• University of Derby Online Learning Featured Masters Courses
  • University of Leeds Featured Masters Courses
  • Jacobs University Bremen gGmbH Featured Masters Courses
  • Aberystwyth University Featured Masters Courses
  • University of Bristol Featured Masters Courses
  • Northumbria University Featured Masters Courses
  • University of Edinburgh Featured Masters Courses

Postgrad LIVE! Study Fair

Birmingham | Bristol | Sheffield | Liverpool | Edinburgh

Kingston University Featured Masters Courses
Durham University Featured Masters Courses
University of the West of England, Bristol Featured Masters Courses
FindA University Ltd Featured Masters Courses
University of the West of England, Bristol Featured Masters Courses
"statistical" AND "comput…×
0 miles

Masters Degrees (Statistical Computing)

We have 184 Masters Degrees (Statistical Computing)

  • "statistical" AND "computing" ×
  • clear all
Showing 1 to 15 of 184
Order by 
This is a part-time, modular based programme for health professionals who wish to strengthen their statistical skills and ability to analyse data. Read more
This is a part-time, modular based programme for health professionals who wish to strengthen their statistical skills and ability to analyse data.

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

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

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

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

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

Course aims

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

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

Programme details

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

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

Compulsory Modules

• Essential Medical Statistics
• Statistics for Clinical Trials

Optional Modules – 1

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

Optional Modules – 2

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

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

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

Read less
The two-year master’s programme Statistical Science for the Life and Behavioural Sciences provides you with a thorough introduction to the general philosophy and methodology of statistical modelling, data analysis and data science. Read more

The two-year master’s programme Statistical Science for the Life and Behavioural Sciences provides you with a thorough introduction to the general philosophy and methodology of statistical modelling, data analysis and data science.

What does this master’s programme entail?

The two-year master’s programme in Statistical Science provides you with a thorough introduction to the general philosophy and methodology of statistical modelling and data analysis. The programme consists of a core programme shared by all students, and specialisation specific courses, electives, an internship or research project and master’s thesis. You can specialise in either life and behavioural sciences, where the emphasis is on the application in multidisciplinary environments, or in data sciences where you focus more on data mining, pattern recognition and deep learning.

Read more about the Statistical Science for the Life and Behavioural Sciences programme.

Why study Statistical Sciences for the Life and Behavioural Sciences at Leiden University?

  • Each specialisation offers you a unique combination of knowledge and expertise. These allow for a thorough preparation for a career as a data scientist, researcher or statistician anywhere.
  • Job perspectives after graduation are great: statisticians and data scientists are highly sought after in various industries such as academia, marketing, banking, government, official statistics, healthcare, bioinformatics and more.
  • The Statistical Science programme is a collaborative effort. Four Leiden University Institutes closely collaborate with top research institutes such as Wageningen UR and VUMC, which means that your education is provided by experts in their respective fields.

Find more reasons to choose Statistical Science for the Life and Behavioural Sciencese at Leiden University.

Statistical Sciences for the Life and Behavioural Sciences: the right master’s programme for you?

The field of statistics, like other areas of applied mathematics, often attracts students who are interested in the analysis of patterns in data: developing, understanding, abstracting, and packaging analytical methods for general use in other subject areas. Statistics is also, by definition, an information science. Imaginative use of both computing power and new computing environments drives much current research - so an interest in computation and/or computer science can also be a start for a statistician. With the growing importance of data within our society, you’ll be highly in demand with a degree in Statistical Sciences.

Read more about the entry requirements for the Statistical Science programme.



Read less
The Probability and Statistics group (4 Professors, 3 Senior Lecturers and 10 Lecturers) in the School of Mathematics have a long-standing reputation… Read more

The Probability and Statistics group (4 Professors, 3 Senior Lecturers and 10 Lecturers) in the School of Mathematics have a long-standing reputation and experience of offering one year, high quality taught courses in areas of Statistics leading to the degree of MSc.These courses have aimed to offer a thorough professional training which prepare students to embark on statistical careers in a variety of areas. (There is a shortage of statisticians trained to postgraduate level in the UK and the employment prospects for such people remain good.)   They have also provided a very good foundation for further study at PhD level.

Our current MSc programme in Statistics allows students to take one of two different MSc degrees, depending on their interests and career aspirations. There is the main programme in Statistics and one associated pathway in Financial Statistics. Each is built around a common core of five modules and then students study an additional set of three specialist modules to make a total of eight in all. 

