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Masters Degrees (Statistical Science)

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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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Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Read more
Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Data scientists help organisations make sense of their data. As data is collected and analysed in all areas of society, demand for professional data scientists is high and will grow higher. The emerging Internet of Things, for instance, will produce a whole new range of problems and opportunities in data analysis.

In the Data Science master’s programme, you will gain a solid understanding of the methods used in data science. You will learn not only to apply data science: you will acquire insight into how and why methods work so you will be able to construct solutions to new challenges in data science. In the Data Science master’s programme, you will also be able to work on problems specific to a scientific discipline and to combine domain knowledge with the latest data analysis methods and tools. The teachers of the programme are themselves active data science researchers, and the programme is heavily based on first-hand research experience.

Upon graduating from the Data Science MSc programme, you will have solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills. In particular, you will be able to:
-Understand the general computational and probabilistic principles underlying modern machine learning and data mining algorithms.
-Apply various computational and statistical methods to analyse scientific and business data.
-Assess the suitability of each method for the purpose of data collection and use.
-Implement state-of-the-art machine learning solutions efficiently using high-performance computing platforms.
-Undertake creative work, making systematic use of investigation or experimentation, to discover new knowledge.
-Report results in a clear and understandable manner.
-Analyse scientific and industrial data to devise new applications and support decision making.

The MSc programme is offered jointly by the Department of Computer Science, the Department of Mathematics and Statistics, and the Department of Physics, with support from the Helsinki Institute for Information Technology (HIIT) and the Helsinki Institute of Physics (HIP), all located on the Kumpula Science campus. In your applied data science studies you can also include multidisciplinary studies from other master's programmes, such as digital humanities, and natural and medical sciences.

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: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

Programme Contents

The Data Science MSc programme combines elements from computer science and mathematical sciences to provide you with skills in topics such as machine learning, distributed systems and statistical methods. You might also find that knowledge in a particular scientific field is useful for your future career. You can obtain this through minor studies in the MSc programme, or it might already be part of your bachelor-level degree.

Studies in the Data Science MSc programme include both theoretical and practical components, including a variety of study methods (lectures, exercises, projects, seminars; done both individually and in groups). Especially in applied data science, we also use problem-based learning methods, so that you can address real-world issues. You will also practise academic skills such as scientific writing and oral presentation throughout your studies. You are encouraged to include an internship in your degree in order to obtain practical experience in the field.

Minor studies give you a wider perspective of Data Science. Your minor subject can be an application area of Data Science (such as physics or the humanities), a discipline that supports application of Data Science (such as language technology), or a methodological subject needed for the development of new Data Science methods and models (such as computer science, statistics, or mathematics).

Selection of the Major

You can specialise either in the core areas of data science -- algorithms, infrastructure and statistics -- or in its applications. This means that you can focus on the development of new models and methods in data science, supported by the data science research carried out at the University of Helsinki; or you can become a data science specialist in an application field by incorporating studies in another subject. In addition to mainstream data science topics, the programme offers two largely unique opportunities for specialisation: the data science computing environment and infrastructure, and data science in natural sciences, especially physics.

Programme Structure

You should be able to complete the MSc Programme in Data Science of 120 credits (ECTS) in two years of full-time study. The programme consists of:
-Common core studies of basic data science courses.
-Several modules on specific topics within data science algorithms, data science infrastructures and statistical data science, and on data science tools.
-Seminars and colloquia.
-Courses on academic skills and tools.
-Possibly an internship in a research group or company.
-Studies in an application domain.
-Master’s thesis (30 credits).

Career Prospects

Industry and science are flooded with data and are struggling to make sense of it. There is urgent demand for individuals trained to analyse data, including massive and heterogeneous data. For this reason, the opportunities are expected to grow dramatically. The interdisciplinary Data Science MSc programme will train you to work in data-intensive areas of industry and science, with the skills and knowledge needed to construct solutions to complex data analysis problems.

If you are focusing on the core areas of data science, you will typically find employment as a researcher or consultant, sometimes after taking a PhD in Computer Science or Statistics to deepen your knowledge of the field and research methods. If your focus is on the use of data science for specific applications, you will typically find work in industry or in other fields of science such as physics, digital humanities, biology or medicine.

Internationalization

The Data Science MSc is an international programme, with students from around the world and an international research environment. All of the departments taking part in the programme are internationally recognised for their research and a significant fraction of the teaching and research staff come from abroad.

The departments participate in international student exchange programmes and offer you the chance to include international experience as part of your degree. Data Science itself is an international field, so once you graduate you can apply for jobs in any country.

In the programme, all courses are in English. Although the Helsinki area is quite cosmopolitan and English is widely spoken, you can also take courses to learn Finnish at the University of Helsinki Language Centre. The Language Centre also offers an extensive programme of foreign language courses for those interested in learning other languages.

Research Focus

The MSc programme in Data Science is offered jointly by three departments and two research institutes. Their research covers a wide spectrum of the many aspects of data science. At a very general level, the focal areas are:
-Machine learning and data mining
-Distributed computation and computational infrastructures
-Statistical modelling and analysis
-Studies in the programme are tightly connected to research carried out in the participating departments and institutes.

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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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Medical statistics is a fundamental scientific component of health research. Medical statisticians interact with biomedical researchers, epidemiologists and public health professionals and contribute to the effective translation of scientific research into patient benefits and clinical decision-making. Read more
Medical statistics is a fundamental scientific component of health research. Medical statisticians interact with biomedical researchers, epidemiologists and public health professionals and contribute to the effective translation of scientific research into patient benefits and clinical decision-making. As new biomedical problems emerge, there are exciting challenges in the application of existing tools and the development of new superior models.

Degree information

The UCL Medical Statistics degree provides students with a sound background in theoretical statistics as well as practical hands-on experience in designing, analysing and interpreting health studies, including trials and observational studies. The taught component equips students with analytical tools for health care economic evaluation, and the research project provides experience in using real clinical datasets.

Students undertake modules to the value of 180 credits.

The programme consists of a foundation course, six core modules (90 credits) two optional modules (30 credits) and the research dissertation (60 credits).

