11 Leading in data science and a future outlook

 

This chapter covers

  • Clarifying four reasons why leading in DS is increasingly important
  • Summarizing what we can learn when building a career
  • Mastering how to practice leading in DS
  • Anticipating future roles, capabilities, and responsibilities in building trust and pursuing a career

As human beings, we understand our world through what we see with our eyes, with what we detect with machines, and with reasoning, deduction, and hypotheses that can be tested against reality. DS helps us drive a quantitative understanding of the world around us through reasoning, deduction, and hypotheses that can be tested against reality. It provides a data lens with which we interpret and anticipate the ways the world works. This book aggregates the top capabilities and virtues for successfully leading efforts in DS for the purpose of better understanding and influencing our world.

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Data science provides a data lens with which we can interpret and anticipate the ways the world works. It drives a quantitative understanding through reasoning, deduction, and hypotheses that can be tested against reality.

Leading in DS is challenging, as it involves a broad skill set that can take time to internalize. This skill set is shaped by the unique challenges of working with data and coordinating with executives, teams, and partners to make DS efforts successful.

11.1 The why, what, and how of leading in DS

11.1.1 Why is learning to lead in DS increasingly important?

11.1.2 What is a framework for leading in DS?

11.1.3 How to use the framework in practice?

11.2 The future outlook

11.2.1 The role: The emergence of data product managers

11.2.2 The capability: The availability of function-specific data solutions

11.2.3 The responsibility: Instilling trust in data

Summary