Coursework and assessment

There are two teaching semesters of 12 weeks each and approximately 15 weeks of project work. Assessment for the taught part is by exams and coursework. Following the successful completion of the taught part of the programme (worth a total of 120 credits) students are then expected to work on a dissertation from June to September which is worth a further 60 credits, making 180 credits in total. Information on the various topics and projects which will be available for dissertation are provided to the students in May from which they are invited to state their preferences.  

Course unit details

The taught part of the programme is divided into two 12-week semesters, each followed by a two-week period of examinations. This in turn is followed by a period of approximately 12 weeks of research work over the summer which is supervised by a member of the academic staff and ends with submission of the MSc dissertation in September. In the taught part of the course, full-time students attend weekly lectures and support classes for four modules (4 x 15 credits) in each semester. Students are also able to enrol on a part-time basis if they wish. In this case they study over a two year period and only take two modules per semester, with the dissertation being completed at the end of the second year. Details of the programme structure are given below.

Main MSc Statistics

  Semester One:

  • Linear Models & Nonparametric Regression
  • Statistical Computing
  • Statistical Inference
  • Multivariate Statistics

 Semester Two:

  • Generalized Linear Models & Survival Analysis
  • Longitudinal Data Analysis
  • Markov Chain Monte Carlo (MCMC)
  • Design and Analysis of Experiments

 This degree is accredited by the Royal Statistical Society.

Financial Statistics Pathway

This comprises a core of five modules which are common to the main programme, plus three specialist modules in financial statistics.

Semester One:

  • Linear Models & Nonparametric Regression
  • Statistical Computing
  • Statistical Modelling in Finance
  • Extreme Values and Financial Risk

 Semester Two:

  • Generalized Linear Models & Survival Analysis
  • Longitudinal Data Analysis
  • Markov Chain Monte Carlo (MCMC)
  • Time Series Analysis and Financial Forecasting

This degree is also accredited by the Royal Statistical Society.

Accreditation by the Royal Statistical Society (RSS) provides reassurance that our MSc programme produces graduates with the technical skills and subject knowledge required of a statistician. This provides our graduates with a competitive edge in the job market and provides employers with an assurance of quality of our degree.

Dissertation  Following the successful completion of the taught part of the programme (worth a total of 120 credits) students are then expected to work on a dissertation from June to September which is worth a further 60 credits, making 180 credits in total. Information on the various topics and projects which will be available for dissertation are provided to the students in May from which they are invited to state their preferences.  

Facilities

The School of Mathematics is the largest in the UK with an outstanding research reputation andfacilities .

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: 

Career opportunities

These programmes will prepare students for a broad range of statistical careers, particularly in the financial, medical, pharmaceutical and industrial sectors of the economy, but also with local and national government agencies, as well as in other areas. They will also provide an excellent foundation for students wishing to pursue advanced postgraduate research in statistics.



Read less
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.



Read less
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.



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

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

Programme structure

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

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

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

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

Careers

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

Read less
In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. Read more

In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. The recent growth of data science has increased the awareness of the importance of statistics, with the analysis of data and interpretation of the results firmly embedded within this newly recognised field.

This programme is designed to train the next generation of statisticians with a focus on the newly recognised field of data science. The syllabus combines rigorous statistical theory with wider hands-on practical experience of applying statistical models to data. In particular the programme includes:

  • classical and Bayesian ideologies
  • computational statistics
  • regression
  • data analysis of a range of models and applications

Graduates will be in high demand. It is anticipated that the majority of students will be employed as statisticians within private and public institutions providing statistical advice/consultancy.

Programme structure

To be awarded the MSc degree you need to obtain a total of 180 credits. All students take courses during semester 1 and 2 to the value of 120 credits which will be a combination of compulsory and optional courses. Successful performance in these courses (assessed via coursework or examinations or both) permits you to start work on your dissertation (60 credits) for the award of the MSc degree. The standard dissertation will take the form of two consultancy-style case projects in different application areas.

The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.