Core modules
-Foundation Course (not credit bearing)
-Statistical Inference
-Statistical Models and Data Analysis
-Medical Statistics I
-Medical Statistics II
-Statistical Computing
-Applied Bayesian Methods

Optional modules - at least one from:
-Statistics for Interpreting Genetic Data
-Bayesian Methods in Health Economics

and at least one from:
-Epidemiology
-Statistical Design of Investigations

Dissertation/report
All MSc students undertake an individual 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, for example, the presentation of statistical graphs and tables.

Careers

Medical statisticians enable the application of the best possible quantitative methods in health research and assist in the reliable translation of research findings to public and patients’ health care.

The National Institute of Health Research (NIHR) has identified Medical Statistics as one of the priority areas in their capacity building strategy and has awarded UCL two studentships annually for this MSc.

Top career destinations for this degree:
-Graduate Bio-Statistician, PRA International
-Statistical and Epidemiological Modeller, University of Oxford
-Biostatistician, Boehringer Ingelheim
-PhD Statistical Science, University College London (UCL)

Employability
There is an acute shortage of medical statisticians in the UK and employment opportunities are excellent. Recent graduates from this programme have been employed by clinical trials units, pharmaceutical industry, NHS trusts and Universities (e.g. London School of Hygiene and Tropical Medicine, UCL).

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.

UCL is linked with four NHS hospital trusts and hosts three biomedical research centres, four clinical trial units and an Institute of Clinical Trials and Methodology. Established links between the Department of Statistical Science, the NIHR UCLH/UCL Biomedical Research Centre and the Clinical Trial Units provide high-quality biomedical projects for Master's students and opportunities for excellent postgraduate teaching and medical research.

The programme 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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Environmental earth science (or geoscience) covers a range of topics including hydrology, sedimentology and geomorphology. This course provides specialist skills and knowledge for science graduates wanting to pursue careers in environmental earth science. Read more

What is environmental earth science?

Environmental earth science (or geoscience) covers a range of topics including hydrology, sedimentology and geomorphology.

Who is this course for?

This course provides specialist skills and knowledge for science graduates wanting to pursue careers in environmental earth science. Environmental scientists undertake work such as developing ways to minimise the impacts of humans on the natural environment.

Course learning outcomes

The graduates of James Cook University are prepared and equipped to create a brighter future for life in the tropics world-wide.
JCU graduates are committed to lifelong learning, intellectual development, and to the display of exemplary personal, professional and ethical standards. They have a sense of their place in the tropics and are charged with professional, community, and environmental responsibility. JCU graduates appreciate the need to embrace and be acquainted with the Aboriginal and Torres Strait Islander Peoples of Australia. They are committed to reconciliation, diversity and sustainability. They exhibit a willingness to lead and to contribute to the intellectual, environmental, cultural, economic and social challenges of regional, national, and international communities of the tropics.
On successful completion of the Graduate Diploma of Science, graduates will be able to:
*Integrate and apply advanced theoretical and technical knowledge in one or more science disciplines
*Retrieve, analyse, synthesise and evaluate knowledge from a range of sources
*Plan and conduct reliable, evidence-based laboratory and/or field experiments/practices by selecting and applying methods, techniques and tools, as appropriate to one or more science disciplines
*Organise, analyse and interpret complex scientific data using mathematical, statistical and technological skills
*Communicate complex scientific ideas, arguments and conclusions clearly and coherently to a variety of audiences through advanced written and oral English language skills and a variety of media
*Identify, analyse and generate solutions to unpredictable or complex problems, especially related to tropical, rural, remote or Indigenous contexts, by applying scientific knowledge and skills with initiative and high level judgement
*Explain and apply regulatory requirements, ethical principles and, where appropriate, cultural frameworks, to work effectively, responsibly and safely in diverse contexts
*Reflect on current skills, knowledge and attitudes to manage their professional learning needs and performance, autonomously and in collaboration with others.

Award title

GRADUATE DIPLOMA OF SCIENCE (GDipSc)

Course articulation

Students who complete the Graduate Diploma of Science are eligible for entry to the Master of Science, and may be granted advanced standing for all subjects completed under the Graduate Diploma.

Entry requirements (Additional)

English band level 1 - the minimum English Language test scores you need are:
*Academic IELTS – 6.0 (no component lower than 5.5), OR
*TOEFL – 550 (plus minimum Test of Written English score of 4.0), OR
*TOEFL (internet based) – 79 (minimum writing score of 19), OR
*Pearson (PTE Academic) - 57

If you meet the academic requirements for a course, but not the minimum English requirements, you will be given the opportunity to take an English program to improve your skills in addition to an offer to study a degree at JCU. The JCU degree offer will be conditional upon the student gaining a certain grade in their English program. This combination of courses is called a packaged offer.
JCU’s English language provider is Union Institute of Languages (UIL). UIL have teaching centres on both the Townsville and Cairns campuses.

Minimum English language proficiency requirements

Applicants of non-English speaking backgrounds must meet the English language proficiency requirements of Band 1 – Schedule II of the JCU Admissions Policy.

Why JCU?

James Cook University brings together a team of academic and associate staff across multiple disciplines.
*Nationally-recognised leader in geoscience
*state-of-the-art research and teaching facilities
*internationally-acclaimed academic teaching staff
*strong collaboration with industry and research organisations, both locally and internationally.

Career Opportunities

A postgraduate qualification from JCU can enhance your career prospects, enable you to reskill and change careers completely, or develop a specialist area of expertise and personal interest.
Earth science and environmental science graduates enjoy well-paid careers in Australia and overseas. A range of opportunities await graduates in the academia as well as in private and public sectors.
As an Environ mental Scientist, for instance, you will measure and record features of the environment and study, assess and develop methods of controlling or minimizing the harmful effects of hum an activity on the environment.
Graduates can also get jobs as research assistants or support staff for teaching. With a PhD, you can gain research positions (Postdoctoral, Fellowships) that are often funded for a few years or apply for permanent positions as a lecturer and researcher.

