date_range Débute le 9 janvier 2019
event_note Se termine le 27 mars 2019
list 8 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
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Les infos clés

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timer 32 heures de cours

En résumé

Data is everywhere, from the media to the health sciences, and from financial forecasting to engineering design. It drives our decisions, and shapes our views and beliefs. But how can we make sense of it?

This course introduces some of the key ideas and concepts of statistics, the discipline that allows us to analyse and interpret the data that underpins modern society.

In this course, you will explore the key principles of statistics for yourself, using interactive applets, and you will learn to interpret and evaluate the data you encounter in everyday life.

No previous knowledge of statistics is required, although familiarity with secondary school mathematics is advisable.

Logo image: © The University of Edinburgh 2016 CC BY, derived from Waverley Bridge, by Manuel Farnlack on Flickr, 2010 CC BY

After completing this course, you’ll be able to:

  • Understand the key principles of statistics;
  • Interpret and evaluate the kinds of data found in everyday life;
  • Perform, and interpret results from, simple statistical analyses.

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Les prérequis

Secondary school mathematics (GCSE/Standard Grade/National 5 grade C or above in Mathematics, or equivalent)

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Le programme

Week 1: Introducing Data
What is statistics? We begin the course with this question, and see how data lies at the heart of statistics. We look at common techniques for presenting and summarising data.

Week 2: Patterns in Data
We look further into the science of data analysis, focusing on finding and interpreting relationships between different data sets, and on using trends in data to make predictions.

Week 3: Collecting Data
We look at key methods of data collection, seeing how we generally use samples of a population to make predictions about the whole population. We learn about how to choose a representative sample, and how to set up a statistical experiment.

Week 4: Uncertainty in Data
Using samples to make predictions about a population brings uncertainty into our data. As the study of risk and uncertainty, probability is therefore key to understanding statistics. We introduce the ideas here for describing and quantifying uncertainty via probabilities.

Week 5: Distribution
We look again at probability and describe a range of common situations that lead to standard forms for describing the associated probability of different possible outcomes. The ability to describe such probabilities provides the basis for building up the knowledge and understanding needed to study deeper statistical methods.

Week 6: Estimation
We will build on the idea of estimating properties of a population using sample data. Further, as the answer that we provide is only an estimate of the (unknown) true value, we will also describe how we may construct an associated uncertainty interval for the parameter being estimated, using properties of the sampling distribution.

We introduce the testing method that is fundamental to all of science: the hypothesis test. We learn how to set up and perform a hypothesis test, and look at how such tests are used in scientific research.

Week 7: Statistical Testing
We introduce the concepts of the testing method that is fundamental to all of science: the hypothesis test. We learn how to set up and perform simple hypothesis tests.

Week 8: Further Statistical Testing
We build on the ideas of the hypothesis test and look at further tests that are commonly used in scientific research.
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Les intervenants

Mairi Walker
Mathematics Engagement Officer for the year 2016/17
The University of Edinburgh

Ruth King
Thomas Bayes’ Chair of Statistics
The University of Edinburgh

Chris Sangwin
Professor of Technology Enhanced Science Education
The University of Edinburgh

Bruce Worton
Lecturer in Statistics
University of Edinburgh

Francesca Iezzi
Mathematics Engagement Officer
University of Edinburgh

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Le concepteur

Delivering excellence in teaching and learning. Consistently ranked as one of the world's top 50 universities and top 3 UK provider of online Masters courses. The University of Edinburgh offers over 700 diverse degree programmes with over 31,000 students currently studying with us from across the world.
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La plateforme

EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.

EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.

Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.

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