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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.

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 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.

Degree information

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 two core modules (30 credits), three optional modules (45 credits) and a dissertation/report (105 credits).

Core modules
-Investigating Research
-Researcher Professional Development

Optional modules - students select three modules from the following:
-Advanced Topics in Machine Learning
-Statistical Inference
-Applied Bayesian Methods
-Approximate Inference and Learning in Probabilistic Models
-Graphical Models
-Information Retrieval and Data Mining
-Inverse Problems in Imaging
-Machine Vision
-Probabilistic and Unsupervised Learning
-Statistical Computing
-Statistical Inference
-Statistical Models and Data Analysis
-Supervised Learning
-Selected Topics in Statistics

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.

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 coordinated the PASCAL European Network of Excellence.

UCL CSML is a major European centre for machine learning, having organised 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.

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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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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. Students on the taught master’s programme will take optional courses to the value of two units, while those on the research track will substitute one unit with a dissertation.

Graduate destinations

The programme will prepare graduates for work within the public sector, market research organisations and survey research organisations, or for further study.

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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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When you study mathematics and statistics at the University of Helsinki, some of the best mathematicians and statisticians in the world will be your instructors. Read more
When you study mathematics and statistics at the University of Helsinki, some of the best mathematicians and statisticians in the world will be your instructors. Studies in this Master’s programme will give you a solid basis for maths and statistics applications. Graduates of this Master’s programme find employment as researchers, teachers, and in demanding expert posts in the public and private sectors in Finland and abroad.

The Master’s programme in mathematics and statistics is based on top research. The teaching within the sub-programmes at the University of Helsinki follows a high standard and is highly valued, not just within Finnish academia but in global university rankings. Upon graduating from this Master’s programme, you will:
-Be an expert in the methods of mathematics or statistics.
-Have mastered the basics of another scientific discipline.
-Be able to apply scientific knowledge and methods.
-Be able to follow developments in mathematics and statistics.
-Know how to think critically, argue a point, and solve problems.
-Have excellent interaction skills and be assertive and creative.
-Understand the principles of ethical and sustainable development.
-Be well prepared to work as an expert and developer in your field.
-Be prepared for scientific postgraduate studies.

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

Programme Contents

The Master’s programme consists of courses in mathematics, applied mathematics, and statistics. The courses include group and lecture instruction, exercises, literature, and workshops. Most courses also include exams or project assignments. In addition, you can complete some courses independently, by taking literature-based exams. The instructors in this programme have received prizes for their high standard of teaching. The teaching methods used in the subjects in this Master's programme have been widely recognised in the media.

Selection of the Major

The specialisation subjects within the programme are:
-Analysis
-Mathematical physics and stocastics
-Applied analysis
-Computational science
-Mathematical logic
-Mathematical modelling
-Insurance and financial mathematics
-Algebra and topology
-Statistics
-European Master in Official Statistics, EMOS (based on the statistics education in the Faculty of Social Sciences).

You will select your specialisation subject during your first year.

Programme Structure

The Master’s programme comprises 120 credits, which you can complete in two years. The degree in mathematics includes:
-85 credits of advanced courses, including the Master’s thesis (Pro gradu, 30 credits).
-35 credits of other courses from your own or other programmes.
-Working-life orientation and career planning.
-Personal study plan.

The degree in statistics includes:
-25 credits of advanced mathematics courses.
-60 credits of advanced statistics courses, including the Master’s thesis (Pro gradu, 30 credits).
-35 credits of other courses e.g. more advanced courses in statistics, or intermediate courses in some other subject, in which you included basic courses in your BSc degree, or, module/s from other university programmes.
-Working-life orientation and career planning.
-Personal study plan.

The European Master in Official Statistics sub-programme includes:
-85 credits of advanced courses in statistics or mathematics, including the Master’s thesis (Pro gradu, 30 credits) and a traineeship.
-35 credits of other courses from your own or other programmes.
-Working-life orientation and career planning.
-Personal study plan.