Application deadlines

*1st February for commencement in semester one (February)
*1st July for commencement in semester two (mid-year/July)

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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

Programme description

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
linear and generalised linear models
computational statistics applied to a range of models and applications
regression
data analysis

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 of which compulsory course units comprise 60 credits. 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 dissertation will generally take the form of two consultancy-style case projects or an externally supervised project.

Compulsory courses (60 credits):

Statistical Theory (10 credits, semester 1)
Statistical Regression Models (10 credits, semester 1)
Bayesian Theory (10 credits, semester 1)
Statistical Programming (10 credits, semester 1)
Bayesian Data Analysis (10 credits, semester 2)
Likelihood and Generalised Linear Models (10 credits, semester 2)

Optional courses (60 credits) include:

Data Analysis (20 credits, semester 1)
Introductory Applied Machine Learning (10 credits, semester 1)
Text Technologies for Data Science (10 credits, semester 1)
Fundamentals of Optimization (10 credits, semester 1)
The Analysis of Survival Data (10 credits, semester 2)
Stochastic Modelling (10 credits, semester 2)
Multilevel Modelling (20 credits, semester 2)
Large Scale Optimization for Data Science (10 credits, semester 2)
Modern Optimization Methods for Big Data Problems (10 credits, semester 2)
Time Series Analysis and Forecasting (5 credits, semester 2)
Combinatorial Optimization (5 credits, semester 2)
Probabilistic Modelling and Reasoning (10 credits, semester 2)

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.

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The MSc Archaeological Science will provide you with a solid grounding in the theory and application of scientific principles and techniques within archaeology. Read more
The MSc Archaeological Science will provide you with a solid grounding in the theory and application of scientific principles and techniques within archaeology. The programme also develops critical, analytical and transferable skills that prepare you for professional, academic and research careers in the exciting and rapidly advancing area of archaeological science or in non-cognate fields.

The programme places the study of the human past at the centre of archaeological science enquiry. This is achieved through a combination of science and self-selected thematic or period-based modules allowing you to situate your scientific training within the archaeological context(s) of your choice. The programme provides a detailed understanding of the foundations of analytical techniques, delivers practical experience in their application and data processing, and the ability to design and communicate research that employs scientific analyses to address archaeological questions. Upon graduation you will have experience of collecting, analysing and reporting on data to publication standard and ideally equipped to launch your career as a practising archaeological scientist.

Distinctive features

The MSc Archaeological Science at Cardiff University gives you access to:

• A flexible and responsive programme that combines training in scientific enquiry, expertise and vocational skills with thematic and period-focused archaeology.

• Materials, equipment, library resources and funding to undertake meaningful research in partnership with a wide range of key heritage organisations across an international stage.

• A programme with core strengths in key fields of archaeological science, tailored to launch your career in the discipline or to progress to doctoral research.

• A department where the science, theory and practice of archaeology and conservation converge to create a unique environment for exploring the human past.

• Staff with extensive professional experience in researching, promoting, publishing, and integrating archaeological science across academic and commercial archaeology and the wider heritage sector.

• An energetic team responsible for insights into iconic sites (e.g. Stonehenge, Çatalhöyük), tackling key issues in human history (e.g. hunting, farming, food, and feasts) through the development and application of innovative science (e.g. isotopes, residue analysis, DNA, proteomics)

• A unique training in science communication at every level - from preparing conference presentations and journal articles, to project reports, press releases and public engagement, our training ensures you can transmit the excitement of scientific enquiry to diverse audiences.

• Support for your future career ambitions. From further study to science advisors to specialists – our graduates work across the entire spectrum of archaeological science as well as moving into other successful careers.

Structure

There are two stages to this course: stage 1 and stage 2.

Stage 1 is made up of:

• 40 credits of Core Skills and Discipline-Specific Research Training modules for Archaeology and Conservation Master's students
• A minimum of 40 credits of Archaeological Science modules
• An additional 40 credits of Archaeological Science or Archaeology modules offered to MA and MSc students across the Archaeology and Conservation department

Stage 2 comprises:

• 60 credit Archaeological Science Dissertation (16-20,000 words, topic or theme chosen in consultation with academic staff)

Core modules:

Postgraduate Skills in Archaeology and Conservation
Skills and Methods for Postgraduate Study
Archaeological Science Dissertation

Teaching

Teaching is delivered via lectures, laboratory sessions, interactive workshops and tutorials, in addition to visits to relevant local resources such as the National Museum Wales and local heritage organisations.

Lectures take a range of forms but generally provide a broad structure for each subject, an introduction to key concepts and relevant up-to-date information. The Archaeological Science Master's provides students with bespoke training in scientific techniques during laboratory sessions. This includes developing practical skills in the identification, recording and analysis of archaeological materials during hands on laboratory sessions. These range from macroscopic e.g. bone identification, to microscopic e.g. material identification or status with light based or scanning electron microscopy, to sample selection, preparation and analysis e.g. isotopic or aDNA and include health and safety and laboratory management skills. Students will be able to develop specialist practical skills in at least one area of study. In workshops and seminars, you will have the opportunity to discuss themes or topics, to receive and consolidate feedback on your individual learning and to develop skills in oral presentation.

This programme is based within the School of History, Archaeology and Religion and taught by academic staff from across Cardiff University and by external speakers. All taught modules within the Programme are compulsory and you are expected to attend all lectures, laboratory sessions and other timetabled sessions. Students will receive supervision to help them complete the dissertation, but are also expected to engage in considerable independent study.

Assessment

The 120 credits of taught Modules within Stage 1 of the Programme are assessed through in-course assessments, including:

Extended essays
Oral presentations
Poster presentations
Statistical assignments
Critical appraisals
Practical skills tests
Data reports
Research designs

You must successfully complete the taught component of the programme before progressing to Stage 2 where assessment is:

Dissertation (16-20,000 words)

Career prospects

After successfully completing this MSc, you should have a broad spectrum of knowledge and a variety of skills, making you highly attractive both to potential employers and research establishments. You will be able to pursue a wide range of professional careers, within commercial and academic archaeology and the wider heritage sector. Career paths will generally be specialist and will depend on the choice of modules. Graduates will be well placed to pursue careers as a specialist in isotope analysis, zooarchaeological analysis or human osteoarchaeology. They will also be in a position to apply for general laboratory based work and archaeological fieldwork. Working within science communication and management are other options. Potential employers include archaeological units, museums, universities, heritage institutions, Historic England and Cadw. Freelance or self-employment career routes are also common for animal and human bone analysts with postgraduate qualifications.

