An MSc is generally accepted as being highly desirable for starting and developing a career in Medical Statistics. The MSc in Statistics with Applications in Medicine is also an excellent preparation for embarking on a PhD project in Statistics or Medical Statistics.
The MSc in Statistics with Applications in Medicine, taught by one of the largest and strongest Statistics groups in the UK, will provide you with a sound Masters-level training in Statistical methodology, with an emphasis on practical problems arising in the context of collecting and analysing Medical data. Several modules are delivered by Medical Statisticians, who can provide data and case studies from their own day to day work at Southampton General Hospital and the Medical Research Council Lifecourse Epidemiology Unit.
While studying for your degree, you will develop key transferrable skills, such as written and oral communication, the use of and some programming in Statistical software, time management, and basic research skills.
Programme objectives are:
Past graduates have joined major pharmaceutical companies, research teams at the Medical Research Council, university-based medical research units, contract research organisations, government, the financial sector, or have continued with further study to become successful PhD students.
The ever-increasing amount and range of patient data presents the pharmaceutical industry and medical research institutions with significant challenges and great opportunities. Medical Statisticians design and analyse clinical trials for new treatments; they help to identify the genes responsible for disease and they developing methodology to enable advances in personalised medicine.
Their work underpins scientific breakthroughs that will be life-saving for many. In the MSc in Statistics with Applications in Medicine you will examine new developments in challenging medical data problems through the study of clinical trials, statistical genetics and epidemiological methods.
The full-time MSc is completed over a 12-month period. There are two semesters of taught material, which account for 60 ECTS credits, followed by the MSc project in summer, which accounts for 30 ECTS credits.
The programme structure allows you to select options ranging from the more theoretical aspects of Statistics, including a module on research topics, to those which cover material focussed on practical applications of Statistics in a clinical setting. This is complemented by modules on research skills, a Medical Statistics seminar series providing insight into the role of Medical Statisticians in various different careers (which also gives opportunities for networking with the speakers), and several presentations on transferrable skills by the University Careers and Employability Service Team.
In addition to being a science in its own right, mathematics plays a fundamental role in the quantitative areas of practically all other academic disciplines, particularly in the natural sciences, engineering, business administration, economics, medicine and psychology. Mathematical results permeate nearly all facets of life and are a necessary prerequisite for the vast majority of modern technologies – and as our IT systems become increasingly powerful, we are able to mathematically handle enormous amounts of data and solve ever more complex problems.
Special emphasis is placed on developing students' ability to formalise given problems in a way that facilitates algorithmic processing as well as enabling them to choose or develop, and subsequently apply, suitable algorithms to solve problems in an appropriate manner. The degree programme is theoretical in its orientation, with strongly application-oriented components. Studying this programme, you can gain advanced knowledge in the mathematical areas of Cryptography, Computer Algebra, Algorithmic Algebra and Geometry, Image and Signals Processing, Statistics and Stochastic Simulation, Dynamical Systems and Control Theory as well as expert knowledge in Computer Science fields such as Data Management, Machine Learning and Data Mining.
Furthermore, you will have the chance to learn how to apply your knowledge to tackle problems in areas as diverse as Marketing, Predictive Analytics, Computational Finance, Digital Humanities, IT Security and Robotics.
The core modules consist of two mathematics seminars and the presentation of your master's thesis.The compulsory elective modules are divided into eight module groups:
1) Algebra, Geometry and Cryptography
This module group imparts advanced results in the areas of algebra and geometry, which constitute the fundament for algorithmic calculations, particularly in cryptography but also in many other mathematical areas.
2) Mathematical Logic and Discrete Mathematics
The theoretical possibilities and limitations of algorithm-based solutions are treated in this module group.
3) Analysis, Numerics and Approximation Theory
Methods from the fields of mathematical analysis, applied harmonic analysis and approximation theory for modelling and approximating continuous and discrete data and systems as well as efficient numerical implementation and evaluation of these methods are the scope of this module group.
4) Dynamical Systems and Optimisation
Dynamical systems theory deals with the description of change over time. This module group is concerned with methods used for the modelling, analysis, optimisation and design of dynamical systems, as well as the numerical implementation of such techniques.
5) Stochastics, Statistics
This module group deals with methods for modelling and analysing complex random phenomena as well as the construction, analysis and optimisation of stochastic algorithms and techniques used in statistical data analysis.
6) Data Analysis and Data Management and Programming
This module group examines the core methods used in computer science for the analysis of data of heterogeneous modalities (e.g. multimedia data, social networks and sensor data) and for the realisation of data analysis systems.
