The first data hires at an early stage startup face numerous challenges — an infrastructure built to run the business but not analyze it, an organization hungry for information without
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I often get asked by junior data professionals how they can improve as data scientists. Today I will outline a generic framework for thinking about learning and provide a few
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I have spoken to many fellow analytics practitioners who are adament that they want their team to never touch “production.” While there are good reasons to be careful whenever you make changes
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Often, Data and Analytics teams go under-utilized in their organization because they can not collaborate effectively with the broader Technology and Software Engineering teams.
By designing software following the “code
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A hypothetical tech company just completed an A/B test of two experiences, A (the test) and B (the control). The test was set up properly and executed successfully. The following
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Everyone has their own reaction when discovering wrong data. It might start with a double take or maybe an itching feeling that the number should be a higher. However it
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Anyone who has worked in digital analytics will tell you that day over day performance can be volatile. Shifts in marketing mix can cause fluctuating e-commerce conversion rates, new feature
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Imagine you hit a roadblock while trying to tackle a complex piece of analysis, using a python function or designing your first data organization. What do you do? Of course
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There has been a lot of discussion in the data science community about the use of black-box models, and there is lots of really fascinating ongoing research into methods, algorithms, and tools to
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