Before I even begin this article, let me just say that I love iPython Notebooks, and Atom is not an alternative to Jupyter in any way.

With ML Engineer job roles in all the vogue and a lot of people preparing for them, I get asked a lot of times by my readers to recommend courses for the ML engineer roles particularly and not for the Data Science roles.

Pandas is one of the best data manipulation libraries in recent times.

As Alexander Pope said, to err is human. By that metric, who is more human than us data scientists?

Data Exploration is a key part of Data Science. And does it take long?

Just recently, I had written a simple tutorial on FastAPI, which was about simplifying and understanding how APIs work, and creating a simple API using the framework.

Have you ever been in a situation where you want to provide your model predictions to a frontend developer without them having access to model related code?

Big Data has become synonymous with Data engineering. But the line between Data Engineering and Data scientists is blurring day by day.

Every few years, some academic and professional field gets a lot of cachet in the popular imagination.

Recently, I was reading Rolf Dobell’s The Art of Thinking Clearly, which made me think about cognitive biases in a way I never had before.