How to Lead in Data Science /

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 p...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Chong, Jike (Autor), Chang, Yue (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Manning Publications, 2021.
Edición:1st edition.
Temas:
Information technology > Management.
Database management.
Leadership.
Leadership
Technologie de l'information > Gestion.
Bases de données > Gestion.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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?
  • 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
  • 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
  • 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
  • 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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