the Data Science Shop: why this project?
... and why it will improve the practice of Data Science
Many years ago, a friend gave me a radical piece of advice: “every six years, take a sabbatical from your professional life and use it to gain perspective, regroup, and organize what you’ve learned”. I never regretted following his advice.
My latest sabbatical from Data Science was spent as a Visiting Professor at Columbia University. During that year, I started working on a project that I expect will improve the practice of Data Science. I organized my own thoughts and experiences. I distilled insights from many hours of conversations with a wide network of colleagues in Data Science. I tested the resulting ideas and materials in the classroom. I gathered feedback from talks and lectures I’ve given in the US and abroad.
Inevitably, my sabbatical was extended to work full time on the many components that bring this project to life.
What is this project about?
This project is about explaining the practice of Data Science, clarifying what it can do for a business, detailing how it operates in a company (outside of Big Tech), and unpacking what it needs to succeed.
The initial rendering of the project went live today as a website - datascienceshop.com - that summarizes the roadmap for successful Data Science. All graphic materials on the site are free to download. Many more will be added in the coming months. Make sure to drop by often!
Additional materials will flow through this Substack where I will:
continue to unpack the Data Science playbook with posts and infographics,
help non-technical audiences navigate technical topics related to Data Science and technology, and
untangle what it takes to become an accomplished Data Scientist beyond mastering algorithms.
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I am also compiling the final rounds of feedback on a book manuscript that explains the “what” of Data Science and the “how” of successful Data Science in business. More news to come soon!
Why this project?
During a social event some years ago, I struck a conversation with a Senior Executive at a company wrestling with distribution logistics. He offered to hire me on the spot as soon as he heard the word Data Science in my job title. “We have data and I need you to bring the Data Science that will take my company to the next level”, he said.
As our conversation continued, we talked about what he thought was a solution to his problem, the data his company had, and the technology at his disposal. He was shocked to learn that a different solution – standard in Data Science – would be more appropriate, that his company was not ripe for Data Science, and that the investment that he was seeking to make in a single Data Scientist – me – would be an expensive waste of money.
That’s when it hit me: what was obvious to me from years of practicing Data Science, was a complete revelation to that Senior Executive. Our conversation made clear to him what Data Science could realistically do for his business, how it operates, and what it needs to succeed. I realized that we need a simple way to make the Data Science playbook accessible. So, the Data Science Shop project was conceived.
That was not the first – or the last – time that a business owner or a Senior Executive offered me a job on the spot. Too many colleagues in Data Science have similar anecdotes. What binds these stories together is that, with few exceptions, the person offering the job has a misleading understanding of Data Science for businesses.
Data Science has a playbook, but no one has spent the time to lay it out to the people that would benefit most from it. There is also no graduate course to learn about the practice of Data Science from a business perspective. There is no manual to plan and evaluate the return of an investment in Data Science. There is no book that outlines the steps to bring Data Science to a company for the first time. There is no white paper that coherently organizes the playbook (elements and processes) for the practice of Data Science in a business.
I decided to step in and start filling these gaps.
Who is this project for?
This project is for anyone interested in the practice of Data Science, but three audiences might find it particularly useful:
executives (decision-makers) who need a roadmap to help them decide whether they need Data Science (or AI) in their companies
middle managers (implementers) who need a playbook to implement Data Science (or AI) in their companies for the first time
newly minted Data Scientists (practitioners) who want to learn the practice of Data Science before they jump on the job market
Why do I get to work on this project?
I am a Data Science sherpa. I have spent the last decade at the intersection of business and technology, leading and building Data Science Shops in the media and entertainment industry. I am also on faculty at Columbia University and Columbia Business School where I teach the practice of Data Science to Social Scientists and technology to non-technical MBAs.