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Designed for students from a wide range of quantitative backgrounds this programme will enhance and develop your skills in a broad range of statistics topics.

You will study modern statistical research methods applied to social science, medicine or biology aligned with interest in statistics. The programme will encompass research skills (such as critical appraisal of published research journal articles) and deep mathematical and statistical learning tools.

Highly desirable for those who want to pursue a career in statistics, this programme will equip you with a range of statistical skills, including problem-solving, project work and presentation, and prepare you for prominent roles in a wide spectrum of employment and research.

Designed for students from a wide range of quantitative backgrounds this programme will enhance and develop your skills in a broad range of statistics topics.

You will study modern statistical research methods applied to social science, medicine or biology aligned with interest in statistics. The programme will encompass research skills (such as critical appraisal of published research journal articles) and deep mathematical and statistical learning tools.

Highly desirable for those who want to pursue a career in statistics, this programme will equip you with a range of statistical skills, including problem-solving, project work and presentation, and prepare you for prominent roles in a wide spectrum of employment and research.

Course Information

Level of Study
Postgraduate

Mode of Study
1 year full-time

Department
Mathematics, Physics and Electrical Engineering

Location
Ellison Building, Newcastle City Campus

City
Newcastle

Start
September 2019

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

Your tutors will use a variety of teaching methods including lectures, seminars and workshops. As this is a Masters course there is a major element of independent learning and self-motivated reflection.

You’ll be taught through lectures, classes, seminars and workshops in computer labs where you’ll work with your fellow students, supported by academic staff.

You will also be able to use the university’s online resources to support your study, including the e-learning portal where you can access course materials and develop discussions with your peers.

We will support you to take an independent approach to problem solving and you’ll develop skills in computer programming and data analysis using a range of specialist applications.

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

You’ll learn from a team of leading mathematicians and statisticians. Our internationally diverse teaching team come from a wide range of backgrounds and have a wealth of experience between them.

You can find out more about our teaching staff and their specific areas of interest and expertise in the staff profiles section.

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

Technology will play a big part in your learning and is embedded throughout the course and you will be able to benefit from an extensive range of specialist facilities to support all aspects of your studies.

You will have access to the high computational and modelling mathematical lab and using the latest statistical software for Big Data and simulations.

Facilities

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

As a Masters student you will develop your research skills to a new and higher level. The research undertaken by our staff focuses on the development and application of statistical models to real-life problems from fields such as health, social science and medicine.

Examples of current and recent research include disease mapping, survival analysis and predictive modelling in contexts such as addressing health inequalities in both developed and developing countries along with evaluation of crime prevention strategies.

Our staff use their research expertise to inform their teaching to ensure that your learning is on the cutting edge of developments in the field of statistics.

Research

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

An MSc in statistics opens up a range of career options.

These include options from statistical consultancy to PhD studies and academic research.

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

By the end of this course, you will have the skills necessary to follow statistical careers, within business, industry, universities or research institutes.

This course will also enable you to progress onto a PhD in Statistics.

Book an Open Day / Experience Statistics

Visit an Open Day to really get an inside view of what it's like to study Statistics at Northumbria. Speak to staff and students from the course, get a tour of the facilities and discover your funding options.

Course in brief

Who would this Course suit?

This course is for students from a range of quantitative backgrounds who are looking to build a career in the field of Statistics.

Entry Requirements 2019/20

Standard Entry

 

Applicants should normally have:

A minimum of a 2:2 honours degree in a subject with significant quantitative content.

Applicants with relevant professional experience and other professional qualifications will be considered.

International qualifications

If you have studied a non UK qualification, you can see how your qualifications compare to the standard entry criteria, by selecting the country that you received the qualification in, from our country pages. Visit www.northumbria.ac.uk/yourcountry

English Language requirements 

International applicants are required to have a minimum overall IELTS (Academic) score of 6.5 with 5.5 in each component (or approved equivalent*).

*The university accepts a large number of UK and International Qualifications in place of IELTS.  You can find details of acceptable tests and the required grades you will need in our English Language section. Visit www.northumbria.ac.uk/englishqualifications

Fees and Funding 2019/20 Entry

Full UK Fee: £7,995

Full EU Fee: £7,995

Full International Fee: £15,000

ADDITIONAL COSTS

There are no Additional Costs

FUNDING INFORMATION

Click here for UK and EU Masters funding and scholarships information.

Click here for International Masters funding and scholarships information.

Click here for UK/EU Masters tuition fee information.

Click here for International Masters tuition fee information.

Click here for additional costs which may be involved while studying.

Click here for information on fee liability.

 

 

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Modules Overview

Modules

Module information is indicative and is reviewed annually therefore may be subject to change. Applicants will be informed if there are any changes.

