Middlesex University

Applied Statistics MSc/PGDip

Middlesex University
In London

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Important information

Typology Master
Location London
Duration 1 Year
Start Different dates available
  • Master
  • London
  • Duration:
    1 Year
  • Start:
    Different dates available

Often known as the science of uncertainty, statistics the study of the collection, analysis, interpretation and presentation of data – is a subject that has an impact in almost all sectors of society. Applied statistics involves putting the theory into practice not only summarising and describing data, but extrapolating from it to draw conclusions about the population being studied. Social policy, medical practice and engineering all rely substantially on statistics and their correct use and interpretation; its impact can be life saving.

Facilities (1)
Where and when
Starts Location
Different dates available
The Burroughs, NW4 4BT, London, England
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Starts Different dates available
The Burroughs, NW4 4BT, London, England
See map

To take into account

· Requirements

Entry requirements UK & EU International How to apply Qualifications We normally require a second class honours degree 2:2 or above, in an appropriate subject with a significant amount of mathematics in its curriculum Eligibility UK/EU and international students are eligible to apply for this course. Academic credit for previous study or experience If you have relevant qualifications or work experience, academic credit may be awarded towards your Middlesex University programme of study. For further information please visit our Accreditation of Prior Learning page.

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Reviews on this course

Emma Ball
5.0 20/07/2016
What I would highlight: As one of the four institutions in the UK which have been approved as a mirror for the R statistical programming language, we ensure that you learn with the same innovative software and systems that you will use in your career.
What could be improved: .
Would you recommend this course?: Yes
Did this opinion help you? Yes (0)
Joe B
4.5 08/06/2015
What I would highlight: Professors encourage students for self development. Courses are mixture of lectures, seminars and independant work. Nice course and great people.
What could be improved: nothing to improve
Would you recommend this course?: Yes
Did this opinion help you? Yes (0)
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What you'll learn on the course

Statistical Modelling
Big Data
electronic databases
computer packages
large populations

Course programme

Course content

What will you study on the MSc Applied Statistics?

You’ll gain a thorough understanding of mathematical and statistical concepts and techniques and how to apply them to data sets. You’ll develop an advanced knowledge of probability, distributions, inference and stochastic processes, statistical modelling and methods of analysis, and will work on highly technical problems both independently and as part of a team. You’ll learn how to obtain different types of data from a variety of sources, including electronic databases; analyse it using programming and computer packages; and compare and choose between different methods of modelling and analysis. The course also covers big data, and the use of both small samples and big data to make judgments about large populations.

  • Modules
    • Statistical Modelling (30 credits) - Compulsory

      This module aims to give students a solid grounding in some of the most important analysis methods. It looks at the different practices and assumptions made in different applied scientific disciplines. It provides students with an understanding of the empirical techniques commonly used in statistical analysis as well as the ability to use these techniques and critically evaluate and interpret empirical work.

    • Probability and Stochastic Processes (30 credits) - Compulsory

      This module aims to give students a solid grounding in some of the most important methods employed by statisticians by providing a deeper understanding of probability theory and random processes. Students will be introduced to modern topics and techniques in stochastic processes. They will learn the relevant theory and gain the ability to formulate and solve practical problems.

    • Inference Theory (15 credits) - Compulsory

      This module aims to introduce students to advanced techniques in inference theory. It develops students’ ability to understand statistical theory as well as applying it to computational methods. Students are introduced to a wide-range of advanced techniques in classical inference and are given a practical introduction to Bayesian analysis.

    • Descriptive Statistical Analysis (15 credits) - Compulsory

      On this module, students are taught the important concepts of descriptive statistical analysis applied on different types of data sets. The course will develop students’ appreciation of the task of a statistician for critically analysing data sets and will be useful to anyone considering a job in statistics. Students will develop a keener understanding about structures that underlie data observations.

    • Time Series and Forecasting (15 credits) - Compulsory

      Data obtained from observations collected sequentially over time are extremely common. The purpose of time series analysis is to understand or model the stochastic mechanism that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series, and, possibly, other related series or factors.

    • Data Mining (15 credits) - Optional

      The quantity of data available to analysts is growing at an ever-increasing rate. This data has become a vital tool for decision-making in a competitive world. However, the size, which makes the data so valuable, also makes it difficult to analyse using traditional statistical methods. This module introduces the student to a variety of methodologies now employed to explore, analyse, categorise and visualise data from large data sets.

    • Survival Analysis (15 credits) - Optional

      This module aims to introduce statistical methods used for modelling and evaluating survival data as well as to implement estimation and test procedures. Survival models are used in bio-statistical, epidemiological and health related fields, as well as in research in the physical sciences including economic, financial, sociological, psychological, political and anthropological data.

You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.

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