TODAY'S ISSUE
TODAY’S DAILY DOSE OF DATA SCIENCE
NumPy Cheat Sheet for Data Scientists
Here’s a NumPy cheat sheet that depicts the 40 most commonly used methods from NumPy:
In our experience, you will use these methods 95% of the time working with NumPy.
Whenever you are trying to learn a new library, mastering/practicing each and every method is not necessary.
Instead, put Pareto’s principle to work—20% of your inputs contribute towards generating 80% of your outputs.
👉 Over to you: Have I missed any commonly used methods?
IN CASE YOU MISSED IT
​​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​​ →
In case you missed it
Building an MCP server
Imagine you only know English.
To get info from a person who only knows:
- French, you must learn French.
- German, you must learn German.
- And so on.
Learning even 5 languages will be a nightmare for you!
But what if you add a translator that understands all languages?
- You talk to the translator.
- It infers the info you want.
- It picks the person to talk to.
- It gets you a response.
The translator is like an MCP!
It lets you (Agents) talk to other people (tools) through a single interface.
​We recently covered how you can build your MCP servers here →​
Here's what we did:
- Understood MCP with a simple analogy.
- Built a local MCP server and interact with it via Cursor IDE.
- Integrated ​​Firecrawl’s MCP server​​ and interacted with its tools.
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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