Data Analysis (Paperback)

Graham Upton

ISBN 10: 0192885782 / ISBN 13: 9780192885784

Published by Oxford University Press, Oxford, 2023

About this Item

Paperback. 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, classificationusing 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 bookcould 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 whomdata science is a new departure, overall appealing to any computer-literate reader with data to analyse. This slim volume provides a very approachable guide to the techniques and basic ideas of probability and statistics and more advanced techniques such as generalised linear models, classification using logistic regression, and support-vector machines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780192885784

Bibliographic Details

• Title: Data Analysis (Paperback)
• Publisher: Oxford University Press, Oxford
• Publication Date: 2023
• Language: English
• ISBN 10: 0192885782
• ISBN 13: 9780192885784
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.

About the Author

Graham Upton, Formerly Professor of Applied Statistics, University of Essex, UK, Dan Brawn, Lecturer, Department of Mathematical Sciences, University of Essex, UK

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.