| Tag |
First Indicator |
Second Indicator |
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| LEADER |
00000cam a22000007a 4500 |
| 001 |
in00004670421 |
| 006 |
m o d |
| 007 |
cr cnu|||||||| |
| 008 |
081221s2021 xx o 000 0 eng d |
| 005 |
20260123213208.4 |
| 020 |
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|z 9781617298899
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| 024 |
8 |
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|a 9781617298899
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| 035 |
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|a (OCoLC)1288628699
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| 037 |
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|a 9781617298899
|b O'Reilly Media
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| 040 |
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|a AU@
|b eng
|e pn
|c AU@
|d EBLCP
|d ORMDA
|d OCLCO
|d OCLCF
|d OCLCQ
|d TOH
|d OCLCQ
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| 049 |
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|a TXAM
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| 050 |
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4 |
|a HD30.2
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| 082 |
0 |
4 |
|a 004.068
|2 23
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| 100 |
1 |
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|a Chong, Jike,
|e author.
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| 245 |
1 |
0 |
|a How to Lead in Data Science /
|c Chong, Jike.
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| 250 |
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|a 1st edition.
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| 264 |
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1 |
|b Manning Publications,
|c 2021.
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| 300 |
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|a 1 online resource (512 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
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| 520 |
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|a How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It's filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You'll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you'll build practical skills to grow and improve your team, your company's data culture, and yourself.
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| 542 |
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|f © 2021 Manning Publications Co. All rights reserved.
|g 2021
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| 550 |
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|a Made available through: Safari, an O'Reilly Media Company.
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| 588 |
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|a Online resource; Title from title page (viewed December 7, 2021)
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| 505 |
0 |
|
|a Intro -- inside front cover -- How to Lead in Data Science -- Copyright -- dedication -- brief contents -- contents -- front matter -- foreword -- preface -- References -- acknowledgments -- about this book -- Who should read this book -- How this book is organized -- Self-assessment and development focus -- Case studies -- Gem insights -- liveBook discussion forum -- about the authors -- about the cover illustration -- 1 What makes a successful data scientist? -- 1.1 Data scientist expectations -- 1.1.1 The Venn diagram a decade later -- 1.1.2 What is missing?
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| 505 |
8 |
|
|a 1.1.3 Understanding ability and motivation: Assessing capabilities and virtues -- 1.2 Career progression in data science -- 1.2.1 Interview and promotion woes -- 1.2.2 What are (hiring) managers looking for? -- Summary -- References -- Part 1. The tech lead: Cultivating leadership -- 2 Capabilities for leading projects -- 2.1 Technology: Tools and skills -- 2.1.1 Framing the problem to maximize business impact -- 2.1.2 Discovering patterns in data -- 2.1.3 Setting expectations for success -- 2.2 Execution: Best practices -- 2.2.1 Specifying and prioritizing projects from vague requirements
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| 505 |
8 |
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|a 2.2.2 Planning and managing data science projects -- 2.2.3 Striking a balance between trade-offs -- 2.3 Expert knowledge: Deep domain understanding -- 2.3.1 Clarifying business context of opportunities -- 2.3.2 Accounting for domain data source nuances -- 2.3.3 Navigating organizational structure -- 2.4 Self-assessment and development focus -- 2.4.1 Understanding your interests and leadership strengths -- 2.4.2 Practicing with the CPR process -- 2.4.3 Developing a prioritize, practice, and perform plan -- 2.4.4 Note for DS tech lead managers -- Summary -- References
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| 505 |
8 |
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|a 3 Virtues for leading projects -- 3.1 Ethical standards of conduct -- 3.1.1 Operating in the customers' best interest -- 3.1.2 Adapting to business priorities in dynamic business environments -- 3.1.3 Imparting knowledge confidently -- 3.2 Rigor cultivation, higher standards -- 3.2.1 Getting clarity on the fundamentals of scientific rigor -- 3.2.2 Monitoring for anomalies in data and in deployment -- 3.2.3 Taking responsibility for enterprise value -- 3.3 Attitude of positivity -- 3.3.1 Exhibiting positivity and tenacity to work through failures
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| 505 |
8 |
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|a 3.3.2 Being curious and collaborative in responding to incidents -- 3.3.3 Respecting diverse perspectives in lateral collaborations -- 3.4 Self-assessment and development focus -- 3.4.1 Understanding your interests and leadership strengths -- 3.4.2 Practicing with the CPR process -- 3.4.3 Self-coaching with the GROW model -- 3.4.4 Note for DS tech lead managers -- Summary -- References -- Part 2. The manager: Nurturing a team -- Reference -- 4 Capabilities for leading people -- 4.1 Technology: Tools and skills -- 4.1.1 Delegating projects effectively
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| 650 |
|
0 |
|a Information technology
|x Management.
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| 650 |
|
0 |
|a Database management.
|
| 650 |
|
0 |
|a Leadership.
|
| 650 |
|
2 |
|a Leadership
|
| 650 |
|
6 |
|a Technologie de l'information
|x Gestion.
|
| 650 |
|
6 |
|a Bases de données
|x Gestion.
|
| 650 |
|
6 |
|a Leadership.
|
| 650 |
|
7 |
|a Database management.
|2 fast
|0 (OCoLC)fst00888037
|
| 650 |
|
7 |
|a Information technology
|x Management.
|2 fast
|0 (OCoLC)fst00973112
|
| 650 |
|
7 |
|a Leadership.
|2 fast
|0 (OCoLC)fst00994701
|
| 700 |
1 |
|
|a Chang, Yue,
|e author.
|
| 710 |
2 |
|
|a Safari, an O'Reilly Media Company.
|
| 856 |
4 |
0 |
|u https://proxy.library.tamu.edu/login?url=https://go.oreilly.com/TAMU/library/view/-/9781617298899/?ar
|z Connect to the full text of this electronic book
|t 0
|
| 955 |
|
|
|a Safari/O'Reilly ebooks
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
f |
f |
|s d057f9d4-82ce-4d91-a0de-f5d0872af271
|i 6e0ebb08-239c-4064-94e5-0909b3f52c89
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|e HD30.2
|h Library of Congress classification
|
| 998 |
f |
f |
|a HD30.2
|t 0
|l Available Online
|