Dr. Becky Tucker is a senior data scientist at Netflix and chats with us about how Netflix is an extremely data-driven company how they apply data science to every aspect of the user experience, including content – one of the most nuanced ways to use data science.
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.
The increasing demand for artificial Intelligence (AI) and data science experts, driven in part by the COVID-19 economic crisis, is showing no sign of abating. Many employers are failing to identify viable job candidates, much less interviewing or hiring them. What’s the biggest obstacle holding them back? In our experience, it is often a poor job posting.
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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