42 Essential Quotes by Data Science Thought Leaders

42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.

By Lauren Delapenha, Editor at DiscoverDataScience.com

Skeptical detective. Signal-extractor. Possessed. If you’re a data scientist, it’s entirely legitimate for you to claim one (or all) of these identities as your own. At least, so say some of the leading innovators in data science today. In these quotes, they share their advice and insights drawn from years of experience to address the field’s most pressing questions. The list is designed to help both aspiring and established data scientists advance their careers, and also to spark some intelligent conversation at your next data science meetup.

Who is a data scientist?

  1. “By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights. It’s Columbus meet Columbo―starry-eyed explorers and skeptical detectives.

Monica Rogati, Independent Data Science Advisor

Read full article here.

  1. “‘Possessed’ is probably the right word. I often tell people, ‘I don’t want to necessarily be a data scientist. You just kind of are a data scientist. You just can’t help but look at that data set and go, ‘I feel like I need to look deeper. I feel like that’s not the right fit.’”

Jennifer Shin, Senior Principal Data Scientist at Nielsen; Lecturer at UC Berkeley

Listen to full interview here.

  1. “I think of data science as more like a practice than a job. Think of the scientific method, where you have to have a problem statement, generate a hypothesis, collect data, analyze data and then communicate the results and take action…. If you just use the scientific method as a way to approach data-intensive projects, I think you’re more apt to be successful with your outcome.

Bob Hayes, Ph.D, Chief Research Officer at Appuri

Listen to full interview here.

  1. As a data scientist, I can predict what is likely to happen, but I cannot explain why it is going to happen. I can predict when someone is likely to attrite, or respond to a promotion, or commit fraud, or pick the pink button over the blue button, but I cannot tell you why that’s going to happen. And I believe that the inability to explain why something is going to happen is why I struggle to call ‘data science’ a science.”

Bill Schmarzo, Chief Technology Officer at Dell EMC

Read full article here.

  1. “Data scientists are kind of like the new Renaissance folks, because data science is inherently multidisciplinary.”

John Foreman, Vice President of Product Management at MailChimp

Read full article here.

  1. Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.

Josh Wills, Director of Data Engineering at Slack

Read full article here.

  1. As data scientists, our job is to extract signal from noise.

Daniel Tunkelang, Consultant / Advisor

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  1. The job of the data scientist is to ask the right questions. If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?’ that’s more of a data science question.”

Hilary Mason, Founder, Fast Forward Labs

Read full article here.

  1. “A data scientist does model-driven analyses of our data; analyzes to improve our planning, increase our productivity, and develop our deeper levels of subject matter expertise. A data scientist works at the tactical, operational, and strategic levels, sharing insights with the business.”

Chris Pehura, Practice Director, Management Consultant at C-SUITE DATA

Read full article here.

  1. “[Data scientists are] able to think of ways to use data to solve problems that otherwise would have been unsolved, or solved using only intuition.

Peter Skomoroch, Former Principal Data Scientist at LinkedIn

Read full article here.

  1. “What sort of personality makes for an effective data scientist? Definitely curiosity…. The biggest question in data science is ‘Why?’ Why is this happening? If you notice that there’s a pattern, ask, “Why?” Is there something wrong with the data or is this an actual pattern going on? Can we conclude anything from this pattern? A natural curiosity will definitely give you a good foundation.”

Carla Gentry, Data Scientist at Talent Analytics

Read full article here.

  1. “There is a saying, ‘A jack of all trades and a master of none.’ When it comes to being a data scientist you need to be a bit like this, but perhaps a better saying would be, ‘A jack of all trades and a master of some.’”

Brendan Tierney, Principal Consultant at Oralytics

Read full article here.

How do I become a data scientist?

  1. “My number one piece of advice always is to follow your passions first. Know what you are good at and what you care about, and pursue that…. As a successful data scientist, your day can begin and end with you counting your blessings that you are living your dream by solving real-world problems with data.”

Dr. Kirk Borne, Principal Data Scientist, Booz Allen Hamilton

Read full article here.

  1. The No. 1 thing is you’ve got to have passion. This rich passion for going ruthlessly after the problem and being deeply intellectually honest with yourself about whether this is a reasonable answer….

“The second part is having the ability to be extremely clever with the data. And what I mean by that is: You’re working with ambiguity. And very often you can’t approach the problem with the rigor you would a homework assignment. The only way to survive through that is by being clever—to think of a different question that gets at the answer.

DJ Patil, Former US Chief Data Scientist

Read full article here.

  1. “Nobody ever talks about motivation in learning. Data science is a broad and fuzzy field, which makes it hard to learn. Really hard. Without motivation, you’ll end up stopping halfway through and believing you can’t do it, when the fault isn’t with you―it’s with the teaching.

Take control of your learning by tailoring it to what you want to do, not the other way around.

Vik Paruchuri, Founder, Dataquest

Read full article here.

  1. “I do not know how you teach someone to love to learn, but being self-motivated is integral to this field. Once you have the core concepts, to be able to be really excited about, and continue to seek out, new information is something that I look for, for example, when we are recruiting people.”

Shelly D. Farnham, Ph.D., Executive Director & Research Scientist, Third Place Technologies

Read full article here.

  1. You can best learn data mining and data science by doing, so start analyzing data as soon as you can! However, don’t forget to learn the theory, since you need a good statistical and machine learning foundation to understand what you are doing and to find real nuggets of value in the noise of big data.”

Gregory Piatetsky-Shapiro, President, KDnuggets

Read full article here.

  1. “Learning how to do data science is like learning to ski. You have to do it.”

Claudia Perlich, Chief Scientist, Dstillery

Read full article here.

  1. Data science’s learning curve is formidable. To a great degree, you will need a degree, or something substantially like it, to prove you’re committed to this career….

“Classroom instruction is important, but a curriculum that is 100 percent devoted to reading books, taking tests and sitting through lectures is insufficient. Hands-on laboratory work is paramount for a truly well-rounded data scientist….

“It should not degenerate into a program that produces analytics geeks with heads stuffed with theory but whose diplomas are only fit for hanging on the wall.”

James Kobielus, Lead Analyst for Data Science, Deep Learning, and Application Development at SiliconANGLE Media, Inc.

Read full article here.

  1. “In my view, success for data science professionals relies on becoming trained and able data scientists with the ability to perform data processing and computation at a massive scale. To achieve this, professionals must invest time in ongoing education through institutions with multidisciplinary programs that include elements from engineering, mathematical sciences, and social sciences. Converting big data into meaningful information begins with skilled professionals who are educated in all disciplines to be both data scientists and statisticians.”

Devavrat Shah, Professor at MIT’s Department of Electrical Engineering and Computer Science

Read full article here.

  1. There is no bottleneck for data scientists… The bottleneck is very often for companies who don’t have a culture of working with data to actually cut down the process into the right steps.”

Lutz Finger, Director of Data Science at Snap

Listen to full interview here.

  1. “Once you have a certain amount of math/stats and hacking skills, it is much better to acquire a grounding in one or more subjects than in adding yet another programming language to your hacking skills, or yet another machine learning algorithm to your math/stats portfolio…. Clients will rather work with some data scientist A who understands their specific field than with another data scientist B who first needs to learn the basics―even if B is better in math/stats/hacking.”

Stephan Kolassa, Data Science Expert at SAP Switzerland AG

Read full article here.