Carl Shan Henry Wang William Chen - The Data Science Handbook

In the past five years, data science has made an impact in almost every major area of human endeavour. From commerce, to education, to energy, and of course, software and the Internet, data science has created immense value across the world. In fact, in early 2015 the President of the United States announced the new role of Chief Data Scientist to the White House and appointed DJ Patil, one of the interviewees in this book, to the position.

Like many innovations in the world, the birth of this industry was started by a few motivated people. Over the last few years, they founded, developed and advocated for the value that data analytics can bring to every industry. In The Data Science Handbook, you will have the opportunity to meet many of these founding data scientists, hear first hand accounts of the incredible journeys they took, and read where they believe the field is headed.

The road to becoming a data scientist is not always an easy one. When I tried to transition from experimental particle physics to industry, resources were few and far between. In fact, although a need for data science existed in companies, the job title had not even been created. I spent a lot of time teaching myself, working on various startup projects, and later saw many of my friends from academia run into the same challenges.

I observed a groundswell of incredibly gifted and highly trained researchers who were excited about moving into data-driven roles, yet were missing key pieces of knowledge about how to do so. As a result, they had trouble transferring their incredible quantitative and data analysis research skills to a career in industry. Meanwhile, having lived and worked in Silicon Valley, I also saw that there was very strong demand from technology companies who wanted to hire these exact people.

In my personal journey to both entering the technology field as well as creating a community for others to do the same, one key resource I found to be tremendously useful was conversations with those who had successfully made the transition. As I developed Insight, I have had the chance to engage with some of Silicon Valleys best data scientists who are mentors to the program:

Jonathan Goldman created one of the first data products at LinkedInPeople You May Knowwhich transformed the growth trajectory of the company. DJ Patil built and grew the data science team at LinkedIn into a powerhouse and co-coined the term Data Scientist. Riley Newman worked on developing product analytics that was instrumental in Airbnbs growth. Jace Kohlmeier led the data team at Khan Academy that helped optimize learning for millions of students.

Unfortunately, its hard to get face-to-face time with these remarkable people. At Insight, to maintain an exceptionally high quality and personal time with these mentors, we select only a small group of talented scientists and engineers three times per year.

However, The Data Science Handbook provides readers with a way to have these in-depth conversation at scale.

By reading the interviews in The Data Science Handbook, you will have the experience of learning from the leaders in data science at your own pace, no matter where you are in the world. Each interview is an in-depth conversation, covering the personal stories of these data scientists from their initial experiences that helped them find their own path to a career in data science.

Its not just the early data science leaders who can have a big impact on the field. There is also new talent entering, with the opportunity for each and every new member to push the field forward. When I met the authors of this book, they were still college students and aspiring data scientists, full of the same questions that those beginning in data science have.

Through 18 months of hard work, they have done the legwork in seeking out some of the best data scientists around the country, and asking them for their advice and guidance. This book is the result of that work, containing over 100 hours of collected wisdom with people otherwise inaccessible to most of us (imagine having to compete with President Obama to talk with DJ Patil!).

By reading these extended, informal interviews, you will get to sit down with industry trailblazers like DJ Patil, Jonathan Goldman and Pete Skomoroch, who were all part of the early, core LinkedIn data science teams. You will meet with Hilary Mason and Drew Conway, who were instrumental in creating the thriving New York data science community. You will hear advice from the next generation of data science leaders, like Diane Wu and Chris Moody, both Insight Alumni, who are now blazing new trails at MetaMinds and Stitch Fix.

You will meet data scientists who are having a big impact in academia, including Bradley Voytek from UC San Diego and Joe Blitzstein from Harvard. You will meet data scientists in startups, such as Clare Corthell from Mattermark and Kunal Punera of Bento Labs, who will share how they use data science as a core competitive advantage.

The data scientists in the Data Science Handbook, along with dozens of others, have helped create the very industry that is now having such a tremendous impact on the world. Here in this book, they discuss the mindset that allowed them to create this industry, address misconceptions about the field, share stories of specific challenges and victories, and talk about what they look for when building their teams.

I hope that by reading their stories, hearing how they think, and learning their vision for the future of data science, you will come to think of ways you can have an impact, and perhaps even advance the field yourself.

Jake Klamka

Founder

Welcome to The Data Science Handbook!

In the following pages, you will find in-depth interviews with 25 remarkable data scientists. They hail from a wide selection of backgrounds, disciplines, and industries. Some of them, like DJ Patil and Hilary Mason, were part of the trailblazing wave of data scientists who catapulted the field into national attention. Others are at the start of their careers, such as Clare Corthell, who made her own path to data science by creating the Open Source Data Science Masters, a self-guided curriculum built on freely available internet resources.