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Data analysis has been a hot topic for a number of years, and many future data scientists have backgrounds that are relatively light in mathematics. This slim volume provides a very approachable guide to the techniques of the subject, designed with such people in mind. Formulae are kept to a minimum, but the book's scope is broad, introducing the basic ideas of probability and statistics and more advanced techniques such as generalised linear models, classification using logistic regression, and support-vector machines. An essential feature of the book is that it does not tie to any particular software. The methods introduced in this book could also be implemented using any other statistical software and applying any major statistical package. Academically, the book amounts to a first course, practical for those at the undergraduate level, either as part of a mathematics/statistics degree or as a data-oriented option for a non-mathematics degree. The book appeals to would-be data scientists who may be formula shy. However, it could also be a relevant purchase for statisticians and mathematicians, for whom data science is a new departure, overall appealing to any computer-literate reader with data to analyse.

Mathematical statistics. --- Probabilities. --- Analyse des données. --- Statistique mathématique. --- Probabilités.

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"Causality is central to the understanding and use of data; without an understanding of cause and effect relationships, we cannot use data to answer important questions in medicine and many other fields"

Causation. --- Science --- Observation (Scientific method) --- Inference. --- Probabilities. --- Mathematical statistics. --- Causalité. --- Sciences --- Observation (méthode scientifique) --- Inférence. --- Probabilités. --- Statistique mathématique. --- Experiments. --- Expériences.