Applied data science : lessons learned for the data driven business / Martin Braschler, Thilo Stadelmann, Kurt Stockinger, editors.
"This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it...
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| Language: | English |
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Springer Nature Switzerland AG,
[2019]
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| 245 | 0 | 0 | |a Applied data science : |b lessons learned for the data driven business / |c Martin Braschler, Thilo Stadelmann, Kurt Stockinger, editors. |
| 264 | 1 | |a Basel : |b Springer Nature Switzerland AG, |c [2019] | |
| 264 | 4 | |c ©2019. | |
| 300 | |a 1 online resource (xiii, 465 pages) : |b illustrations. | ||
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| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | 0 | |g Foundations -- |t Introduction to Applied Data Science / |r Thilo Stadelmann, Martin Braschler, Kurt Stockinger -- |t Data Science / |r Martin Braschler, Thilo Stadelmann, Kurt Stockinger -- |t Data Scientists / |r Thilo Stadelmann, Kurt Stockinger, Gundula Heinatz Bürki, Martin Braschler -- |t Data Products / |r Jürg Meierhofer, Thilo Stadelmann, Mark Cieliebak -- |t Legal Aspects of Applied Data Science / |r Michael Widmer, Stefan Hegy -- |t Risks and Side Effects of Data Science and Data Technology / |r Clemens H. Cap -- |t Use Cases -- |t Organization / |r Martin Braschler, Thilo Stadelmann, Kurt Stockinger -- |t What Is Data Science? -- |t Michael L. Brodie -- |t On Developing Data Science / |r Michael L. Brodie --The |t Ethics of Big Data Applications in the Consumer Sector / |r Markus Christen, Helene Blumer, Christian Hauser, Markus Huppenbauer -- |t Statistical Modelling / |r Marcel Dettling, Andreas Ruckstuhl -- |t Beyond ImageNet: Deep Learning in Industrial Practice / |r Thilo Stadelmann, Vasily Tolkachev, Beate Sick, Jan Stampfli, Oliver Dürr --The |t Beauty of Small Data: An Information Retrieval Perspective / |r Martin Braschler -- |t Narrative Visualization of Open Data / |r Philipp Ackermann, Kurt Stockinger -- |t Security of Data Science and Data Science for Security / |r Bernhard Tellenbach, Marc Rennhard, Remo Schweizer -- |t Online Anomaly Detection over Big Data Streams / |r Laura Rettig, Mourad Khayati, Philippe Cudré-Mauroux, Michał Piorkówski -- |t Unsupervised Learning and Simulation for Complexity Management in Business Operations / |r Lukas Hollenstein, Lukas Lichtensteiger, Thilo Stadelmann, Mohammadreza Amirian, Lukas Budde, Jürg Meierhofer et al. -- |t Data Warehousing and Exploratory Analysis for Market Monitoring / |r Melanie Geiger, Kurt Stockinger -- |t Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning / |r Jonathan P. Leidig, Greg Wolffe -- |t Economic Measures of Forecast Accuracy for Demand Planning: A Case-Based Discussion / |r Thomas Ott, Stefan Glüge, Richard Bödi, Peter Kauf -- |t Large-Scale Data-Driven Financial Risk Assessment / |r Wolfgang Breymann, Nils Bundi, Jonas Heitz, Johannes Micheler, Kurt Stockinger -- |t Governance and IT Architecture / |r Serge Bignens, Murat Sariyar, Ernst Hafen -- |t Image Analysis at Scale for Finding the Links Between Structure and Biology / |r Kevin Mader -- |t Lessons Learned and Outlook -- |t Lessons Learned from Challenging Data Science Case Studies / |r Kurt Stockinger, Martin Braschler, Thilo Stadelmann. |
| 520 | |a "This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors - some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors' combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry."--Publisher's website. | ||
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227. | |
| 650 | 0 | |a Electronic data processing. |0 http://id.loc.gov/authorities/subjects/sh85042288. | |
| 700 | 1 | |a Braschler, Martin, |e editor. |0 http://id.loc.gov/authorities/names/no2012015441 |1 http://isni.org/isni/0000000362263423. | |
| 700 | 1 | |a Stadelmann, Thilo, |e editor. |0 http://id.loc.gov/authorities/names/no2019173102. | |
| 700 | 1 | |a Stockinger, Kurt, |e editor. |0 http://id.loc.gov/authorities/names/no2019172962 |1 http://isni.org/isni/0000000022645505. | |
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