front matter

 

foreword

Over the past decade, I chaired or co-chaired more than 40 premier data and AI conferences internationally. It has been amazing to witness the evolution and impact of analytics, data science, and machine learning worldwide. Data science continues to be one of the fastest-growing job functions in the industry today. When I was the chief data scientist of O’Reilly Media, study after study we conducted confirmed that companies continue to invest in data infrastructure, data science, and machine learning. We also found the companies that excel in using data science and machine learning were the ones that invested in foundational technologies and used those tools to expand their capabilities gradually, one use case at a time.

While much of what we read about pertains to tools or breakthroughs in models, the reality is that organizational issues pose some of the major bottlenecks within most companies. The critical ingredient is recognizing organizational excellence in people, culture, and structure. If you don’t have the right people and organizational structure in place, you will still underperform competitors that do.

As demand for data scientists continues to grow and training programs proliferate, I am frequently asked for advice. Novices ask how they can join the ranks of data scientists, and more experienced data scientists ask for pointers on how they can take their careers to the next level.

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