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.
What makes a good data scientist?
- “Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.”
―Jean-Paul Isson, Global VP Predictive Analytics & BI, Monster Worldwide Inc.
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- “Without a grounding in statistics, a Data Scientist is a Data Lab Assistant.”
―Martyn Jones, Managing Director at Cambriano Energy
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- “Having skills in statistics, math, and programming is certainly necessary to be a great analytic professional, but they are not sufficient to make a person a great analytic professional.”
―Bill Franks, Chief Analytics Officer at Teradata
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- “Talented data scientists leverage data that everybody sees; visionary data scientists leverage data that nobody sees.”
―Vincent Granville, Executive Data Scientist & Co-Founder at Data Science Central
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- “What makes a good scientist great is creativity with data, skepticism and good communication skills. Getting all of that together in the same person is difficult―because traditionally, different people follow different paths in their careers―some are more technical, others are more creative and communicative. A data scientist has to have both.”
―Monica Rogati, Independent Data Science Advisor
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- “Good data science is exactly the same [as] good science…. Good data science will never be measured by the terabytes in your Cassandra database, the number of EC2 nodes your jobs is using, or the volume of mappers you can send through a Hadoop instance. Having a lot of data does not license you to have a lot to say about it.”
―Drew Conway, Founder and CEO at Alluvium
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- “Critical thinking skills…really [set] apart the hackers from the true scientists, for me…. You must must MUST be able to question every step of your process and every number that you come up with.”
―Jake Porway, Founder and Executive Director of DataKind
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- “How do we start to regulate the mathematical models that run more and more of our lives? I would suggest that the process begin with the modelers themselves. Like doctors, data scientists should pledge a Hippocratic Oath, one that focuses on the possible misuses and misinterpretations of their models.”
―Cathy O’Neil, Author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Read full article here.
- “With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real… Big data isn’t about bits, it’s about talent.”
―Douglas Merrill, Founder & CEO at ZestFinance
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- “Data analysts who don’t organize their transformation pipelines often end up not being able to repeat their analyses, so the advice I would give to myself is the same advice often given to traditional scientists: make your experiments repeatable!”
―Mike Driscoll, Founder & CEO at Metamarkets
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- “Great data scientists never assume they know something without in-depth analysis, they think in hypotheses which need to be either rejected or proved, and they ask a lot of questions, even if they are 99.9% sure they know the answer.”
―KarolisUrbonas, Head of Business Intelligence, Amazon Devices at Amazon
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- “For me, data science is a mix of three things: quantitative analysis (for the rigor necessary to understand your data), programming (so that you can process your data and act on your insights), and storytelling (to help others understand what the data means).”
―Edwin Chen, Data Scientist and Blogger
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- “The difference between a great [data scientist] and a good one is like the difference between lightning and a lightning bug.”
―Thomas C. Redman, Ph.D., “The Data Doc” at Data Quality Solutions
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How should I communicate data science findings?
- “The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning…. Data-driven predictions can succeed―and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.”
―Nate Silver, Founder and Editor-in-Chief of FiveThirtyEight
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- “One of the big challenges of being a data scientist that people might not usually think about – is that the results or the insights you come up with have to make sense and be convincing. The more intelligible you can make them, the more likely it is that your recommendations will be put into effect.”
―Victor Hu, Head of Data Science at QBE Insurance
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- “A data scientist must possess the knack of being able to ‘identify business value from mathematical models.’ But that vital business value can only materialize if the data scientist also networks with other departments, understands their objectives, is familiar with their data and processes – and can spot the analysis options they provide.”
―Alexander Linden, VP of Data Science, Gartner
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- “People like simple explanations for complex phenomena. If you work as a data scientist, or if you are planning to become/hire one, you’ve probably seen storytelling listed as one of the key skills that data scientists should have. Unlike ‘real’ scientists that work in academia and have to explain their results mostly to peers who can handle technical complexities, data scientists in industry have to deal with non-technical stakeholders who want to understand how the models work. However, these stakeholders rarely have the time or patience to understand how things truly work. What they want is a simple hand-wavy explanation to make them feel as if they understand the matter―they want a story, not a technical report.”
―YanirSeroussi, Independent Data Science Consultant & Entrepreneur
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- “You need to be able to take a dataset and discover and communicate what’s interesting about it for your users. To turn this into a product requires understanding how to turn one-off analysis into something reliable enough to run day after day, even as the data evolves and grows, and as different users experience different aspects of it.”
―Amy Heineike, VP Technology, Stealth Startup
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What is the future of data science?
- “We should expect a ‘Big Data 2.0’ phase to follow ‘Big Data 1.0’. Once firms have become capable of processing massive data in a flexible fashion, they should begin asking: ‘What can I do that I couldn’t do before, or do better than I could do before?’ This is likely to be the golden era of data science.”
―Foster Provost and Tom Fawcett, Co-authors of Data Science for Business
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- “As the field grows, keep an open mind and evolve with it. Work hard, think outside the box, and learn as much as you can about the technical side of being a data scientist. Be responsible with the data and realize the potential the data can have to solving problems. Always ask yourself how the data can be used to positively impact the lives around you, and use that to guide your design and development.”
―ShanjiXiong, Chief Scientist at Experian’s Global DataLab
Read full article here.
These thoughts are more than just clever wordsmithing; they show us fresh ways of understanding, valuing, and breaking into this dynamic field. So, now is an excellent time to pursue a degree in the popular field of data science (more convenient, too, as data science and other up-and-coming degrees, like a master’s in counseling or a doctorate in education, have recently begun to be offered online). Before you do, though, it’s a good idea to hear from a variety of perspectives on how to succeed as a data scientist, especially when those perspectives coincide on a few important points. So here are some key takeaways to remember:
- Data science is not easy. Seems obvious enough, but as you encounter step-by-step career paths online, be aware that no single method is going to work for everyone, and the multiplicity of opinions on how to become a data scientist confirms that.
- Practice, practice, practice. It absolutely doesn’t make perfect, but it’s absolutely necessary throughout the duration of your data science career.
- Do your homework. Theory is essential to shape and direct the course of all that practice.
- Learn how to effectively communicate your findings. This one speaks for itself, but your data won’t.
If you have a favorite data science quote, leave it in the comments to continue the conversation!
Bio: Lauren Delapenha is an editor at DiscoverDataScience.com, a one-stop resource for learning about the rapidly-evolving field of data science through comprehensive education and career guides.
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