In this module group, you will practise applying the mathematical methods learned in module groups 1 to 6 to real-world applications such as Marketing, Predictive Analytics and Computational Finance.
8) Key Competencies and Language Training
In this module group, you will choose seminars that develop your non-subject-specific skills, such as public speaking and academic writing and other soft skills; you may also undertake internships. This serves to complement your technical expertise gained during your degree studies and helps to prepare you for your professional life after university.
Life Sciences is one of the strategic research fields at the University of Helsinki. The multidisciplinary Master’s Programme in Life Science Informatics (LSI) integrates research excellence and research infrastructures in the Helsinki Institute of Life Sciences (HiLIFE).
The Master's Programme is offered by the Faculty of Science. Teaching is offered in co-operation with the Faculty of Medicine and the Faculty of Biological and Environmental Sciences. As a student, you will gain access to active research communities on three campuses: Kumpula, Viikki, and Meilahti. The unique combination of study opportunities tailored from the offering of the three campuses provides an attractive educational profile. The LSI programme is designed for students with a background in mathematics, computer science and statistics, as well as for students with these disciplines as a minor in their bachelor’s degree, with their major being, for example, ecology, evolutionary biology or genetics. As a graduate of the LSI programme you will:
Further information about the studies on the Master's programme website.
The Life Science Informatics Master’s Programme has six specialisation areas, each anchored in its own research group or groups.
Algorithmic bioinformatics with the Genome-scale algorithmics, Combinatorial Pattern Matching, and Practical Algorithms and Data Structures on Strings research groups. This specialisation area educates you to be an algorithm expert who can turn biological questions into appropriate challenges for computational data analysis. In addition to the tailored algorithm studies for analysing molecular biology measurement data, the curriculum includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science and Data Science.
Applied bioinformatics, jointly with The Institute of Biotechnology and genetics.Bioinformatics has become an integral part of biological research, where innovative computational approaches are often required to achieve high-impact findings in an increasingly data-dense environment. Studies in applied bioinformatics prepare you for a post as a bioinformatics expert in a genomics research lab, working with processing, analysing and interpreting Next-Generation Sequencing (NGS) data, and working with integrated analysis of genomic and other biological data, and population genetics.
Biomathematics with the Biomathematics research group, focusing on mathematical modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of topics ranging from problems at the molecular level to the structure of populations. To tackle these problems, the research group uses a variety of modelling approaches, most importantly ordinary and partial differential equations, integral equations and stochastic processes. A successful analysis of the models requires the study of pure research in, for instance, the theory of infinite dimensional dynamical systems; such research is also carried out by the group.
Biostatistics and bioinformatics is offered jointly by the statistics curriculum, the Master´s Programme in Mathematics and Statistics and the research groups Statistical and Translational Genetics, Computational Genomics and Computational Systems Medicine in FIMM. Topics and themes include statistical, especially Bayesian methodologies for the life sciences, with research focusing on modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of collaborative topics in various biomedical disciplines. In particular, research and teaching address questions of population genetics, phylogenetic inference, genome-wide association studies and epidemiology of complex diseases.
Eco-evolutionary Informatics with ecology and evolutionary biology, in which several researchers and teachers have a background in mathematics, statistics and computer science. Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Evolutionary biology studies processes supporting biodiversity on different levels from genes to populations and ecosystems. These sciences have a key role in responding to global environmental challenges. Mathematical and statistical modelling, computer science and bioinformatics have an important role in research and teaching.
Systems biology and medicine with the Genome-scale Biology Research Program in Biomedicum. The focus is to understand and find effective means to overcome drug resistance in cancers. The approach is to use systems biology, i.e., integration of large and complex molecular and clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets. Particular interest is focused on developing and applying machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information.
The Modelling Biological Complexity MRes is designed for students who wish to develop the skills to apply mathematical, computational and physical science techniques to real biological problems. The programme provides a broad overview of the cutting edge research at the interface of the life, mathematical and physical sciences.
Foundation courses use innovative teaching methods for interdisciplinary research to provide essential background knowledge in mathematical, computational and physical techniques and a broad introduction to core biological concepts and systems. A range of interdisciplinary research-driven projects follow in which students gain experience of different research techniques and a range of areas of biological interest.
Students undertake modules to the value of 180 credits.
The programme consists of four compulsory modules: foundation courses module, transferable skills module (20%), three mini projects (40%) and a research (summer) project (40%).
