語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Applied data science = lessons learn...
~
Braschler, Martin.
FindBook
Google Book
Amazon
博客來
Applied data science = lessons learned for the data-driven business /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Applied data science/ edited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger.
其他題名:
lessons learned for the data-driven business /
其他作者:
Braschler, Martin.
出版者:
Cham :Springer International Publishing : : 2019.,
面頁冊數:
xiii, 465 p. :ill., digital ;24 cm.
內容註:
Preface -- 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.
Contained By:
Springer eBooks
標題:
Big data. -
電子資源:
https://doi.org/10.1007/978-3-030-11821-1
ISBN:
9783030118211
Applied data science = lessons learned for the data-driven business /
Applied data science
lessons learned for the data-driven business /[electronic resource] :edited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger. - Cham :Springer International Publishing :2019. - xiii, 465 p. :ill., digital ;24 cm.
Preface -- 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.
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.
ISBN: 9783030118211
Standard No.: 10.1007/978-3-030-11821-1doiSubjects--Topical Terms:
2045508
Big data.
LC Class. No.: QA76.9.B45 / A66 2019
Dewey Class. No.: 005.7
Applied data science = lessons learned for the data-driven business /
LDR
:04741nmm a2200337 a 4500
001
2191991
003
DE-He213
005
20190627141341.0
006
m d
007
cr nn 008maaau
008
200506s2019 gw s 0 eng d
020
$a
9783030118211
$q
(electronic bk.)
020
$a
9783030118204
$q
(paper)
024
7
$a
10.1007/978-3-030-11821-1
$2
doi
035
$a
978-3-030-11821-1
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA76.9.B45
$b
A66 2019
072
7
$a
UNF
$2
bicssc
072
7
$a
COM021030
$2
bisacsh
072
7
$a
UNF
$2
thema
072
7
$a
UYQE
$2
thema
082
0 4
$a
005.7
$2
23
090
$a
QA76.9.B45
$b
A652 2019
245
0 0
$a
Applied data science
$h
[electronic resource] :
$b
lessons learned for the data-driven business /
$c
edited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
xiii, 465 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
Preface -- 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.
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.
650
0
$a
Big data.
$3
2045508
650
1 4
$a
Data Mining and Knowledge Discovery.
$3
898250
650
2 4
$a
Machine Learning.
$3
3382522
650
2 4
$a
Big Data/Analytics.
$3
2186785
650
2 4
$a
Information Storage and Retrieval.
$3
761906
700
1
$a
Braschler, Martin.
$3
1567589
700
1
$a
Stadelmann, Thilo.
$3
3411832
700
1
$a
Stockinger, Kurt.
$3
3411833
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
856
4 0
$u
https://doi.org/10.1007/978-3-030-11821-1
950
$a
Computer Science (Springer-11645)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9374587
電子資源
11.線上閱覽_V
電子書
EB QA76.9.B45 A66 2019
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入