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1. Predictive Analytics Mindset
Do data scientists perform predictive analytics? Absolutely . Do non-data scientists also? Sure . However, if I were to put data scientist on one end of the predictive analytics see-saw, and on the other end, I would expect the data scientist would always hit the ground.
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95 reads
2. Curiosity
- "How can we increase sales?"
- "Why is churn higher in some months than in others?"
- "Why does this need to be done like that ?"
- "What happens if we do X to Y?"
- "How does X play into what happens over here?"
- "Have we tried...?"
- And so on...
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56 reads
3. Systems Thinking
It is the data scientist's job to know as much about their relevant systems as possible, and leverage their curiosity and predictive analytical mindset to account for as much of these systems' operations and interactions as feasible, in order to keep them running smoothly even when being tweaked. If you aren't able to appreciate why no one person is able to fully explain how the economy works, data science is not for you.
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39 reads
4. Creativity
Of course, we need the technical, statistical, and additional skills to be able to follow up on these questions, but these skills are useless if we don't have the creativity to think of interesting and non-obvious ways to be able to investigate and ultimately provide answers. This is why data scientists must inherently be creative.
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42 reads
5. Storytelling Sensibilities
This storytelling is not like fictional storytelling; it's more like "fancy explaining," or providing an intuitive explanation tailored to the listener. You wouldn't tell a five year old a Stephen King story a bedtime, just like you wouldn't delve into a dry, long-winded narrative about supply chain metrics to someone working in research and development. Be aware of your audience.
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44 reads
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