KC7014 -

Bayesian Statistics (Core, 20 Credits)

You will gain an understanding of Bayesian statistics. The module introduces Bayes’ Theorem and its application to both simple and complex problems. It examines the algebraic and numerical techniques used to handle different problems and the important concept of combining prior information with data to form a posterior distribution. A wide range of real-life problems will be used to motivate the subject matter. Both conjugate and non-conjugate problems will be considered.

The module will be delivered using a combination of lecture and computer laboratory sessions. Assessment will be via a formal examination.

OUTLINE SYLLABUS

Bayes’ Theorem and its application. Bayesian inference.
Prior distributions, maximum likelihood estimation, posterior distributions.
Single-parameter models, multi-parameter models. Vague and informative priors.
Large-sample inference. Normal approximation to the posterior distribution.
Monte Carlo method and simulation of data. Markov Chain Monte Carlo (MCMC) methods for non-conjugate problems, including the Gibbs sampler and Metropolis-Hastings.
Introduction to Bayesian regression modelling.

More information

KC7015 -

Time Series & Forecasting (Core, 20 Credits)

You will learn about a range of appropriate statistical techniques that are used to analyse time series data. You will be introduced to the different methods that can be used to remove any trend or seasonality that are present in the data and learn how to determine the appropriate time series model for this modified time series. Once the model is chosen, you will learn verification techniques to confirm that you have selected the correct model and then, if required, learn how to forecast future values based on this model.

By the end of the module, you will have developed an awareness of different approaches to analysing time series data and to be able to tailor these techniques based on the initial assessment of the time series data.

Outline Syllabus
On this module, you will cover:
• Differencing methods to remove trends and/or seasonality.
• Diagnostic tools to select appropriate model
• Autoregressive Integrated Moving Average (ARIMA) models
• Model identification methods
• Verification of model
• Seasonal Autoregressive Integrated Moving Average (SARIMA) models and their identification and modelling.

An appropriate statistical computer package will be used.

More information

KC7018 -

Masters Project (Core, 60 Credits)

The module provides the opportunity to apply the concepts studied in the MMath degree to a problem with mathematical or real world application interest and do analytical and/or numerical exploration of this problem using mathematical techniques and models.

Outline Syllabus

This module will allow you to engender a spirit of enquiry into a practical or theoretical dissertation. The aim of this module is to provide the opportunity for students to work independently, under supervision of the project supervisor, on a project topic, research the background, develop the appropriate methodology, construct the model and apply to appropriate data (if necessary) to verify and evaluate the model performance. The project can be a theoretical or laboratory-based exercise. The module will include an aspect of research and critical appraisal; development of practical skills and/or discussion of results; and an opportunity to compose a written dissertation. The module will be assessed by viva and dissertation bringing out the key aspects of the project. The dissertation would vary with subject area but would typically be within the range 50 to 70 pages. A project plan should be completed as part of KC7023 Research Methods and Professional Practice module and should be available prior to starting the project.
Assessment of the module is written dissertation and oral Viva examination (70% and 30% respectively). Students will receive feedback on their Viva first; this will enable students to act on the feedback received. Feedback for the Dissertation will be given at the end of the semester.

More information

KC7023 -

Research Methods and Professional Practice (Core, 20 Credits)

You will learn about the techniques and methods used in applied, contract, academic and/or professional research. You will also develop your understanding of the theoretical philosophies underpinning research, as well as how to design and run research in an applied context. This will focus on the problems and issues that occur in establishing empirical knowledge in the information science area. You will explore various research philosophies and methodologies which can be applied to professional practice. You are also encouraged to apply models of reflective practice throughout the module.
Topics include:
1) The research landscape (research philosophies)
a) The epistemology and ontology of major research paradigms
b) Limitations of the research philosophies
c) The research hierarchy

2) Information Discernment
a) High level of Information Literacy in all domains
b) Developing critical arguments in support of the students own research
c) Identifying appropriate evidence from practice literature to demonstrate engagement with the profession
d) The ethical researcher
e) The reflective researcher
f) Developing research questions, aims and hypotheses

3) Research methods, data collection techniques and data analysis
a) The use of methods in research and implications of choice
b) Exploring methods, including; phenomenology, ethnography, case study, action research, Delphi study, experiments, quasi-experiments, survey etc.
c) Exploring techniques, including; questionnaire design, interviewing, focus groups, observation, diaries etc.
d) Data analysis, including; descriptive and inferential statistics, statistical modelling, multivariate techniques, constant comparative analysis, grounded theory and theoretical sensitivity.
e) Research data management
f) Writing research proposals

More information

KL5001 -

Academic Language Skills for Mathematics, Physics and Electrical Engineering (Core, 0 Credits)

Academic skills when studying away from your home country can differ due to cultural and language differences in teaching and assessment practices. This module is designed to support your transition in the use and practice of technical language and subject specific skills around assessments and teaching provision in your chosen subject. The overall aim of this module is to develop your abilities to read and study effectively for academic purposes; to develop your skills in analysing and using source material in seminars and academic writing and to develop your use and application of language and communications skills to a higher level.

