StatsModels & Scikit-learn are two popular packages for working with stats and machine learning in Python. Learn more about each from The Data Incubator.
It’s that most magical time of the year. Cheeks are 23% rosier week over week, sleigh-bell tinkling is up a remarkable 285% over the previous month, and Santa’s elves are busily vacuuming their Christmas-wish databases. And all of us are trying to find that perfect holiday gift.
The shape of code, or what we can learn from indentation. As a TDI data scientist in residence, I have learned to judge code quality at a quick glance by looking at indentation. The rule of thumb is: good code has frequent changes in indentation, but should not be deeply indented.
Picture this: You’ve been working hard on a project at work. You’ve run several algorithms, tuned the necessary hyperparameters, performed cross validation and exhausted the checks required to ensure you’re not overfitting.
It’s 2020 and the world has changed remarkably, including in how companies screen data science candidates. While many things have changed, there is one change that stands out above the rest. At The Data Incubator, we run a data science fellowship and are responsible for hundreds of data science hires each year.
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