Why is data science sexy? It has something to do with so many new applications and entire new industries come into being from the judicious use of copious amounts of data. Examples include speech recognition, object recognition in computer vision, robots and self-driving cars, bioinformatics, neuroscience, the discovery of exoplanets and an understanding of the origins of the universe, and the assembling of inexpensive but winning baseball teams. In each of these instances, the data scientist is central to the whole enterprise. He/she must combine knowledge of the application area with statistical expertise and implement it all using the latest in computer science ideas.

In the end, sexiness comes down to being effective. I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists across 16 different industries to understand both how they think about it theoretically and also very practically what problems they’re solving, how data’s helping, and what it takes to be successful. All 16 interviewees are at the forefront of understanding and extracting value from data across an array of public and private organizational types — from startups and mature corporations to primary research groups and humanitarian nonprofits — and across a diverse range of industries — advertising, e-commerce, email marketing, enterprise cloud computing, fashion, industrial internet, internet television and entertainment, music, nonprofit, neurobiology, newspapers and media, professional and social networks, retail, sales intelligence, and venture capital.

In particular, Sebastian asked open-ended questions so that the personalities and spontaneous thought processes of each interviewee would shine through clearly and accurately. The practitioners in this book share their thoughts on what data science means to them and how they think about it, their suggestions on how to join the field, and their wisdom won through experience on what a data scientist must understand deeply to be successful within the field.

In this post, I want to share the best answers that these data scientists gave for the question:

“What advice would you give to someone starting out in data science?”

1 — Chris Wiggins, Chief Data Scientist at The New York Times and Associate Professor of Applied Mathematics at Columbia

“Creativity and caring. You have to really like something to be willing to think about it hard for a long time. Also, some level of skepticism. So that’s one thing I like about PhD students — five years is enough time for you to have a discovery, and then for you to realize all of the things that you did wrong along the way. It’s great for you intellectually to go back and forth from thinking “cold fusion” to realizing, “Oh, I actually screwed this up entirely,” and thus making a series of mistakes and fixing them. I do think that the process of going through a PhD is useful for giving you that skepticism about what looks like a sure thing, particularly in research. I think that’s useful because, otherwise, you could easily too quickly go down a wrong path — just because your first encounter with the path looked so promising. And although it’s a boring answer, the truth is you need to actually have technical depth. Data science is not yet a field, so there are no credentials in it yet. It’s very easy to get a Wikipedia-level understanding of, say, machine learning. For actually doing it, though, you really need to know what the right tool is for the right job, and you need to have a good understanding of all the limitations of each tool. There’s no shortcut for that sort of experience. You have to make many mistakes. You have to find yourself shoehorning a classification problem into a clustering problem, or a clustering problem into a hypothesis testing problem. Once you find yourself trying something out, confident that it’s the right thing, then finally realizing you were totally dead wrong, and experiencing that many times over — that’s really a level of experience that unfortunately there’s not a shortcut for. You just have to do it and keep making mistakes at it, which is another thing I like about people who have been working in the field for several years. It takes a long time to become an expert in something. It takes years of mistakes. This has been true for centuries. There’s a quote from the famous physicist Niels Bohr, who posits that the way you become an expert in a field is to make every mistake possible in that field.”

2 — Caitlin Smallwood, Vice President of Science and Algorithms at Netflix

“I would say to always bite the bullet with regard to understanding the basics of the data first before you do anything else, even though it’s not sexy and not as fun. In other words, put effort into understanding how the data is captured, understand exactly how each data field is defined, and understand when data is missing. If the data is missing, does that mean something in and of itself? Is it missing only in certain situations? These little, teeny nuanced data gotchas will really get you. They really will. You can use the most sophisticated algorithm under the sun, but it’s the same old junk-in–junk-out thing. You cannot turn a blind eye to the raw data, no matter how excited you are to get to the fun part of the modeling. Dot your i’s, cross your t’s, and check everything you can about the underlying data before you go down the path of developing a model. Another thing I’ve learned over time is that a mix of algorithms is almost always better than one single algorithm in the context of a system, because different techniques exploit different aspects of the patterns in the data, especially in complex large data sets. So while you can take one particular algorithm and iterate and iterate to make it better, I have almost always seen that a combination of algorithms tends to do better than just one algorithm.”

3 — Yann LeCun, Director of AI Research at Facebook and Professor of Data Science/Computer Science/Neuroscience at NYU

“I always give the same advice, as I get asked this question often. My take on it is that if you’re an undergrad, study a specialty where you can take as many math and physics courses as you can. And it has to be the right courses, unfortunately. What I’m going to say is going to sound paradoxical, but majors in engineering or physics are probably more appropriate than say math, computer science, or economics. Of course, you need to learn to program, so you need to take a large number of classes in computer science to learn the mechanics of how to program. Then, later, do a graduate program in data science. Take undergrad machine learning, AI, or computer vision courses, because you need to get exposed to those techniques. Then, after that, take all the math and physics courses you can take. Especially the continuous applied mathematics courses like optimization, because they prepare you for what’s really challenging. It depends where you want to go because there are a lot of different jobs in the context of data science or AI. People should really think about what they want to do and then study those subjects. Right now the hot topic is deep learning, and what that means is learning and understanding classic work on neural nets, learning about optimization, learning about linear algebra, and similar topics. This helps you learn the underlying mathematical techniques and general concepts we confront every day.”

4 — Erin Shellman, Data Science Manager at Zymergen, Ex-Data Scientist at Nordstrom Data Lab and AWS S3