer Science.

Facilities

Facilities

You will have dedicated computing facilities in the School of Computing. You will have access to the latest tools for system analysis and development. For certain projects, special facilities for networking can be set up.

You will enjoy access to specialist IT facilities to support your studies, including:

  • a dedicated virtual Linux workstation
  • a dedicated virtual Windows workstation
  • high specification computers only for postgrduates
  • over 300 PC's running Windows, 120 just for postgraduates
  • over 300 Raspberry Pi devices 
  • high-performance supercomputers
  • the latest Windows operating system and development tools
  • 27" monitors with high resolution (2560X1440) display
  • high-capacity database servers
  • motion capture facilities
  • 3D printing facilities

You will have access to a Linux based website that you can customise with PHP hosting services.

We have moved to the new £58m purpose-built Urban Sciences Building. Our new building offers fantastic new facilities for our students and academic community. The building is part of Science Central, a £350 million project bringing together:

  • academia
  • the public sector
  • communities
  • business and industry.


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