Previous compulsory courses for 2017-18:

  • Bayesian Data Analysis
  • Bayesian Theory
  • Generalised Regression Models
  • Incomplete Data Analysis
  • Statistical Programming
  • Statistical Research Skills

Previous optional courses for 2017-18 include:

  • The Analysis of Survival Data
  • Biomedical Data Science
  • Credit Scoring
  • Fundamentals of Operational Research
  • Fundamentals of Optimization
  • Genetic Epidemiology
  • Large Scale Optimization for Data Science
  • Machine Learning and Pattern Recognition
  • Machine Learning Practical
  • Nonparametric Regression Models
  • Object-Oriented Programming with Applications
  • Probabilistic Modelling and Reasoning
  • Python Programming
  • Scientific Computing
  • Statistical Consultancy
  • Statistical Methodology
  • Stochastic Modelling
  • Time Series

Learning outcomes

At the end of this programme you will have:

  • knowledge and understanding of statistical theory and its applications within data science
  • the ability to formulate suitable statistical models for new problems, fit these models to real data and correctly interpret the results
  • the ability to assess the validity of statistical models and their associated limitations
  • practical experience of implementing a range of computational techniques using statistical software R and BUGS/JAGS

Career opportunities

Trained statisticians are in high demand both in public and private institutions. This programme will provide graduates with the necessary statistical skills, able to handle and analyse different forms of data, interpret the results and effectively communicate the conclusions obtained.

Graduates will have a deep knowledge of the underlying statistical principles coupled with practical experience of implementing the statistical techniques using standard software across a range of application areas, ensuring they are ideally placed for a range of different job opportunities.

The degree is also excellent preparation for further study in statistics or data science.



Read less
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/

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

Degree information

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

Students undertake modules to the value of 180 credits.

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

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

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

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

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

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

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

Careers

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

Why study this degree at UCL?

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

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

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

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

About the course

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

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

Aims

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

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

Course Content

Compulsory modules:

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

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

Teaching

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

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

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

Assessment

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

Special Features

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

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

Read less
Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. Read more

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

About this degree

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

Students undertake modules to the value of 180 credits.

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

Core modules

  • Applied Machine Learning (15 credits)
  • Introduction to Machine Learning (15 credits)
  • Introduction to Statistical Data Science (15 credits)

Optional modules

Students must choose 30 credits from Group One options. For the remaining 45 credits, students may choose up to 30 credits from Group Two options or up to 45 credits from Electives.

Group One Options (30 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Birkbeck College: Cloud Computing (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)
  • Web Economics (15 credits)

Group Two Options (up to 30 credits)

  • Applied Bayesian Methods (15 credits)
  • Decision and Risk (15 credits)
  • Forecasting (15 credits)
  • Statistical Design of Investigations (15 credits)

Electives (up to 45 credits)

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Bioinformatics (15 credits)
  • Computational Modelling for Biomedical Imaging (15 credits)
  • Graphical Models (15 credits)
  • Stochastic Systems (15 credits)
  • Supervised Learning (15 credits)

Please note: the availability and delivery of modules may vary, based on your selected options.

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

Dissertation/report

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

Teaching and learning

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

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

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. This is a very new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:

  • Google Deepmind
  • Microsoft Research
  • Dunnhumby
  • Index Ventures
  • Cisco
  • Deutsche Bank
  • IBM
  • Morgan Stanley

Employability

Students gain a thorough understanding of the fundamentals required from the best practitioners, and the programme's broad base enables data scientists to adapt to rapidly evolving goals.

Why study this degree at UCL?

UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).

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

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

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.



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

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

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

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

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

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

Intercalating this course

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

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

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

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

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

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

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

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

Objectives

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

- select appropriate study designs to address questions of medical relevance

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

- use various software packages for statistical analysis and data management

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

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

- explore current and anticipated developments in medical statistics

Structure

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

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

*Recommended modules

- Slot 1:
Generalised Linear Models (compulsory)

- Slot 2:
Statistical Methods in Epidemiology (compulsory)

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

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

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

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

Project Report

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

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

Read less
This course has a strong theoretical and methodological component to supplement a focus on applications of statistics to real life scientific problems. Read more

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

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

You will study a range of modules as part of your course, some examples of which are listed below.

Core

◾Bayesian Inference

◾Statistics in Practice

◾Likelihood Inference

◾Generalised Linear Models

◾Computational Intensive Methods II

◾Masters Dissertation

Optional

◾Genomics: technologies and data analysis

◾Extreme Value Theory

◾Clinical Trials

◾Principles of Epidemiology

◾Longitudinal Data Analysis

◾Pharmacological Modelling

◾Survival and Event History Analysis

◾Environmental Epidemiology



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

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

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

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

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

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

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

Read less
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



Read less

Show 10 15 30 per page



Cookie Policy    X