The archaeology department has strong links and collaborations across the heritage sector and beyond. British organisations that staff currently work with include Cadw, Historic England, English Heritage, Historic Scotland, National Museum Wales, the British Museum, the Welsh archaeological trusts and a range of other archaeology units (e.g. Wessex Archaeology, Oxford Archaeology, Cambridge Archaeology Unit, Archaeology Wales). In addition, staff are involved with archaeological research across the world. You will be encouraged to become involved in these collaborations via research projects and placements to maximise networking opportunities and increasing your employability.

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Programme Description. Read more

Programme Description

How do children learn to reason in increasingly abstract ways? How do they learn language with such remarkable speed and fluidity? How do children use their reasoning and language skills to help them explain and understand people’s behaviour and emotions? Why does the amount of information that we can hold in mind at once increase from early childhood to adulthood? Why does children’s ability to control their own thinking, attention and behaviour improve as they get older? How does the development of children’s brains affect their behaviour, memory and ability to learn?

In this taught programme on Developmental Cognitive Science, you will learn how questions like these can be addressed using research techniques from several inter-related disciplines (e.g., Developmental Psychology, Cognitive Psychology, Computational Science, Neuroscience, Linguistics).

This programme aims to enhance your understanding of key theoretical and practical issues about typical and atypical development in children and young people, from a cognitive science perspective. It also aims to equip you with the skills required to conduct independent scientific research that addresses key issues in developmental cognitive science.

The University of Edinburgh has a long tradition of research expertise in developmental psychology and in cognitive science. This programme brings these two strands together focusing on a developmental cognitive science approach to both typical and atypical development in children and young people.

You will benefit from the breadth and strength of the interdisciplinary academic community at Edinburgh, for example by having the opportunity to select option courses and attend research seminars across different disciplines.

Programme Structure

You will undertake the following:

Core courses (worth 100 credits in total):

Univariate Statistics and Methodology using R (10 credits)

Multivariate Statistics and Methodology using R (10 credits)

Psychological Research Skills (20 credits)

Current Topics in Psychological Research (10 credits)

Introduction to Developmental Cognitive Science (10 credits)

Research Methods for Developmental Cognitive Science (10 credits)

Seminar in Developmental Cognitive Science (10 credits)

Current Topics in Developmental Cognitive Science (10 credits)

Research Internship in Developmental Cognitive Science (10 credits)

2 option courses worth 20 credits in total:

Chosen from a wide range of courses relevant to Developmental Cognitive Science from Psychology or other disciplines, as approved by Programme Director (20 credits in total)

And a Dissertation in Developmental Cognitive Science (60 credits)

Learning Outcomes

The overall aim of the proposed programme is to advance students’ understanding of how questions about developmental changes in children’s cognitive abilities can be addressed using scientific methods drawn from a range of fields, including developmental psychology, cognitive psychology, computational modelling, neuroscience and linguistics. More specifically, the programme aims to:

enhance students’ understanding of key theoretical and practical issues about typical and atypical development in children and young people, from a cognitive science perspective

teach students how to conduct independent scientific research that addresses key issues in developmental cognitive science

provide advanced training in critical thinking skills

Students who successfully complete the programme will be able to:

carry out high quality original research in developmental cognitive science

evaluate published research studies in developmental cognitive science

make well-informed contributions to discussions about the interplay between developmental research and real-world applications/implications

Career Opportunities

Career opportunities for graduates from this programme include:

undertaking a PhD in Developmental Cognitive Science or in a related field

undertaking a Professional Doctorate in Clinical or Educational Psychology (applicable only to students who have an accredited undergraduate degree in Psychology)

wide variety of careers where it is valuable to be able to use research skills, critical thinking skills and understanding of developmental processes to develop and evaluate practices and policies relating to children and young people – e.g. teaching, speech & language therapy, policy development in education, health and social care.





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Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science. Read more
Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science.

Furthermore, we are home to the Centre for Research in Social Simulation (CRESS) and its world-leading expertise in agent-based modelling.

PROGRAMME OVERVIEW

Interest in simulation has grown rapidly in the social sciences. New methods have been developed to tackle this complexity. This programme will integrate traditional and new methods, to model complexity, evolution and the adaptation of social systems.

These new methods are having an increasing influence on policy research through a growing recognition that many social problems are insufficiently served by traditional policy modelling approaches.

The Masters in Social Science and Complexity will equip you to develop expertise in the methods necessary to tackle complex, policy-relevant, real-world social problems through a combination of traditional and computational social science methods, and with a particular focus on policy relevance.

PROGRAMME STRUCTURE

This programme is studied full-time over one academic year and part-time over two academic years. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analysis
-Field Methods
-Computational Modelling
-Theory Model Data
-Modelling the Complex World
-Policy Modelling
-Theory and Method
-Statistical Modelling
-Evaluation Research
-Dissertation

EDUCATIONAL AIMS OF THE PROGRAMME

The main aims of the programme are to:
-Provide an appropriate training for students preparing MPhil/PhD theses, or for 
 students going on to employment involving the use of social science and policy research
-Provide training that fully integrates social science, policy modelling and computational methodologies to a high standard
-Provide training resulting in students with high quality analytic, methodological, computational and communication skills

