How do you make the transition from a career in academia to a data professional? Many students at The Data Incubator (TDI) say that was their first hurdle when deciding if they were ready to apply. Hear more from alumni Newton Le and his journey out of academia.
Do you keep pushing forward in academia, betting on being one of the few to land the coveted tenure track? Or do you leap into something new and carve your path outside of academia?
While we can’t tell you with absolute certainty what path is right for you, we can help you get started if you choose to transition out of academia.
Did you know that Python has two ways of measuring “sameness”? Read on to learn what equality vs identity is and which one to use.
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.
The concept of “edge computing” has been around since the late 90s, and typically refers to systems that process data where it is collected instead of having to both store and push it to a centralized location for off-line processing.
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