Jordan Goldmeier is an entrepreneur, author, and keynote speaker. Jordan is considered one of the leading global minds on data science, data visualization, and analytics. He currently works with Fortune 500 companies and institutions. Past clients include Principal Financial, H&M, and ElementSix, Vitus, the Lindner College of Business at University of Cincinnati, and the State University of New York - Stony Brook. Jordan supports analysts by delivering relevant and actionable content on social media and providing free-of-charge speaking engagements for tech-focused community events. His third book, Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning is a #1 Best-Seller across multiple categories - and is currently being used as a textbook for various universities. He has received the prestigious Microsoft Most Valuable Professional Award for a 7th year. His first analytics project saved the United States Air Force $60 million. Jordan served as a volunteer Emergency Medical Technician for multiple agencies and as adjunct faculty for the Center for Analytics Impact at Wake Forest University. How would you describe what data science is and what it is not?
To be honest, I think we all care too much what is and isn’t data science. To me, data science is the field of working with data. And that’s a broad field that includes everything from databases to data visualization to project management. Yes, it includes statistics and computer science at its core. But I think there ought to be a definition for the entire field of working in data. And I like using data science for it.
All that being said, if I have to make a distinction, here’s what it would look like. Only some of the folks who work in data science are scientists. That is, they run experiments. Most people in data science are practitioners—they are using the technology of data science to solve problems. Most people are data science practitioners. Ultimately, this distinction makes sense to me. But also… who cares?
Companies need people to solve problems using data. Do whatever needs to be done to solve the problem and call it whatever you want.
What are the top three myths people seem to believe in when it comes to data analysis and how do we bust those myths?
The first myth is that data can really do anything. People are enamored by the success of big tech employing data science for everything from security to operations. But as Alex and I say in Becoming a Data Head, Big Tech has a major upper hand. They have a wealth of high quality, labeled data which you need for robust machine learning. To bust this myth, companies have to be honest about what they can and can’t do with the data they have.
The second myth is propagated by the consulting companies. It says companies have “analytics maturity” when they’ve successfully progressed from descriptive analytics to predictive analytics to prescriptive analytics. This is wholesale BS. Sorry to blow up people’s bubbles by being honest about it. A company shouldn’t define its analytics posture based on generic successive steps. Instead, companies should define the business problem and figure out what they can do with the people, data, and budget they already have. That might mean incorporating pieces across different types of analysis.
The third myth is that working in data is sexy. It’s a lot of potentially boring work: chasing ideas that go nowhere; pushing up against leaders who don’t really get it, and working on problems that are forgotten about a year later. Not all jobs are like this, but many are. And some people like these aspects more than others. I would simply say to those just joining the data field to be prepared for work that doesn’t meet their expectations. There’s not much an analyst can do about this except pay attention to what they do and don’t like and continually seek opportunities to get closer to their passions. But ultimately experience is the best guide here, and sometimes you have to make a few mistakes before you find what works. So don’t judge yourself.
Data science is also slowly making its way into the legal industry. What are your thoughts on getting legal professionals over their fear of math and data as it pertains to using it in their practice?
As Alex and I write in Becoming a Data Head, above all the most important skill is critical thinking. Lawyers are (hopefully) trained critical thinkers, and so, they are well suited for this type of work. The math might be new and frightening for some, but it is attainable to most.
I will use myself as an example. For whatever reason, I test poorly in math. I’m not actually bad at math per se, but I am bad at handwritten computation. (Seriously, if I am allowed to write a program to solve math problems, I am good.) My GMAT math scores are hilariously low. And I failed linear algebra 3 times before passing with a C. I did even worse in other math classes.
Linear algebra is an important part of data science. But one doesn’t even need to take a linear algebra class to understand what the algorithms are doing. That was part of why Alex and I wrote Becoming a Data Head.
In truth, computers can do a lot of calculations for you—and hiring a statistician is easy. What the field really needs are folks who understand what’s going on at a high-level, enough to know what questions to ask and how to argue with the data. (Shameless plug: this is all covered in our book!)
What's your take on the relationship between computer science and data science? How has it evolved as computers and businesses have grown ever more complex?
Computer science is one of the fields from which data science pulls. For some academic programs, the true difference might simply be that computer science is more theoretical and data science is more applied.
Ultimately, however, we can understand fields as different traditions. The tradition of computer science focuses largely on computation, complexity, and theory. The tradition of data science focuses a lot on solving specific problems by combining computer science with math, stats, and business knowledge. Most recently, however, data science has shifted focus toward AI and machine learning.
As with my other answers, data science I believe is more applicable and accessible to legal work. But it might be worth a lawyer learning computer science depending upon what they want to get into.
You both wrote an excellent book. What was the impetus for writing it and how did you manage to cover such a breadth of topics within a rather brief number of pages?
Alex and I had been talking about writing this book for ten years. Over a decade, we practiced explaining tough concepts to each other and poking holes in each other’s explanations. The book is the culmination of these conversations. By the time we started writing, we had battle-tested our explanations.
The impetus was that we found many people who work in this space either knew a lot about data science but felt like they couldn’t speak up or people actually didn’t know and pretended to understand in an effort not to look ignorant. The effect of this lack of communication is failed projects with high turnover and unrealistic expectations. We felt it was time to really help people understand what was happening behind the curtain without being too high-level or being too technical.
As for the number of pages, I should add that the original manuscript was 399 pages. As with all my writing, I first write everything I am thinking. Then, during editing, I removed everything that does not singularly drive the point of the chapter. Thankfully, Alex was game for this. Ultimately, this allowed us to hone down the most important topics and concepts.
A friend of mine said I should be a movie producer for how much I seem to enjoy editing and removing stuff. If you’ve never done it before, it can be hard to remove stuff you like. (And will likely challenge your ego.) But that’s basically the process.
To someone eager to learn more about data science, what would be your advice in addition to, of course, reading your book?
Data science is such a huge field. I would advise someone to write down the things they most enjoy (or want to learn) and begin to pursue learning those things specifically. Whether it’s through an online course, certificate program, or a book there are many ways to learn. It’s best to not get caught up in the "right way." Go out and learn—and figure out if you’ll even like it! (You might not!)