MARC details
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04903cam a22003615i 4500 |
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10062498 |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN |
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20260511121914.0 |
| 006 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA--CARACTERÍSTICAS DEL MATERIAL ADICIONAL |
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| 007 - CAMPO FIJO DE DESCRIPCIÓN FÍSICA--INFORMACIÓN GENERAL |
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191016s2019 gw | o |||| 0|eng d |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO |
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| Número Internacional Estándar del Libro |
9783030118211 |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO |
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| Número Internacional Estándar del Libro |
9783030118204 |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO |
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| Número Internacional Estándar del Libro |
9783030118228 |
| 024 7# - IDENTIFICADOR DE OTROS ESTÁNDARES |
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| Número estándar o código |
10.1007/978-3-030-11821-1 |
| Fuente del número o código |
doi |
| 035 ## - NÚMERO DE CONTROL DEL SISTEMA |
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| Número de control de sistema |
(WaSeSS)ssj0002206515 |
| 040 ## - FUENTE DE LA CATALOGACIÓN |
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| Centro/agencia modificador |
WaSeSS |
| Centro/agencia transcriptor |
tbs |
| 041 ## - CÓDIGO DE LENGUA |
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| Código de lengua del texto/banda sonora o título independiente |
English |
| 050 #4 - SIGNATURA TOPOGRÁFICA DE LA BIBLIOTECA DEL CONGRESO |
|---|
| Número de clasificación |
QA76.9.D343 |
| 245 10 - MENCIÓN DE TÍTULO |
|---|
| Título |
Applied Data Science |
| Resto del título |
: Lessons Learned for the Data-Driven Business |
| Mención de responsabilidad, etc. |
/ edited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger. |
| 250 ## - MENCIÓN DE EDICIÓN |
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| Mención de edición |
First edition |
| 264 #1 - PRODUCCIÓN, PUBLICACIÓN, DISTRIBUCIÓN, FABRICACIÓN Y COPYRIGHT |
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| Producción, publicación, distribución, fabricación y copyright |
Cham : |
| Nombre del de productor, editor, distribuidor, fabricante |
Springer International Publishing : |
| -- |
Imprint: Springer, |
| Fecha de producción, publicación, distribución, fabricación o copyright |
2019. |
| 505 0# - NOTA DE CONTENIDO CON FORMATO |
|---|
| Nota de contenido con formato |
Foundations — 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 ## - NOTA DE RESTRICCIONES AL ACCESO |
|---|
| Limitaciones de acceso |
Access may be restricted to institutions with a site license. |
| 520 ## - SUMARIO, ETC. |
|---|
| Sumario, etc. |
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. <br/><br/>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. <br/><br/>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 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Data mining |
| 9 (RLIN) |
7979 |
| 650 #0 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Machine learning |
| 9 (RLIN) |
1129 |
| 650 #0 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Big data |
| 9 (RLIN) |
3389 |
| 650 #0 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Information storage and retrieval. |
| 9 (RLIN) |
26867 |
| 700 ## - ENTRADA AGREGADA--NOMBRE PERSONAL |
|---|
| Nombre de persona |
Braschler, Martin. |
| 9 (RLIN) |
26868 |
| 700 ## - ENTRADA AGREGADA--NOMBRE PERSONAL |
|---|
| Nombre de persona |
Stadelmann, Thilo. |
| 9 (RLIN) |
26869 |
| 700 ## - ENTRADA AGREGADA--NOMBRE PERSONAL |
|---|
| Nombre de persona |
Stockinger, Kurt. |
| 9 (RLIN) |
26870 |
| 942 ## - ELEMENTOS DE ENTRADA AGREGADA (KOHA) |
|---|
| Fuente del sistema de clasificación o colocación |
Clasificación de Library of Congress |
