A common definition for a data scientist is someone who uses data to solve problems. A Philosophy of Data Science starts with the premise that it is not only important that one can solve problems, but that they are able to articulate them as well. Unfortunately, the critical skill of asking the right question, rather than simply finding the right answers, has been neglected by much of the data and computational social science literature. This book is intended to address this gap.
A Philosophy of Data Science begins by showing that the assumptions, beliefs, and goals that motivate the specification and application of data science models are based both on data - the focus of the data and computational social sciences - but also on theoretical and philosophical considerations as well. It has been written to develop a set of rules and tools that can help inform data and computational social scientists on how to best use the awesome methods that they now have at their disposal. Thus, this book is not a replacement for the impressive corpora of method-oriented data science literature used by today's quantitative analysts, but a complement to these contributions; one that uses philosophy to help motivate the questions to which they seek to give technical answers.
Auflage
Sprache
Verlagsort
Bury St Edmunds
Großbritannien
Zielgruppe
Editions-Typ
Produkt-Hinweis
Maße
Höhe: 212 mm
Breite: 148 mm
ISBN-13
978-1-83711-439-9 (9781837114399)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Klassifikation
Solomon Major earned his PhD in Political Science from Stanford University, USA. Both as a professor and as a government contractor, he seeks to leverage both social and data science methods to address real-world policy problems.
