Analytics and Tech Mining for Engineering Managers

Scott W. Cunningham (u. a.)

ISBN 10: 1606505106 / ISBN 13: 9781606505106

Published by Momentum Press, 2016

About this Item

Analytics and Tech Mining for Engineering Managers | Scott W. Cunningham (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2016 | Momentum Press | EAN 9781606505106 | Verantwortliche Person für die EU: Mare Nostrum Group B.V., Doelen 72, 4831 GR BREDA, NIEDERLANDE, gpsr[at]mare-nostrum[dot]co[dot]uk | Anbieter: preigu.

Seller Inventory # 103611149

Bibliographic Details

• Title: Analytics and Tech Mining for Engineering Managers
• Publisher: Momentum Press
• Publication Date: 2016
• Language: English
• ISBN 10: 1606505106
• ISBN 13: 9781606505106
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 8D millimeters 229 millimeters width by 152 millimeters height by 8 millimeters depth
• Weight: 223 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book offers practical tools in Python to students of innovation, as well as competitive intelligence professionals, to track new developments in science, technology, and innovation. The book will appeal to both tech-mining and data science audiences. For tech-mining audiences, Python presents an appealing, all-in-one language for managing the tech-mining process. The book is a complement to other introductory books on the Python language, providing recipes with which a practitioner can grow a practice of mining text. For data science audiences, this book gives a succinct overview over the most useful techniques of text mining. The book also provides relevant domain knowledge from engineering management; so, an appropriate context for analysis can be created. This is the first book of a two-book series. This first book discusses the mining of text, while the second one describes the analysis of text. This book describes how to extract actionable intelligence from a variety of sources including scientific articles, patents, pdfs, and web pages. There is a variety of tools available within Python for mining text. In particular, we discuss the use of pandas, BeautifulSoup, and pdfminer.

About the Author

Scott Cunningham is an associate professor at the Delft University of Technology. He teaches and researches topics including data science, network science, and game theory. His research is directed toward helping national governments anticipate the potentially unforeseen consequences of new and emerging technologies. Prior to joining Delft University of Technology, he worked for AT&T as a knowledge discovery analyst, helping customers in the manufacturing and commercial sectors make the best use of their data.

Jan Kwakkel is an assistant professor at Delft University of Technology. His research focusses on model-based support for decision making, with a particular focus on the treatment of uncertainty. Text mining, and more general machine learning techniques, are important in his research. He has applied his research in various domains including transport, water, and energy.