Data Science: Find Books

The legal domain distinguishes between different types of data and attaches a different level of protection to each of them. Thus, non-personal data are left largely unregulated, while privacy and data protection rules apply to personal data or personal information. There are stricter rules for processing sensitive personal data than for 'ordinary' personal data, and metadata or communications data are regulated differently than content communications data. Technological developments challenge these legal categorisations on at least three fronts: First, the lines between the categories are becoming harder to draw and more fluid. Second, working with various categories of data works well when the category a datum or dataset falls into is relatively stable. However, this is less and less so. Third, scholars increasingly question the rationale behind the various legal categorisations. This book assesses to what extent either of these strategies is feasible and to what extent alternative approaches could be developed by combining insights from three fields: technology, practice and law.

This book explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications.

Amidst a growing movement to use science for positive change, this book offers a solution-oriented approach to the ethical challenges of data science. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction.

This book outlines the essential stages in running successful data science projects. It provides comprehensive guide-lines to help you identify potential issues and then a range of strategies for mitigating them. The book is also organized as a sequential process allowing the reader to work their way through a project from an initial idea all the way to a deployed and integrated product.

"Introduces the ethical dimensions of data science for undergraduates, masters students, and professionals"-- Summary provided by the publisher