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.
Rezensionen / Stimmen
Data analysis by Graham Upton and Dan Brawn is a concise and to-the-point guide to data analysis. Compared to other statistics and data analysis books, this one is well written without delving into extensive details on each topic, making it accessible to college-level students who want to grasp the basics in a short time. For newcomers to data analysis and its foundational concepts, this book serves as an excellent starting point. The authors present the material in a straightforward and clear manner, allowing readers to comprehend the essentials in a single sitting. The book focuses solely on the main points necessary for a deeper exploration of data analysis, making it suitable for beginners rather than those seeking to deepen an existing understanding. * J. Arul, Computing Reviews *
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Maße
Höhe: 229 mm
Breite: 163 mm
Dicke: 20 mm
Gewicht
ISBN-13
978-0-19-288577-7 (9780192885777)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Dr Upton studied at Leicester and Birmingham Universities before taking up a position at the University of Newcastle-upon Tyne. From 1973 until 2014 he taught at the University of Essex, where his responsibilities included time as a dean and as a head of department. His varied data analysis included making sense of British voting figures, identifying gene patterns using microarrays, and estimating rainfall using many different measuring instruments.
Dr Brawn is the holder of two PhDs: in Seismology from the University of Witwatersrand (1989); in Applied Statistics from the University of Essex (2009), both fields involving considerable data analysis. His extensive teaching experience covers many branches of the mathematical sciences and has most recently focused on introducing data analysis to students with limited mathematical background.
Autor*in
Formerly Professor of Applied StatisticsFormerly Professor of Applied Statistics, University of Essex, UK
LecturerLecturer, Department of Mathematical Sciences, University of Essex, UK
1: First steps
2: Summarising data
3: Probablity
4: Probability distributions
5: Estimation and confidence
6: Models, p-values, and hypotheses
7: Comparing proportions
8: Relations between two continous variable
9: Several explanatory variables
10: Classification
11: Last Words