PROGRAMME LEARNING OUTCOMES
The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:
-Develop skills in tackling real world policy problems with creativity and sound methodological judgment
-Cover the principles of research design and strategy, including formulating research 
questions or hypotheses and translating these into practicable research designs and models
-Introduce students to the methodological and epistemological issues surrounding research in the social sciences in general and computational modelling in particular
-Develop skills in programming in NetLogo for the implementation of agent-based models for the modelling of social phenomena
-Develop skills in the acquisition and analysis of social science data
-Make students aware of the range of secondary data available and equip them to evaluate its utility for their research
-Develop skills in searching for and retrieving information, using library and Internet resources
-Develop skills in the use of SPSS, and in the main statistical techniques of data analysis, including multivariate analysis
-Develop skills in the use of CAQDAS software for the analysis of qualitative data
-Develop skills in writing, in the preparation of a research proposal, in the presentation ofresearch results and in verbal communication
-Help students to prepare their research results for wider dissemination, in the form of seminar papers, conference presentations, reports and publications, in a form suitable for a range of audiences, including academics, stakeholders, policy makers, professionals, service users and the general public

Knowledge and understanding
-Show advanced knowledge of qualitative, quantitative and computational methodologies in the social science
-Show advanced knowledge of modelling methodologies, model construction and analysis
-Show critical understanding of methodological and epistemological challenges of social science and computer modelling
-Show critical awareness and understanding of the methodological implications of a range of sociological theories and approaches
-Show understanding the use and value of a wide range of different research approaches across the quantitative and qualitative spectra
-Show advanced knowledge in data collection, analysis and data driven modelling
-Show advanced knowledge of policy relevant social science research and modelling
-Show advanced understanding of the policy process and the role of social science and modelling therein
-Show advanced knowledge of statistical modelling

Intellectual / cognitive skills
-Systematically formulate researchable problems; analyse and conceptualise issues; critically appreciate alternative approaches to research; report to a range of audiences
-Conceptual development of Social Science and Complexity models to creatively enhance the understanding of social phenomena
-Integration of qualitative, quantitative and computational data
-Judgement of problem-methodology match
-Analyse qualitative and quantitative data drawn both from ‘real world’ and ‘virtual world’ environments, using basic and more advanced techniques, and draw warranted conclusions
-Develop original insights, questions, analyses and interpretations in respect of research questions
-Critically evaluate the range of approaches to research

Professional practical skills
-Formulate, design, plan, carry out and report on a complete research project
-Use the range of traditional and computational techniques employed in sociological research
-Ability to produce well founded, data driven and validated computational models
-Generate both quantitative and qualitative data through an array of techniques, and select techniques of data generation on appropriate methodological bases
-Employ a quantitative (SPSS) and qualitative software package to manage and analyse data
-Plan, manage and execute research as part of a team and as a sole researcher
-Ability to communicate research findings models in social science and policy relevant ways
-Ability to manage independent research

Key / transferable skills
-Communicate complex ideas, principles and theories by oral, written and visual means
-Apply computational modelling methodology to complex social issues in appropriate ways
-Creativity in approaching complex problems and a the ability of communicating and justifying problem solutions
-Apply computing skills for computational modelling, research instrument design, data analysis, and report writing and presentation
-Work to deadlines and within work schedules
-Work independently or as part of a team
-Demonstrate experience of a work environment

PLACEMENTS

On the MSc Social Science and Complexity, we offer the opportunity to take a research placement during the Easter vacation. This will provide you with first-hand experience of real-life policy research in action.

Organisations in which placements might be possible are a number of consultancies (e.g. Sandtable), government departments (e.g. Defra) and academic research centres (e.g. Centre for Policy Modelling at Manchester).

CAREER OPPORTUNITIES

Computational methods and especially computer-based simulations, are becoming increasingly important in academic social science and policy making.

Graduates might find career opportunities in government departments, consultancies, government departments, consultancies, NGOs and academia.

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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This Masters course will open the door to a fascinating and fast-moving sector of analytical science that will build on your previous undergraduate studies, in chemistry, biology or other appropriate science courses. Read more
This Masters course will open the door to a fascinating and fast-moving sector of analytical science that will build on your previous undergraduate studies, in chemistry, biology or other appropriate science courses. You will gain knowledge and scientific skills that are directly applicable to the field of forensic science, with prospects of employment in forensic science laboratories as well as in other analytical science laboratories.

The course involves a unique combination of forensic chemistry and forensic biology, covering subjects such as trace evidence, toxicology and DNA analysis. Once you have covered the underlying principles of both areas, you can then specialise in your chosen field for your MSc research project.

The course is accredited by the Chartered Society of Forensic Sciences, which enhances its credibility and currency among potential employers.

This course can also be taken part time - for more information, please view this web-page: https://www.northumbria.ac.uk/study-at-northumbria/courses/forensic-science-dtpfrs6/

Learn From The Best

Our teaching team are active researchers who routinely incorporate their expertise and enthusiasm into their teaching. Many of the staff have worked in forensic science laboratories and have been involved in high profile cases such as the Stephen Lawrence, Joanna Yeates, Suffolk strangler and Jigsaw murder cases. Their areas of research include toxicology, the analysis of fibres and their transfer and persistence and the analysis of ancient DNA.

Academic staff include former forensic biologists, forensic toxicologists, and forensic fibre experts. They continue to maintain close links with the industry including the police and practising forensic scientists. Many of them are well-established within professional forensic science societies and organisations, which directly inform policy and practices within the field.

Teaching And Assessment

Our teaching will give you a solid grounding in all the technical areas that are key to forensic science, while simultaneously developing the higher level of independent thinking and advanced interpretation that is expected at Masters level. To support your learning journey, many of the staff have an ‘open door’ policy which makes it easy to ask questions; it’s also possible to book appointments with them so that you can work through queries about lab work, concepts and theories, and any other aspects of the subject.

We use different types of assessments: some will contribute to your final grade while others will be used to provide you with guidance on your progress and reinforce your learning. You can expect both your tutors and your peers to provide useful comments and feedback throughout the course.

Module Overview
AP0700 - Graduate Science Research Methods (Core, 20 Credits)
AP0703 - Subject Exploration (Core, 20 Credits)
AP0708 - Applied Sciences Research Project (Core, 60 Credits)
AP0723 - Practices & Procedures in Forensic Science (Core, 20 Credits)
AP0724 - Forensic Toxicology & Drugs of Abuse (Core, 20 Credits)
AP0725 - Criminalistics (Core, 20 Credits)
AP0726 - Forensic Genetics (Core, 20 Credits)

Learning Environment

You will have access to a dedicated crime scene house to enable you to examine simulated crime scenes. Students can also access Return to Scene (R2S) software which provides a 360 degree interactive scan of a crime scene allowing you to perform further analysis in detail after you have left the scene. Northumbria University has also invested heavily in an impressive suite of analytical equipment allowing you to gain first-hand experience of the techniques used in operational laboratories.

