Become a Leader in Data Science
Jike Chong and Yue Cathy Chang
  • MEAP began October 2020
  • Publication in Summer 2021 (estimated)
  • ISBN 9781617298899
  • 300 pages (estimated)
  • printed in black & white
A practical field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.

In Become a Leader in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away.

About the Technology

Leading a data science team takes more than the typical set of business management skills. You need specific know-how to articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. Whether you're looking to manage your team better or work towards a seat at your company’s top leadership table, this book will show you how.

About the book

Become a Leader in Data Science teaches strategies for thriving in data leadership and advancing your career in this competitive field. In this practical guide, authors Jike Chong and Yue Cathy Chang share leadership techniques that they've developed building multiple high-performance data teams.

You'll learn best practices for balancing trade-offs, planning projects from vague requirements, and navigating roadblocks in company management structure. Blending insights from the author's hard-won experience with the timeless teachings of Aristotle and Confucius, this book will help you cultivate yourself, your team, and your company's data culture—all while driving the impact of data science in your organization. Throughout, carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and show development areas to help advance your career.
Table of Contents detailed table of contents

1 What’s in a Successful Data Scientist

1.1 Data Scientist Expectations

1.1.1 The Venn Diagram a Decade After

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?

1.3 Summary

Part 1: Tech Lead - Cultivating Leadership

2 Capabilities for Leading Projects

2.1 Technology

2.1.1 Framing the Problem

2.1.2 Discover Patterns in Data

2.1.3 Set Expectations for Success

2.2 Execution

2.2.1 Specifying Projects from Vague Requirements

2.2.2 Planning a Data Science Project

2.2.3 Striking the Balance

2.3 Expert Knowledge

2.3.1 Clarify Business Context and Craft Narrative

2.3.2 Account for Domain Data Source Nuances

2.3.3 Navigate Organizational Structure

2.4 Self-assessment

2.5 Summary

3 Virtues for Leading Projects

3.1 Ethical Standards of Conduct

3.1.1 Operate in Customers’ Best Interest

3.1.2 Adapt to Dynamic Business Environments

3.1.3 Confidently Impart Knowledge

3.2 Rigorous Cultivation, Higher Standards

3.2.1 The Foundation of Scientific Rigor

3.2.2 Monitor for Anomalies

3.2.3 Take Responsibility for Enterprise Value

3.3 Attitude of Positivity

3.3.1 Positivity and Tenacity to Work Through Failures

3.3.2 Curiosity and Collaboration in Responding to Incidents

3.3.3 Respect Diverse Perspectives in Lateral Collaborations

3.4 Self-assessment

3.5 Summary

Part 2. Manager - Nurturing a team

4 Capabilities for Leading People

5 Virtues for Leading People

Part 3: Director - Governing a function

6 Capabilities for Leading a Function

7 Virtues for Leading a Function

Part 4: Executive - Inspiring an industry

8 Capabilities for Leading a Company

9 Virtues for Leading a Company

Part 5: The LOOP

10 The LOOP – Landscape, Organization, Opportunity, and Practice

11 Data Science, Quantifying Tao, and a Future Outlook

What's inside

  • Best practices for leading projects while balancing complex trade-offs
  • Specifying, prioritizing, and planning projects from vague requirements
  • Navigating structural challenges in your organization
  • Working through project failures with positivity and tenacity
  • Growing your team with coaching, mentoring, and advising
  • Crafting technology roadmaps and championing successful projects
  • Driving diversity, inclusion, and belonging within teams
  • Architecting a long-term business strategy and data roadmap as an executive
  • Delivering a data-driven culture and structuring productive data science organizations

About the reader

For data practitioners looking to take their careers into project and people leadership, up to the executive level.

About the authors

Dr. Jike Chong and Yue Cathy Chang have both built, led, and grown multiple high-performing data teams. Dr. Chong developed the Yiren Digital Ltd. data team from the ground up, expanded and led the data team as chief data scientist at Acorns, and recently led the Hiring Marketplace Data Science team at LinkedIn. Yue Cathy Chang and her team "parachuted" into companies to address challenging and meaningful data needs. She has worked with leaders of centralized and distributed data teams at organizations big and small, including large asset-management firms and Fortune 50 companies.

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