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006			m d
007			cr n
008			191016s2019 gw | o |||| 0|eng d
020			_a9783030118211
020			_a9783030118204
020			_a9783030118228
024	7		_a10.1007/978-3-030-11821-1 _2doi
035			_a(WaSeSS)ssj0002206515
040			_dWaSeSS _ctbs
041			_aEnglish
050		4	_aQA76.9.D343
245	1	0	_aApplied Data Science _b: Lessons Learned for the Data-Driven Business _c/ edited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger.
250			_aFirst edition
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019.
505	0		_aFoundations — Introduction to Applied Data Science — Data Science — Data Scientists — Data Products — Legal Aspects of Applied Data Science — Risks and Side Effects of Data Science and Data Technology — Use Cases — Organization — What Is Data Science? — On Developing Data Science — The Ethics of Big Data Applications in the Consumer Sector — Statistical Modelling — Beyond ImageNet: Deep Learning in Industrial Practice — The Beauty of Small Data: An Information Retrieval Perspective — Narrative Visualization of Open Data — Security of Data Science and Data Science for Security — Online Anomaly Detection over Big Data Streams — Unsupervised Learning and Simulation for Complexity Management in Business Operations — Use Cases — Data Warehousing and Exploratory Analysis for Market Monitoring — Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning — Economic Measures of Forecast Accuracy for Demand Planning: A Case-Based Discussion — Large-Scale Data-Driven Financial Risk Assessment — Governance and IT Architecture — Image Analysis at Scale for Finding the Links Between Structure and Biology — Lessons Learned and Outlook — Lessons Learned from Challenging Data Science Case Studies.
506			_aAccess may be restricted to institutions with a site license.
520			_aThis 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.
650		0	_aData mining _97979
650		0	_aMachine learning _91129
650		0	_aBig data _93389
650		0	_aInformation storage and retrieval. _926867
700			_aBraschler, Martin. _926868
700			_aStadelmann, Thilo. _926869
700			_aStockinger, Kurt. _926870
942			_2lcc
999			_c5411 _d5411