Pandas Mind Map

TODAY’S DAILY DOSE OF DATA SCIENCE

​Pandas Mind Map

Here’s a mind map we created that lists all Pandas methods grouped by operation types:

It covers:

• I/O methods
• DataFrame creation methods
• DataFrame statistical info
• Renaming methods
• Plotting methods
• Time-series methods
• Grouping methods
• Pivot methods
• Categorical data methods
• and many more.

I am sure it will help you discover new Pandas methods as well :)

Find the complete mind map here: ​Pandas Mind Map​.

👉 Over to you: What other mind maps do you want me to create?

CRASH COURSE (56 mins)

​Graph Neural Networks​

• Google Maps uses graph ML for ETA prediction.
• Pinterest uses graph ML (PingSage) for recommendations.
• Netflix uses graph ML (SemanticGNN) for recommendations.
• Spotify uses graph ML (HGNNs) for audiobook recommendations.
• Uber Eats uses graph ML (a GraphSAGE variant) to suggest dishes, restaurants, etc.

The list could go on since almost every major tech company I know employs graph ML in some capacity.

Becoming proficient in ​graph ML​ now seems to be far more critical than traditional deep learning to differentiate your profile and aim for these positions.

A significant proportion of our real-world data often exists (or can be represented) as graphs:

• Entities (nodes) are connected by relationships (edges).
• Connections carry significant meaning, which, if we knew how to model, can lead to much more robust models.

The field of ​graph neural networks (GNNs)​ intends to fill this gap by extending deep learning techniques to graph data.

Learn sophisticated graph architectures and how to train them on graph data in ​this crash course​​ →

PRODUCTION ML (WITH IMPLEMENTATION)

5 techniques to test ML models in production​

Despite rigorously testing an ML model locally (on validation and test sets), it could be a terrible idea to instantly replace the previous model with the new model.

A more reliable strategy is to ​test the model in production​ (yes, on real-world incoming data).

While this might sound risky, ML teams do it all the time, and it isn’t that complicated.

There are many ways to do this.

​Learn five must-know strategies; how they work, when to use them, advantages and considerations, and their implementations here →​