The No-code Data Science Tool Stack
8 powerful no-code data science tools in a single frame.
Build production-ready multi-agent apps with SwarmZero
There’s not much you can do with OpenAI's Swarm. It has 7 major limitations 👇
No RAG support.
Supports limited LLMs.
No multimodality support.
The agent orchestration is quite manual and hardcoded.
Does not connect to popular vector databases like Pinecone, ChromaDB, etc.
By their very nature, agents MUST be able to interact with several tools. But this is not possible with OpenAI Swarm.
[Biggest] It’s only built for experimental and educational purposes.
SwarmZero solves all of the above limitations (star the repo below).
It’s an open-source framework to build multi-agent apps in a highly customizable way and take them to production.
Start building multi-agent apps with SwarmZero today →
Thanks to SwarmZero for partnering on today’s issue.
The No-code Data Science Tool Stack
I am a big fan of no-code tools. The below visual depicts 8 powerful no-code tools for DS/ML tasks:
Let’s discuss them one by one.
1) Mito
Create a spreadsheet interface in Jupyter Notebook.
Use Mito AI to conduct data analysis.
Automatically generates Python code for each analysis
2) Gigasheet
Browser-based no-code tool to analyze data at scale.
Use AI to conduct data analysis.
It’s like a combination of Excel + Pandas with no scale limitations.
You can analyze datasets as large as 1B rows.
3) PivotTableJS
Create Pivot tables, aggregations, and charts using drag-and-drop.
Add heatmaps to tables.
Works within Jupyter Notebook.
4) Drawdata
Draw any 2D scatter dataset by dragging the mouse.
Export the data as DataFrame, CSV, or JSON.
Create a histogram and line plot by dragging the mouse.
5) PyGWalker
Open a tableau-style interface in Jupyter notebook.
Analyze a DataFrame as you would in Tableau.
6) Visual Python
A GUI-based Python code generator.
Import libraries, perform data I/O, create plots, write code for ML models, etc. by clicking buttons.
7) Tensorflow Playground
Browser-based tool.
Change data, model architecture, hyperparameters, etc., by clicking buttons.
8) ydata-profiling
Generate a standardized EDA report for your dataset.
Works in a Jupyter notebook.
Covers info about missing values, data statistics, correlation, data interactions, etc.
👉 Over to you: Have I missed any other cool data science no-code tools?
P.S. For those wanting to develop “Industry ML” expertise:
We have discussed several other topics (with implementations) in the past that align with such topics.
Here are some of them:
Learn how to build real-world RAG apps, evaluate, and scale them: A crash course on building RAG systems—Part 3 (With Implementation).
Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks.
Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.
Learn how to identify causal relationships and answer business questions: A Crash Course on Causality.
Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.
Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)
Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.
Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.
All these resources will help you cultivate key skills that businesses and companies care about the most.
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