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
到查詢結果
[ subject:"Electronic data processing departments- Management." ]
切換:
標籤
|
MARC模式
|
ISBD
Data teams = a unified management mo...
~
Anderson, Jesse.
FindBook
Google Book
Amazon
博客來
Data teams = a unified management model for successful data-focused teams /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Data teams/ by Jesse Anderson.
其他題名:
a unified management model for successful data-focused teams /
作者:
Anderson, Jesse.
出版者:
Berkeley, CA :Apress : : 2020.,
面頁冊數:
xxiv, 294 p. :ill., digital ;24 cm.
內容註:
Part 1: Introducing Data Teams -- Chapter 1: Data Teams -- Chapter 2: The Good, the Bad, and the Ugly Data Teams -- Part 2: Building Your Data Team -- Chapter 3: The Data Science Team -- Chapter 4: The Data Engineering Team -- Chapter 5: The Operations Team -- Chapter 6: Specialized Staff -- Part 3: Working Together and Managing the Data Teams -- Chapter 7: Working as a Data Team -- Chapter 8: How the Business Interacts with Data Teams -- Chapter 9: Managing Big Data Projects -- Chapter 10: Starting a Team -- Chapter 11: The Steps for Successful Big Data Projects -- Chapter 12: Organizational Changes -- Chapter 13: Diagnosing and Fixing Problems -- Part 4: Case Studies and Interviews -- Chapter 14: Interview with Eric Colson and Brad Klingenberg, Stitch Fix -- Chapter 15: Interview with Dmitriy Ryaboy, Twitter, Cloudera, Zymergen -- Chapter 16: Interview with Bas Geerdink, ING, Rabobank -- Chapter 17: Interview with Harvinder Atwal, Moneysupermarket -- Chapter 18: Interview with a Large British Telecommunications Company -- Chapter 19: Interview with Mikio Braun, Zalando.
Contained By:
Springer Nature eBook
標題:
Big data. -
電子資源:
https://doi.org/10.1007/978-1-4842-6228-3
ISBN:
9781484262283
Data teams = a unified management model for successful data-focused teams /
Anderson, Jesse.
Data teams
a unified management model for successful data-focused teams /[electronic resource] :by Jesse Anderson. - Berkeley, CA :Apress :2020. - xxiv, 294 p. :ill., digital ;24 cm.
Part 1: Introducing Data Teams -- Chapter 1: Data Teams -- Chapter 2: The Good, the Bad, and the Ugly Data Teams -- Part 2: Building Your Data Team -- Chapter 3: The Data Science Team -- Chapter 4: The Data Engineering Team -- Chapter 5: The Operations Team -- Chapter 6: Specialized Staff -- Part 3: Working Together and Managing the Data Teams -- Chapter 7: Working as a Data Team -- Chapter 8: How the Business Interacts with Data Teams -- Chapter 9: Managing Big Data Projects -- Chapter 10: Starting a Team -- Chapter 11: The Steps for Successful Big Data Projects -- Chapter 12: Organizational Changes -- Chapter 13: Diagnosing and Fixing Problems -- Part 4: Case Studies and Interviews -- Chapter 14: Interview with Eric Colson and Brad Klingenberg, Stitch Fix -- Chapter 15: Interview with Dmitriy Ryaboy, Twitter, Cloudera, Zymergen -- Chapter 16: Interview with Bas Geerdink, ING, Rabobank -- Chapter 17: Interview with Harvinder Atwal, Moneysupermarket -- Chapter 18: Interview with a Large British Telecommunications Company -- Chapter 19: Interview with Mikio Braun, Zalando.
Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. You will: Discover the three teams that you will need to be successful with big data Understand what a data scientist is and what a data science team does Understand what a data engineer is and what a data engineering team does Understand what an operations engineer is and what an operations team does Know how the teams and titles differ and why you need all three teams Recognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects.
ISBN: 9781484262283
Standard No.: 10.1007/978-1-4842-6228-3doiSubjects--Topical Terms:
2045508
Big data.
LC Class. No.: QA76.9.B45
Dewey Class. No.: 005.7
Data teams = a unified management model for successful data-focused teams /
LDR
:03609nmm a2200325 a 4500
001
2243459
003
DE-He213
005
20201231133637.0
006
m d
007
cr nn 008maaau
008
211207s2020 cau s 0 eng d
020
$a
9781484262283
$q
(electronic bk.)
020
$a
9781484262276
$q
(paper)
024
7
$a
10.1007/978-1-4842-6228-3
$2
doi
035
$a
978-1-4842-6228-3
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA76.9.B45
072
7
$a
UN
$2
bicssc
072
7
$a
COM021000
$2
bisacsh
072
7
$a
UN
$2
thema
082
0 4
$a
005.7
$2
23
090
$a
QA76.9.B45
$b
A545 2020
100
1
$a
Anderson, Jesse.
$3
3503519
245
1 0
$a
Data teams
$h
[electronic resource] :
$b
a unified management model for successful data-focused teams /
$c
by Jesse Anderson.
260
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2020.
300
$a
xxiv, 294 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
Part 1: Introducing Data Teams -- Chapter 1: Data Teams -- Chapter 2: The Good, the Bad, and the Ugly Data Teams -- Part 2: Building Your Data Team -- Chapter 3: The Data Science Team -- Chapter 4: The Data Engineering Team -- Chapter 5: The Operations Team -- Chapter 6: Specialized Staff -- Part 3: Working Together and Managing the Data Teams -- Chapter 7: Working as a Data Team -- Chapter 8: How the Business Interacts with Data Teams -- Chapter 9: Managing Big Data Projects -- Chapter 10: Starting a Team -- Chapter 11: The Steps for Successful Big Data Projects -- Chapter 12: Organizational Changes -- Chapter 13: Diagnosing and Fixing Problems -- Part 4: Case Studies and Interviews -- Chapter 14: Interview with Eric Colson and Brad Klingenberg, Stitch Fix -- Chapter 15: Interview with Dmitriy Ryaboy, Twitter, Cloudera, Zymergen -- Chapter 16: Interview with Bas Geerdink, ING, Rabobank -- Chapter 17: Interview with Harvinder Atwal, Moneysupermarket -- Chapter 18: Interview with a Large British Telecommunications Company -- Chapter 19: Interview with Mikio Braun, Zalando.
520
$a
Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. You will: Discover the three teams that you will need to be successful with big data Understand what a data scientist is and what a data science team does Understand what a data engineer is and what a data engineering team does Understand what an operations engineer is and what an operations team does Know how the teams and titles differ and why you need all three teams Recognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects.
650
0
$a
Big data.
$3
2045508
650
0
$a
Database management.
$3
527442
650
0
$a
Electronic data processing departments
$x
Management.
$3
692238
650
1 4
$a
Big Data.
$3
3134868
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/978-1-4842-6228-3
950
$a
Business and Management (SpringerNature-41169)
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
W9404505
電子資源
11.線上閱覽_V
電子書
EB QA76.9.B45
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