The topics you will cover on the module include:

• Understanding assignment briefs and exam questions.
• Developing academic writing skills, including citation, paraphrasing, and summarising.
• Practising ‘critical reading’ and ‘critical writing’
• Planning and structuring academic assignments (e.g. essays, reports and presentations).
• Avoiding academic misconduct and gaining credit by using academic sources and referencing effectively.
• Listening skills for lectures.
• Speaking in seminar presentations.
• Presenting your ideas
• Giving discipline-related academic presentations, experiencing peer observation, and receiving formative feedback.
• Speed reading techniques.
• Developing self-reflection skills.

More information

KL7006 -

Generalized Linear Models (Core, 20 Credits)

This module covers models that extend linear models to generalized linear models (GLM). Two well known families of models will be learnt. These are binary and count data. Binary data is data whose unit can take on only two possible states, traditionally termed 0 and 1. Count data is a type of data in which the observations can take only the non-negative integer. An extension of binary data, often referred to as multinomial data will form part of the module. For count data, negative binomial model used for overdispersed count data will be discussed.

Key issues in GLMs such as model estimation, statistical inference and evaluation of predictive accuracy of the developed model will be discussed. The models are examined using variable selection criteria and regression diagnostics to improve model accuracy. You will use R to execute data analysis.

OUTLINE SYLLABUS
(1) Theory of the generalized linear model
(2) The maximum likelihood principle
(3) GLM for binary outcome variables including logistic, probit and skew extensions
(4) Multinomial Response Models
(5) GLM for count data including Poisson and Negative binomial models
(6) Robust estimation including Huber-White sandwich estimator and Generalized least squares
(7) Penalized regression for Binary data- Ridge and Firth penalization

More information

KL7007 -

Statistical inference and computational statistics (Core, 20 Credits)

This module enables you to gain a deep knowledge of modern statistical inference and computational methods . You will learn how to use computational methods to address complicated statistical problems and apply statistical inference and computational methods to data arising from real-world studies. For example, you will learn how to use Newton Raphson method to estimate the treatment effect when developing a new drug or bootstrap and sandwich formula to estimate variances of market price change, which will be used to determine the price of asset with complex structure. By the end of the module, you will have developed an awareness of using different sophisticated approaches to tackle non-standard problems

Outline syllabus
On this module, you will cover:
• Maximum likelihood estimation
• Newton-Raphson method
• Fisher information
• Statistical simulation
• Random effects models
• Gaussian quadrature
• EM algorithm
• Bootstrap
• Sandwich estimator

More information

KL7008 -

Longitudinal Data Analysis (Core, 20 Credits)

This module covers models that extend linear and generalized linear models in crossectional settings to longitudinal data, where measurements within subjects are correlated. Since subjects are measured repeatedly over time, there may be problem of attrition, commonly known as drop-out. You will learn about statistical models for both continuous and discrete longitudinal data. The impact of missing data and methodology for missing data in these framework will be considered using Rubin’s taxonomy. You will use the open-source and freely avaialble R software package to execute data analysis.

Outline Syllabus
Continuous data
(1) Extension of cross-sectional data to longitudinal data and various data examples
(2) Ad hoc methods for longitudinal data analysis (including two-stage analyses)
(3) The general linear mixed-effect model (LMM)
(4) Exploratory data analysis methods for longitudinal data
(5) Marginal and random effects models

Discrete data
(1) Model families (marginal, conditional and subject-specific models)
(2) Generalized Estimating Equations (GEE) methods for marginal models
(3) Generalized linear mixed model (GLMM) for subject specific models

Missing data
(1) Missing data concepts
(2) Simple missing data methods (e.g. complete case, mean imputation, last observation carried forward and direct likelihood)
(3) Multiple imputation techniques with emphasis on the use of MICE (multiple imputation by chained equations)

More information

Any Questions?

Our admissions team will be happy to help. They can be contacted on 0191 406 0901.

Applicants Contact Details:

bc.applicantservices@northumbria.ac.uk

Current, Relevant and Inspiring

We continuously review and improve course content in consultation with our students and employers. To make sure we can inform you of any changes to your course register for updates on the course page.

Your Learning Experience find out about our distinctive approach at 
www.northumbria.ac.uk/exp

Terms and Conditions - northumbria.ac.uk/terms
Fees and Funding - northumbria.ac.uk/fees
Admissions Policy - northumbria.ac.uk/adpolicy

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