

TODAY'S ISSUE
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 →​
THAT'S A WRAP
No-Fluff Industry ML resources to
Succeed in DS/ML roles

At the end of the day, all businesses care about impact. That’s it!
- Can you reduce costs?
- Drive revenue?
- Can you scale ML models?
- Predict trends before they happen?
We have discussed several other topics (with implementations) in the past that align with such topics.
Here are some of them:
- Learn sophisticated graph architectures and how to train them on graph data in this crash course.
- So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
- Run large models on small devices using Quantization techniques.
- Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
- Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
- Learn how to scale and implement ML model training in this practical guide.
- Learn 5 techniques with implementation to reliably test ML models in production.
- Learn how to build and implement privacy-first ML systems using Federated Learning.
- Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.
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