EMAGISTER CUM LAUDE
Middlesex University

Applied Statistics MSc/PGDip

4.8 excellent 2 opinions
Middlesex University
À London (England)

Prix à consulter

Infos importantes

Typologie Master
Lieu London (England)
Durée 1
Début Dates au choix
  • Master
  • London (England)
  • Durée:
    1
  • Début:
    Dates au choix
Description

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.

Installations (1)
Où et quand
Début Lieu
Dates au choix
London
The Burroughs, NW4 4BT, London, England
Voir plan
Début Dates au choix
Lieu
London
The Burroughs, NW4 4BT, London, England
Voir plan

Opinions

4.8
excellent
Évaluation de la formation
100%
Recommandé
4.7
excellent
Évaluation du Centre

Opinions sur cette formation

J
Joe B
08/06/2015
Le meilleur de la formation: Professors encourage students for self development. Courses are mixture of lectures, seminars and independant work. Nice course and great people.
À améliorer: nothing to improve
Formation effectuée: Juin 2015
Recommanderiez-vous cette centre de formation ?: oui
E
Emma Ball
20/07/2016
Le meilleur de la formation: 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.
À améliorer: .
Formation effectuée: Juillet 2016
Recommanderiez-vous cette centre de formation ?: oui
* Opinions recueillies par Emagister et iAgora

Qu'apprend-on avec cette formation ?

Interpretation
IT
Statistics
Probability
Statistical Modelling
Modelling
Analysis
Big Data
Applied
electronic databases
computer packages
large populations
judgments

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.