From early career data to senior-level professionals, these are the most common mistakes data scientists make . . . and how to avoid them!
As the volume of available data grows more overwhelming, you need to reduce the noise at the source.
Data science and machine learning are closely related fields, but they have some key differences. If you’re entering the profession, you need to understand how they overlap and also where they diverge.
Not all jobs are posted publicly. Here’s how to find them.
You don't have to let go of the hands-on work you love.
Here are six things you need to know about using these powerful tools in order to write more Pythonic code.
Navigating regulation, fraud risk and sensitive data.
Before you hire a data scientist, make sure you understand the stages of the startup lifecycle. You might not be ready.
Here’s how you foster cross-team collaboration.
In this practical approach to NLP, I’ll show you how to use Doc2Vec to create product tags and get the most out of your machine learning.
