The methodology tames unruly pipelines in order to increase the value of your data — so you can adapt faster to business changes.
Data scientists can’t rely only on assumptions, models, and numbers to understand the choices users make.
As a data scientist, you may be able to get away without using linear algebra — but not for long. Here’s how linear algebra can improve your machine learning, computer vision and natural language processing.
It can be frustrating, but it’s rewarding too.
From zoology and physics to designing algorithms.
Applying some software engineering principles to our data science pipeline led to great results. Here’s what we learned.
The working world has different motivations and expectations than your professors did. Read on to learn what being a data scientist is really like.
From early career data to senior-level professionals, these are the most common mistakes data scientists make . . . and how to avoid them!
You don't have to let go of the hands-on work you love.
Through proper data management, companies can process raw data and transform it into high-quality, valuable information. Here’s how.
