How to Lead in Data Science

How to Lead in Data Science (Sách keo gáy, bìa mềm)

A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: • 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 author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook

Thể loại:Computers - Computer Science

Năm:2021

In lần thứ:1

Ngôn ngữ:english

Trang:514