We use a range of technologies to enhance your learning, with tools including web-based self-guided exercises, online tests with feedback, and electronic discussion boards. These tools support and extend the material that is delivered during lectures.

You will have 24/7 term-time access to Northumbria’s library, which was ranked #2 in the Times Higher Education Student Experience Survey for 2015 and has been accredited by the UK Government for Customer Service Excellence since 2010.

Research-Rich Learning

We host the Northumbria University Centre for Forensic Science and our research directly impacts on what and how you learn. Northumbria is helping to push the frontiers of knowledge in areas such as:
-Forensic fibre comparisons using statistical and chemometric approaches
-DNA profiling in contexts such as injuries to children and poaching of wildlife
-Human genetic and phenotypic variation
-Analytical toxicology

As part of the course, you will undertake a Masters project that will require you to evaluate relevant literature as well as to develop your ideas within the context of existing research. The project will involve information retrieval, critical appraisal, presentation of aims and strategy, development of advanced analytical and problem-solving skills, the discussion and interpretation of results, and the composition of a written dissertation. Each project will be aligned to an active area of research that is specific to an academic member of staff.

Give Your Career An Edge

This course is accredited by the Chartered Society of Forensic Sciences. This reflects the relevance and rigour of the curriculum, and provides assurance of workplace-ready knowledge and application.

The focus on practical laboratory work, combined with the mix of group work, independent learning and professional practice, will help ensure that you develop skills that are transferable to a range of careers and disciplines.

Throughout your time at Northumbria we will prompt you to reflect on your self-development through the Higher Education Achievement Report process. We will also encourage you to take advantage of the services of our Careers and Employment Service such as CV advice and interview preparation.

Your Future

Forensic science has gained a high profile through TV dramas and, in the years ahead the sector is likely to be further transformed by technological advances in a number of fields. With an MSc Forensic Science you will be well-placed to take up a fascinating and rewarding role in forensic science laboratories.

What’s more, by developing the attributes of a Masters student, including the ability to solve complex problems, think critically, and work effectively with others and on your own, you will enhance your employability in all sectors of the analytical science industry. You will also be well equipped to pursue further studies at PhD level.

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A postgraduate qualification from JCU can enhance your career prospects, enable you to reskill and change careers completely, or develop a specialist area of expertise and personal interest. Read more

Career Opportunities

A postgraduate qualification from JCU can enhance your career prospects, enable you to reskill and change careers completely, or develop a specialist area of expertise and personal interest.
A range of opportunities await graduates in the academia as well as in private and public sectors.
Aquaculture technicians, for instance, are involved in freshwater and marine farming, hatchery management and research into farmed species. They can be involved in research, equipment design, site development, and the harvesting, processing and shipment of products.
Graduates can also get jobs as research assistants or support staff for teaching. With a PhD, you can gain research positions (Postdoctoral, Fellowships) that are often funded for a few years or apply for permanent positions as a lecturer and researcher.
In the private and public sectors, opportunities exist in non-governmental organisations (e.g. Nature Conservation Agency), federal institutions including the Australian Institute of Marine Science (AIMS), Great Barrier Reef Marine Park Authority (GBRMPA) Commonwealth Science and Industrial Research Organisation (CSIRO), Environment Protection Authorities (EPA), to name a few.

Course learning outcomes

On successful completion of the Graduate Certificate of Science, graduates will be able to:
*Integrate and apply specialised theoretical and technical knowledge in one or more science disciplines
*Retrieve, analyse, synthesise and evaluate knowledge from a range of sources
*Plan and conduct reliable, evidence-based laboratory and/or field experiments/practices by selecting and applying methods, techniques and tools, as appropriate to one or more science disciplines
*Organise, analyse and interpret complex scientific data using mathematical, statistical and technological skills
*Communicate complex scientific ideas, arguments and conclusions clearly and coherently to a variety of audiences through advanced written and oral English language skills and a variety of media
*Identify, analyse and generate solutions to unpredictable or complex problems, especially related to tropical, rural, remote or Indigenous contexts, by applying scientific knowledge and skills with initiative and high level judgement
*Explain and apply regulatory requirements, ethical principles and, where appropriate, cultural frameworks, to work effectively, responsibly and safely in diverse contexts
*Reflect on current skills, knowledge and attitudes to manage their professional learning needs and performance, autonomously and in collaboration with others.

Award title

Graduate Certificate of Science (GCertSc)

Course articulation

Students who complete the Graduate Certificate of Science are eligible for entry to the Graduate Diploma of Science, and may be granted advanced standing for all subjects completed under the Graduate Certificate.

Entry requirements (Additional)

English band level 1 - the minimum English Language test scores you need are:
*Academic IELTS – 6.0 (no component lower than 5.5), OR
*TOEFL – 550 (plus minimum Test of Written English score of 4.0), OR
*TOEFL (internet based) – 79 (minimum writing score of 19), OR
*Pearson (PTE Academic) - 57

If you meet the academic requirements for a course, but not the minimum English requirements, you will be given the opportunity to take an English program to improve your skills in addition to an offer to study a degree at JCU. The JCU degree offer will be conditional upon the student gaining a certain grade in their English program. This combination of courses is called a packaged offer.
JCU’s English language provider is Union Institute of Languages (UIL). UIL have teaching centres on both the Townsville and Cairns campuses.

Minimum English language proficiency requirements

Applicants of non-English speaking backgrounds must meet the English language proficiency requirements of Band 1 – Schedule II of the JCU Admissions Policy.

