It’s always incredible to see your kids go on to great things. We are experiencing just that with our own, Max Song. Max came to us from Brown a few years ago and made material impact on the organization working on two client facing financial projects, despite not having graduated from college yet. His experiences here have created a new chapter in his career. Specifically, Max recently co-authored a book of interviews with a few other prominent data scientists in the field.
The journey continues with his recent book, The Data Science Handbook, published today on Amazon.
I was able to interview Max to get the inside scoop on the purpose of the The Data Science Handbook and where the authors got their inspiration from.
What follows is an edited summary of our email correspondence.
When they decided to write The Data Science Handbook, the authors were just beginning their careers in the field of data science. Max was joining Ayasdi, taking a sojourn from Brown after his junior year, William Chen was starting at Quora, Carl Shan was starting a data science fellowship, and Henry Wang was starting at a sovereign wealth investment firm building quantitative models.
At the time, “big data” and data science were growing in popularity in the tech community, but the resources for people to really dive into making a career in data science were few and far in between. Unsatisfied with the existing answers to the questions that they were curious and hungry about, the authors decided to take it upon themselves to create a compelling resource for others like themselves. They sought to dig deeper into the everyday life of data scientists and find out more about what the career path really entails.
The audience for the book started out as graduate students who enjoyed the experience of research but didn’t see themselves in the fiercely competitive world of academia. As time went on, the authors realized that the advice they were collecting did not just stop at “getting in the door” of data science, but also had value for those at a variety of career stages.
Through in depth interviews with senior data scientists, they were able to answer questions such as “How do you continue to learn on the job” and “What technologies and mindsets are important to learn”, as well as higher order questions of “How do you build a world class data science team?” to “How do you collaborate effectively with product and engineering?” and “What interesting entrepreneurship opportunities exist?”
There are some pretty big names featured in The Data Science Handbook including DJ Patil, the first U.S. Chief Data Scientist. DJ gives some insight on the team he led at LinkedIn and the mindset he expected his new data scientists to possess. He touches on how you must be prepared to be the first ones in the office and the last ones out, in order to “accelerate the learning curve.”
George Roumeliotis, of Intuit, offers advice for the senior leads in data science on how communication is crucial, especially in a field where you aren’t necessarily hired based on your verbal proficiency. He explains the challenges of creating detailed presentations that include axioms, comprehensive steps, and end results, only to be overlooked and flipped through for the conclusions. George notes that business leaders care more about the punchlines and trust the data scientists to do the reasoning, so your presentations will be more effective if they start with the “bottom line”, showing the thought process only when questioned.
Finally, interwoven through all of the narratives is an undercurrent of seeking new frontiers. Many of the prominent data scientists in the industry didn’t know they wanted to be data scientists growing up. Some of them stumbled into the field after exploring adjacent opportunities in academia, taking advantage of the field’s infancy to be the first to make landfall on many different territories.
Similarly, young data scientists should have an adventuring spirit and be willing to explore the unexplored. Data science has proven itself again and again in recommendation engines, advertising and other applications, but the real adventure is just beginning. Max and his fellow authors hope that the book will inspire young data scientists to seek out new opportunities peeking over the horizon.
The authors are careful to enumerate all of the challenges of this emerging field. “Doing the data science tango” involves juggling the interdisciplinary nature of rigorous investigation, practical business impact, nuanced engineering constraints and own intellectual curiosity. Unlike some other fields where it may be easier to focus on one particular niche, data science requires a fluency in product design, sound engineering principles, mathematical formulas and business intuition. While this might scare some people away, it also draws intellectual bon vivants hungering for a challenge.
They key inspiration for the handbook came from Jessica Livingston’s book – Entrepreneurs at Work. This book served as a landmark that gave a new generation of entrepreneurs relatable human role models and stories to understand what the nitty gritty ins and outs of entrepreneurship look like. The hope of the authors of The Data Science Handbook is that it will provide the same context and wisdom for aspiring data scientists.
As more of the world move towards digitization and automation, industries that were once human powered are now being dominated by software. Because information is king in these fields, there is little that data science does not touch- anyplace a human being needs to make a decision, be it a business, product or engineering decision, data science can inform and improve that decision. This book will be a bookshelf staple for all levels in data science to refer back to at any point in their career.
A note from the authors:
This book has been a long time in the making! After two years, 4 different cities, a thousand google docs later, we are proud to bring you our labor of love. Incredibly, the first time that we will get together as a complete team will be this coming August.
In order to share all that we have learned, we decided to self publish and price our book with a Pay-What-You-Want (PWYW) model, so that the knowledge is accessible to all who would enjoy it. We even priced our book with a data science experiment that yielded interesting data about the state of PWYW – you can read more about it on Forbes.