There are no optional modules for this programme.
All students undertake an independent research (summer) project, which culminates in a dissertation of up to 15,000 words, a short presentation and an oral examination.
Teaching and learning
The programme is delivered through a combination of lectures, laboratory work, case presentations, seminars, tutorials and project work. Student performance is assessed by essays, mini projects, oral and poster presentations, a computer programming and biological database task, web development, the research project and an end-of-year viva.
Further information on modules and degree structure is available on the department website: Modelling Biological Complexity MRes
After passing the MRes, students may have the opportunity to progress onto a PhD at UCL.
CoMPLEX has built upon relationships with partners within academia and industry, to develop our existing CoMPLEX programme. so that it continues to be designed specifically to provide training that meets market needs. Graduates have excellent publication outputs, this, together with CoMPLEX's international reputation means that graduates are and will continue to be recognised when entering the job market. 70% of recent graduates have taken up positions in research centres in the UK and abroad. As small number have pursued careers in science policy analysis, cyber security, science teaching, statistical and mathematical consultancy, technology consultancy, or in management and the financial sector.
CoMPLEX is UCL's centre for interdisciplinary research in the life sciences. It brings together life and medical scientists with computer scientists, mathematicians, physicists and engineers to tackle the challenges arising from complexity in biology and medicine.
CoMPLEX collaborates with 250+ supervisors from 40 UCL Departments and maintains strong links with leading UK/International research institutions, charities and industrial partners e.g. AstraZeneca, British Heart Foundation, CRUK, Francis Crick Institute, GlaxoSmithKline, Microsoft Research and Renishaw. As a result CoMPLEX students have a vast range of projects to choose from and the opportunity to network with a plethora of scientific partners.
Peer-to-peer learning is a crucial part of the training, and students will take part in cohort activities, such as, mentoring events, a seminar series, outreach groups and an annual retreat.
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.
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
Our MSc Model-based Drug Development course provides the knowledge and skills for making evidence-based decisions at various stages of drug development.
It covers the scientific and regulatory aspects of evaluating a drug, with emphasis on the use of modelling and simulation methods. You will learn why these methods are so highly valued by industry and regulatory authorities as effective, cost-saving, decision-making tools. Learning is reinforced via hands-on application of the skills to real data.
The course has been developed with an emphasis on mechanistic approaches to assessing and predicting pharmacokinetics and pharmacodynamics (PKPD), such as physiologically-based pharmacokinetics (PBPK) .
As this comes under the general umbrella of systems biology, you will be able to apply your knowledge of modelling and simulation in various areas of research within the pharmaceutical industry.
Full-time students benefit from immersion in the varied biomedical research environment at The University of Manchester, including interaction with research staff at the renowned Centre for Applied Pharmacokinetic Research .
Alternatively, part-time students already working in the pharmaceutical industry can take advantage of the flexible, distance learning mode of the course, which allows you to fit study around other commitments.
The aim of the course is to provide specialist knowledge and skills that are highly relevant for a career linked to drug development and pharmaceutical industry.
It is designed for science, engineering or mathematics graduates who want to acquire:
The course aims to:
Distance learning option
Our distance learning option is ideal for scientists linked to the pharmaceutical industry who wish to expand their expertise while working in the industry.
The full-time mode allows suitably trained mathematics, science or engineering graduates to focus on obtaining the advanced skills needed for a career in this area. We utilise a blended learning approach in which online learning content is supported by regular face-to-face contact with tutors.
Your learning will be reinforced over the duration of the course via hands-on application of your skills to real data.
The course focuses on the following topics.
The course emphasises the development of problem-solving skills. A large portion of the learning involves structured problems requiring you to apply theory and practical skills to solve typical problems that arise in drug development.
The following teaching and learning methods are used throughout the course:
We assess your achievement of the learning outcomes for this course through:
This course was originally developed for scientists working within the pharmaceutical industry who wished to qualify as modellers with hands-on experience. The qualification will enhance your abilities within your current role or provide you with skills to progress into new posts.
The course is also appropriate for science and engineering graduates who wish to enter the industry. The role of modelling and simulation or pharmacometrics is assuming greater and greater importance in the pharmaceutical industry.
Pharmaceutical companies and government regulatory agencies are recognising its value in making best use of laboratory and clinical data, guiding and expediting development and saving time and costs.
A range of well-paid jobs exist in this area across the globe. Scientific and industry publications often discuss the current shortage and growing need for modellers.