Application deadlines

*1st February for commencement in semester one (February)
*1st July for commencement in semester two (mid-year/July)

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The graduates of James Cook University are prepared and equipped to create a brighter future for life in the tropics world-wide. JCU graduates are committed to lifelong learning, intellectual development, and to the display of exemplary personal, professional and ethical standards. Read more

Course learning outcomes

The graduates of James Cook University are prepared and equipped to create a brighter future for life in the tropics world-wide.
JCU graduates are committed to lifelong learning, intellectual development, and to the display of exemplary personal, professional and ethical standards. They have a sense of their place in the tropics and are charged with professional, community, and environmental responsibility. JCU graduates appreciate the need to embrace and be acquainted with the Aboriginal and Torres Strait Islander Peoples of Australia. They are committed to reconciliation, diversity and sustainability. They exhibit a willingness to lead and to contribute to the intellectual, environmental, cultural, economic and social challenges of regional, national, and international communities of the tropics.
On successful completion of the Graduate Diploma of Science, graduates will be able to:
*Integrate and apply advanced theoretical and technical knowledge in one or more science disciplines
*Retrieve, analyse, synthesise and evaluate knowledge from a range of sources
*Plan and conduct reliable, evidence-based laboratory and/or field experiments/practices by selecting and applying methods, techniques and tools, as appropriate to one or more science disciplines
*Organise, analyse and interpret complex scientific data using mathematical, statistical and technological skills
*Communicate complex scientific ideas, arguments and conclusions clearly and coherently to a variety of audiences through advanced written and oral English language skills and a variety of media
*Identify, analyse and generate solutions to unpredictable or complex problems, especially related to tropical, rural, remote or Indigenous contexts, by applying scientific knowledge and skills with initiative and high level judgement
*Explain and apply regulatory requirements, ethical principles and, where appropriate, cultural frameworks, to work effectively, responsibly and safely in diverse contexts
*Reflect on current skills, knowledge and attitudes to manage their professional learning needs and performance, autonomously and in collaboration with others.

Award title

GRADUATE DIPLOMA OF SCIENCE (GDipSc)

Course articulation

Students who complete the Graduate Diploma of Science are eligible for entry to the Master of Science, and may be granted advanced standing for all subjects completed under the Graduate Diploma.

Entry requirements (Additional)

English band level 1 - the minimum English Language test scores you need are:
*Academic IELTS – 6.0 (no component lower than 5.5), OR
*TOEFL – 550 (plus minimum Test of Written English score of 4.0), OR
*TOEFL (internet based) – 79 (minimum writing score of 19), OR
*Pearson (PTE Academic) - 57

If you meet the academic requirements for a course, but not the minimum English requirements, you will be given the opportunity to take an English program to improve your skills in addition to an offer to study a degree at JCU. The JCU degree offer will be conditional upon the student gaining a certain grade in their English program. This combination of courses is called a packaged offer.
JCU’s English language provider is Union Institute of Languages (UIL). UIL have teaching centres on both the Townsville and Cairns campuses.

Minimum English language proficiency requirements

Applicants of non-English speaking backgrounds must meet the English language proficiency requirements of Band 1 – Schedule II of the JCU Admissions Policy.

Career Opportunities

A postgraduate qualification from JCU can enhance your career prospects, enable you to reskill and change careers completely, or develop a specialist area of expertise and personal interest.
A range of opportunities await graduates in the academia as well as in private and public sectors.
Aquaculture technicians, for instance, are involved in freshwater and marine farming, hatchery management and research into farmed species. They can be involved in research, equipment design, site development, and the harvesting, processing and shipment of products.
Graduates can also get jobs as research assistants or support staff for teaching. With a PhD, you can gain research positions (Postdoctoral, Fellowships) that are often funded for a few years or apply for permanent positions as a lecturer and researcher.
In the private and public sectors, opportunities exist in non-governmental organisations (e.g. Nature Conservation Agency), federal institutions including the Australian Institute of Marine Science (AIMS), Great Barrier Reef Marine Park Authority (GBRMPA) Commonwealth Science and Industrial Research Organisation (CSIRO), Environment Protection Authorities (EPA), to name a few.

Application deadlines

*1st February for commencement in semester one (February)
*1st July for commencement in semester two (mid-year/July)

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The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. Read more
The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop skills in database management, programming, summarisation, modelling and interpretation of data. The programme provides graduates with an opportunity, through development of a research project, to investigate the more applied elements of the disciplines.

Visit the website: http://www.ucc.ie/en/ckr49/

Course Details

The MSc in Data Science and Analytics is a significant collaboration between the Departments of Computer Science and Statistics; designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever increasing and complex data. The programme emphasises the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making.

Format

A typical 5 credit module:
• 2 lecture hours per week
• 1–2 hours of practicals per week
• Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.

Structure

Students must attain 90 credits through a combination of:

- Core Modules (30 credits)
- Elective Modules (30 credits)
- Dissertation (30 credits)

Part 1 (60 credits)

- Core Modules (30 credits) -

CS6405 Data Mining (5 credits) - Dr. Marc Van Dongen
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (5 credits)

- Database Modules -

Students who have adequate database experience take:

CS6408 Database Technology (5 credits) - Mr. Humphrey Sorensen
CS6409 Information Storage and Retrieval (5 credits) - Mr. Humphrey Sorensen

- Students who have not studied databases take:

CS6503 Introduction to Relational Databases (5 credits)
CS6505 Database Design and Administration (5 credits)

Elective Modules (30 credits)

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

CS6322 Optimisation (5 credits) - Dr. Steve Prestwich
CS6323 Analysis of Networks and Complex Systems (5 credits) - Prof. Gregory Provan
CS6509 Internet Computing for Data Science (5 credits)
ST6032 Stochastic Modelling Techniques (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)

- Programming Modules -

Students who have adequate programming experience take:

CS6406 Large-Scale Application Development and Integration l (5 credits) - Professor Gregory Provan
CS4607 Large-Scale Application Development and Integration ll (5 credits) - Professor Gregory Provan

- Students who have not studied programming take:

CS6506 Programming in Python (5 credits)
CS6507 Programme in Python with Data Science and Applications (5 credits) - Dr. Kieran Herley

Part 2 (30 credits)

Students select one of the following modules:

CS6500 Dissertation in Data Analytics (30 credits)
ST6090 Dissertation in Data Analytics (30 credits)

Assessment

Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2015 Book and for each module in the Book of Modules 2015/2016 - http://www.ucc.ie/modules/

Postgraduate Diploma in Data Science and Analytics

Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.

