Applied Data Science: Lessons Learned for the Data-Driven Business

✍ Scribed by Martin Braschler, Thilo Stadelmann, Kurt Stockinger

• Publisher: Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 464
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xiii
Front Matter ....Pages 1-1
Introduction to Applied Data Science (Thilo Stadelmann, Martin Braschler, Kurt Stockinger)....Pages 3-16
Data Science (Martin Braschler, Thilo Stadelmann, Kurt Stockinger)....Pages 17-29
Data Scientists (Thilo Stadelmann, Kurt Stockinger, Gundula Heinatz Bürki, Martin Braschler)....Pages 31-45
Data Products (Jürg Meierhofer, Thilo Stadelmann, Mark Cieliebak)....Pages 47-61
Legal Aspects of Applied Data Science (Michael Widmer, Stefan Hegy)....Pages 63-78
Risks and Side Effects of Data Science and Data Technology (Clemens H. Cap)....Pages 79-95
Front Matter ....Pages 97-97
Organization (Martin Braschler, Thilo Stadelmann, Kurt Stockinger)....Pages 99-100
What Is Data Science? (Michael L. Brodie)....Pages 101-130
On Developing Data Science (Michael L. Brodie)....Pages 131-160
The Ethics of Big Data Applications in the Consumer Sector (Markus Christen, Helene Blumer, Christian Hauser, Markus Huppenbauer)....Pages 161-180
Statistical Modelling (Marcel Dettling, Andreas Ruckstuhl)....Pages 181-203
Beyond ImageNet: Deep Learning in Industrial Practice (Thilo Stadelmann, Vasily Tolkachev, Beate Sick, Jan Stampfli, Oliver Dürr)....Pages 205-232
The Beauty of Small Data: An Information Retrieval Perspective (Martin Braschler)....Pages 233-250
Narrative Visualization of Open Data (Philipp Ackermann, Kurt Stockinger)....Pages 251-264
Security of Data Science and Data Science for Security (Bernhard Tellenbach, Marc Rennhard, Remo Schweizer)....Pages 265-288
Online Anomaly Detection over Big Data Streams (Laura Rettig, Mourad Khayati, Philippe Cudré-Mauroux, Michał Piorkówski)....Pages 289-312
Unsupervised Learning and Simulation for Complexity Management in Business Operations (Lukas Hollenstein, Lukas Lichtensteiger, Thilo Stadelmann, Mohammadreza Amirian, Lukas Budde, Jürg Meierhofer et al.)....Pages 313-331
Data Warehousing and Exploratory Analysis for Market Monitoring (Melanie Geiger, Kurt Stockinger)....Pages 333-351
Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning (Jonathan P. Leidig, Greg Wolffe)....Pages 353-369
Economic Measures of Forecast Accuracy for Demand Planning: A Case-Based Discussion (Thomas Ott, Stefan Glüge, Richard Bödi, Peter Kauf)....Pages 371-386
Large-Scale Data-Driven Financial Risk Assessment (Wolfgang Breymann, Nils Bundi, Jonas Heitz, Johannes Micheler, Kurt Stockinger)....Pages 387-408
Governance and IT Architecture (Serge Bignens, Murat Sariyar, Ernst Hafen)....Pages 409-423
Image Analysis at Scale for Finding the Links Between Structure and Biology (Kevin Mader)....Pages 425-443
Front Matter ....Pages 445-445
Lessons Learned from Challenging Data Science Case Studies (Kurt Stockinger, Martin Braschler, Thilo Stadelmann)....Pages 447-465

✦ Subjects

Computer Science; Data Mining and Knowledge Discovery; Big Data/Analytics; Information Storage and Retrieval