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Every product team wants to know what makes their product thrive. They want to know how to optimize metrics and leave users the happiest and most engaged. Without a way to definitively understand user behavior, they must turn to anything they can. Enter the A/B test.
Ultimately, every A/B test starts from a hypothesis. The hypothesis could be, “If we did [blank], then we would improve conversion.” Or “Feature X should drive increased retention, let’s test out that assumption through…Continue
In this day and age, you can’t go a day without hearing terms such as “data science,” “big data,” or “analytics.” These terms have been thrown around to apply to so many situations that the original meaning of these words is lost.
So, what does it take for any organization to be successfully data-driven? Although analytics may seem complicated, the solution comes from simplicity.
I believe it comes down to four things, as I’ve illustrated below: business need, clean data…Continue
Learning any new skill is hard. There are too many possibilities, and the goal seems massive and intimidating.
Enter the Pareto Principle.
The Pareto Principle, also known as the 80/20 rule, suggests that 80 percent of results come from 20 percent of efforts. It can be applied to everything from business to language, even learning how to use R.
With just a…Continue
Building off my last post, I want to use the same healthcare data to demonstrate the use of R packages. Packages in R are stored in libraries and often are pre-installed, but reaching the next level of skill requires being able to know when to use new packages and what they contain. With that let’s get to our example.