Careers

This programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting Computer Science graduates in 2014-15 include:

Accenture, Aer Lingus, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Cisco, CiTi-Technology, Cloudreach, Dell, Digital Turbine Asia Pacific, EMC, Enterprise Ireland, Ericsson, First Derivatives, Guidewire, IBM, Intel, Open Text, Paddy Power, Pilz, PWC, SAP Galway Transverse Technologies, Trend Micro, Uniwink, Version 1 (Software).

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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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). Read more
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). 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:
-Have first class knowledge and capabilities for a career in life science research and in expert duties in the public and private sectors.
-Competence to work as a member of a group of experts.
-Have understanding of the regulatory and ethical aspects of scientific research.
-Have excellent communication and interpersonal skills for employment in an international and interdisciplinary professional setting.
-Understand the general principles of mathematical modelling, computational, probabilistic and statistical analysis of biological data, and be an expert in one specific specialisation area of the LSI programme.
-Understand the logical reasoning behind experimental sciences and be able to critically assess research-based information.
-Have mastered scientific research, making systematic use of investigation or experimentation to discover new knowledge.
-Have the ability to report results in a clear and understandable manner for different target groups.
-Have good opportunities to continue your studies for a doctoral degree.

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: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

Programme Contents

The Life Science Informatics Master’s Programme has six specialisation areas, each anchored in its own research group or groups.

Algorithmic Bioinformatics
Goes 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
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.

Selection of the Major

During the first Autumn semester, each specialisation area gives you an introductory course. At the beginning of the Spring semester you are assumed to have decided your study direction.

Programme Structure

Studies amount to 120 credits (ECTS), which can be completed in two years according to a personal study plan.
-60 credits of advanced studies from the specialisation area, including a Master’s thesis, 30 credits.
-60 credits of other studies chosen from the programme or from other programmes (e.g. computer science, mathematics and statistics, genetics, ecology and evolutionary biology).

Internationalization

The Life Science Informatics MSc is an international programme, with international students and an international research environment. The researchers and professors in the programme are internationally recognized for their research. A significant fraction of the teaching and research staff is international.

As a student you can participate in an international student exchange programme, which offers the possibility to include international experience as part of your degree. Life Science Informatics itself is an international field and graduates can find employment in any country.

In the programme, all courses are given in English. Although the Helsinki region is very international and English is widely spoken, you can also take courses to learn Finnish via the University of Helsinki’s Language Centre’s Finnish courses. The Language Centre also offers an extensive programme of foreign language courses for those interested in learning new languages.

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This programme provides students with thorough grounding in the theory, technical and practical skills in the increasingly critical field of Data Science. Read more
This programme provides students with thorough grounding in the theory, technical and practical skills in the increasingly critical field of Data Science. It includes optional modules in various application domains, drawing on the unique strengths of King's in health, law, the arts and humanities, and the social sciences. The programme will conclude with an individual project, focused either on analysing data from a particular domain, or on exploring computational or statistical methods.

Key benefits

- Unrivalled location in the heart of London giving access to major libraries and leading scientific societies, including the BCS Chartered Institute for IT, and the Institution of Engineering and Technology (IET).

- Equips graduates with an in-depth understanding of the general principles of the computational and statistical methodologies and methods used in data science, and their underlying assumptions and limitations

- Provides students with the skill set required to plan, undertake, manage, and critically assess a data science project.

- Access to speakers of international repute through seminars and external lectures, enabling students to keep abreast of emerging knowledge in advanced computing and related fields.

Visit the website: http://www.kcl.ac.uk/study/postgraduate/taught-courses/data-science-msc.aspx

Course detail

- Description -

An MSc in Data Science will provide you with the practical skills needed to effectively assemble, collate, store, manage and retrieve data required for data science projects and the critical judgement to decide the appropriate statistical and computational data exploration or analysis techniques to evaluate data science activities and projects.

- Course purpose -

The purpose of this degree programme is to train graduates from quantitative disciplines or with relevant quantitative work experience in current methods and techniques of data science, particularly the science of large-scale data collections. These methods and techniques include both computational techniques and methods from mathematical statistics. The MSc will also provide you with an appreciation for the professional, ethical and legal responsibilities of the data scientist, along with standard conceptual or scientific models in at least one domain of application of data science. Your individual project will typically aim to apply these methods to a problem in a specific application domain, and provide valuable preparation for a career in research or industry.

- Course format and assessment -

Lectures; tutorials; seminars; laboratory sessions; optional career planning workshops. Assessed through: coursework; written examinations; final project report.

Core modules:

- Data Science Individual Projects
- Data Mining & Machine Learning
- Simulation & Reseach Methodology

(without a Computer Science undergraduate degree):

- Computer Programming
- Databases, Data Warehousing & Information Retrieval

Career prospects

Via the Department’s Careers Programme, students are able to network with top employers and obtain advice on how to enhance career prospects.

How to apply: http://www.kcl.ac.uk/study/postgraduate/apply/taught-courses.aspx

About Postgraduate Study at King’s College London:

To study for a postgraduate degree at King’s College London is to study at the city’s most central university and at one of the top 20 universities worldwide (2015/16 QS World Rankings). Graduates will benefit from close connections with the UK’s professional, political, legal, commercial, scientific and cultural life, while the excellent reputation of our MA and MRes programmes ensures our postgraduate alumni are highly sought after by some of the world’s most prestigious employers. We provide graduates with skills that are highly valued in business, government, academia and the professions.

Scholarships & Funding:

All current PGT offer-holders and new PGT applicants are welcome to apply for the scholarships. For more information and to learn how to apply visit: http://www.kcl.ac.uk/study/pg/funding/sources

Free language tuition with the Modern Language Centre:

If you are studying for any postgraduate taught degree at King’s you can take a module from a choice of over 25 languages without any additional cost. Visit: http://www.kcl.ac.uk/mlc

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