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The most important aspect of data science is communication. Algorithms, coding languages and software are important to know but these things are easily and quickly looked up when details become shrouded in the dust of time. Given the strong academic backgrounds of most data scientists, it’s not hard for one to learn how to program in a new language in a very short time and even quicker to learn how to read a new language for most data science purposes (with the exception of lower-level languages like C — that’s hard).
Being able to perfectly whiteboard a bubble sort algorithm in 3 languages will not persuade VPs to productionize your experimental results. Additionally, it’s not every day that you reflect on different loss functions or the pseudo-code for an algorithm. Forgetting the details of those things is normal. On the other hand, you communicate every day in some way and yet it takes deliberate, conscious effort to do so respectfully, succinctly and effectively to a high degree. It’s a skill refined over a lifetime and one that some will never improve upon. The following is a living list of tips I’ve come up with based on personal experiences. At the time of this publication, my list is short. Please share your own ideas in the comments so we can crowdsource a master list of helpful recommendations for everyone.
Data has important stories to tell. They rely on you to be their clear and convincing voice.
Here’s some advice on how to be a better communicator, especially applicable to those who spend a good amount of time in the weeds:
- Only talk about the few precious pearls you found not the hundreds of oysters you painstakingly opened. No one cares about the hundreds of oysters you had to open to find those nuggets of luminescent insight. In any field, the best never talk about the effort unless asked. Similarly, be prepared to talk about all of those empty oysters and your process of oyster selection, cleaning and opening, but don’t volunteer this information unless asked because most people won’t care. TLDR; have faith that people understand the lengths you went to for your findings but also be prepared to succinctly explain those due diligence efforts only when asked.
- Strive for audience education. Most data scientists don’t even try, opting instead for academic jargon to maintain an air of superiority, the semblance of complexity for the sake of exclusivity and a “just trust me” attitude. But respecting the intelligence of your peers is actually much more aligned with your self-interests: recruiting more allies dedicated to ensuring implementation of your ideas. At the end of the day, while not everyone will want to know the mathematical details of an algorithm, most people are interested to understand more and you can help.
Please share your own ideas in the comments so we can crowdsource a master list of helpful recommendations for everyone. I’d love to hear other great tips, especially those particularly useful for practicing data scientists .
About the Author
Andrew Young is an R&D Data Scientist Manager at Neustar. For context, Neustar is an information services company that ingests structured and unstructured text and picture data from hundreds of companies in the domains of aviation, banking, government, marketing, social media and telecommunications to name several. Neustar combines these data ingredients then sells a finished dish with added value to enterprise clients for purposes like consulting, cyber security, fraud detection and marketing. In this context, Mr. Young is a hands-on lead architect on a small R&D data science team responsible for building, optimizing and maintaining a system feeding all products and services responsible for $1+ billion in annual revenue for Neustar. Follow Andrew on LinkedIn to stay up-to-date on the latest trends in data science!
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