LEADER 05933nam 22005775 450 001 9910337843503321 005 20200723170258.0 010 $a3-030-11821-5 024 7 $a10.1007/978-3-030-11821-1 035 $a(CKB)4100000008424366 035 $a(MiAaPQ)EBC5789413 035 $a(DE-He213)978-3-030-11821-1 035 $a(PPN)26030428X 035 $a(EXLCZ)994100000008424366 100 $a20190613d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Data Science $eLessons Learned for the Data-Driven Business /$fedited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (464 pages) 311 $a3-030-11820-7 327 $aPreface -- 1 Introduction -- 2 Data Science -- 3 Data Scientists -- 4 Data products -- 5 Legal Aspects of Applied Data Science -- 6 Risks and Side Effects of Data Science and Data Technology -- 7 Organization -- 8 What is Data Science? -- 9 On Developing Data Science -- 10 The ethics of Big Data applications in the consumer sector -- 11 Statistical Modelling -- 12 Beyond ImageNet - Deep Learning in Industrial Practice -- 13 THE BEAUTY OF SMALL DATA - AN INFORMATION RETRIEVAL PERSPECTIVE -- 14 Narrative Visualization of Open Data -- 15 Security of Data Science and Data Science for Security -- 16 Online Anomaly Detection over Big Data Streams -- 17 Unsupervised Learning and Simulation for Complexity Management in Business Operations -- 18 Data Warehousing and Exploratory Analysis for Market Monitoring -- 19 Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning -- 20 Economic Measures of Forecast Accuracy for Demand Planning - A Case-Based Discussion -- 21 Large-Scale Data-Driven Financial Risk Assessment -- 22 Governance and IT Architecture -- 23 Image Analysis at Scale for Finding the Links between Structure and Biology -- 24 Lessons Learned from Challenging Data Science Case Studies. . 330 $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. . 606 $aData mining 606 $aMachine learning 606 $aBig data 606 $aInformation storage and retrieval 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 615 0$aData mining. 615 0$aMachine learning. 615 0$aBig data. 615 0$aInformation storage and retrieval. 615 14$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 615 24$aBig Data/Analytics. 615 24$aInformation Storage and Retrieval. 676 $a338.47006312 702 $aBraschler$b Martin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aStadelmann$b Thilo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aStockinger$b Kurt$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910337843503321 996 $aApplied Data Science$92508174 997 $aUNINA