Career Prospects

Graduates of the Master’s programme can find employment outside the university or continue with one of the doctoral programmes in mathematics and statistics. The Master’s programme will give you excellent capabilities for work in the public or private sector as an expert in mathematics and statistics, skills that are very sought after in the job market both in Finland and abroad. The banking, investment, and insurance fields, for instance, value mathematicians and statisticians very highly internationally. Many of our graduates work in research and development or as teachers in various educational institutions. Graduates from this programme have excellent chances to find employment corresponding to their education.

Internationalization

The international nature of the programme is implemented in many ways:
-Research within the disciplines of the degree programme is of high international standard and is highly regarded.
-Teaching staff and research collaboration within the programme are international.
-The atmosphere of the programme is international, several international students are admitted each year.
-Theses and projects may be completed within international projects.
-There are opportunities for a student exchange period in many foreign universities.

Research Focus

The research focus within the disciplines in the degree programme are e.g.
-Geometric analysis and measurement theory
-Analysis in metric spaces
-Partial differential equations
-Functional analysis
-Harmonic analysis
-Mathematical physics
-Stochastics
-Inversion problems
-Mathematical logic and set theory
-Biomathematics
-Time series analysis
-Biometry
-Econometry
-Psychometrics
-Social statistics

The programme is part of the Analyysin ja dynamiikan (Analysis and dynamics) and the Inversio-ongelmien (Inversion problems) centres of excellence.

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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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This programme qualifies for the prestigious Data Lab Masters Scholarships. The award covers full tuition fee costs. For further information please see. Read more
This programme qualifies for the prestigious Data Lab Masters Scholarships. The award covers full tuition fee costs. For further information please see: Data Lab Masters Scholarships.

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 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.

Degree information

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 five core modules (90 credits), two optional modules (30 credits) and a dissertation (60 credits).

Core modules
-Programming for Business Analytics
-Data Analytics
-Information Retrieval and Data Mining
-Introduction to Supervised Learning
-Statistical NLP

Please note: the availability and delivery of modules may vary.

Optional modules
-Applied Machine Learning
-Graphical Models
-Web Economics
-Statistical Models and Data Analysis
-Statistical Design of Investigations
-Decision and Risk
-Consumer Behaviour and Behavioural Change
-Consulting Psychology
-Talent Management
-Data Science for Spatial Systems
-Group Mini Project: Digital Visualisation
-Urban Simulation
-Mastering Entrepreneurship
-Decision and Risk Analysis
-Managing Hi-Tech Organisations

Please note: the availability and delivery of modules may vary.

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.

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: 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 2014 Research Excellence Framework (REF) ranked the department as first in the UK for research, with 96% regarded as 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.

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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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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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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.

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The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods. Read more

Programme description

The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.

Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.

This MSc programme delivers:

a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
a solid knowledge in financial derivative pricing, risk management and portfolio management
the transferable computational skills required by the modern quantitative finance world

Programme structure

You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.

There are two streams: the Financial stream and the Computational stream.

Compulsory courses (both streams):

Stochastic Analysis in Finance (20 credits, semester 1)
Discrete-Time Finance (10 credits, semester 1)
Finance, Risk and Uncertainty (10 credits, semester 1)
Object-Oriented Programming with Applications (10 credits, semester 1)
Risk-Neutral Asset Pricing (10 credits, semester 2)
Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
Monte Carlo Methods (5 credits, semester 2)
Research-Linked Topics (10 credits, semesters 1 and 2)

Optional courses - Computational stream:

Numerical Methods for Stochastic Differential Equations [compulsory] (5 credits, semester 2)
Numerical Partial Differential Equations [compulsory] (10 credits, semester 2)
Programming Skills - HPC MSc (10 credits, semester 1)
Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)

Optional courses - Financial stream:

Financial Risk Theory [compulsory] (10 credits, semester 2)
Optimization Methods in Finance [compulsory] (10 credits, semester 2)
Advanced Time Series Econometrics (10 credits, semester 2)
Credit Scoring (10 credits, semester 2)
Computing for Operational Research and Finance (10 credits, semester 1)
Financial Risk Management (10 credits, semester 2)
Stochastic Optimization (5 credits, semester 2)

Learning outcomes

At the end of this programme you will have:

developed personal communications skills, initiative, and professionalism within a mathematical context
developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.

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