Data Science: Inferential Thinking through Simulations
list 5 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
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Les infos clés

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En résumé

Using real-world examples from a wide range of domains including law, medicine, and football, you’ll learn how data scientists make conclusions about unknowns based on the data available.

Often, the data we have are not complete, yet we’d still like to draw inferences about the world and quantify the uncertainty in our conclusions. This is called statistical inference. In this course, you will learn the framework for statistical inference and apply them to real-world data sets.

Notably, you will learn how to conduct hypothesis testing—comparing theoretical predictions to actual data, and choosing whether to accept those predictions. You will utilize the power of computation to conduct simulations by which you can evaluate theories or hypotheses about how the world works. This course will teach you the power of statistical inference: given a random sample, how do we predict some quantity that we cannot observe directly?

You will also learn how to by quantifying the uncertainty in the conclusions you draw from hypothesis testing. This helps assess whether patterns that appear to be present in the data actually represent a true relationship in the world, or whether they might merely reflect random fluctuations due to chance. Throughout this course, we will go over multiple methods for estimation and hypothesis testing, based on simulations and the bootstrap method. Finally, you will learn about randomized controlled experiments and how to draw conclusions about causality.

The course emphasizes the conceptual basis of inference, the logic of the decision-making process, and the sound interpretation of results.

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Les prérequis

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Le programme

  • The logical and conceptual frameworks of statistical inference
  • How to conduct hypothesis testing, permutation testing, and A/B testing
  • The purpose and power of resampling methods
  • Relations between sample size and accuracy
  • P-values, quantifying uncertainty, and generating confidence intervals using the bootstrap method
  • How to interpret the results from hypothesis testing
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Les intervenants

Ani Adhikari
Teaching Professor of Statistics
UC Berkeley

John DeNero
Giancarlo Teaching Fellow in the EECS Department
UC Berkeley

David Wagner
Professor of Computer Science
UC Berkeley

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La plateforme

Edx

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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