Hi Reader, Last week, I invited you to help me test Google's Data Science Agent in Colab, which promises to automate your data analysis. Does it live up to that promise? Let's find out! 👇
🔢 How to use Data Science Agent in Colab:Before we get to the results, here's a recap of how it works:
The interface is a bit confusing, so I recorded a short video (no audio) to help you get started. 🎯 Does it do a good job with the analysis?Here's what tester #1 said:
I tested it with a non-trivial statistical analysis and I should say... the results are really impressive. Implementing the same code from scratch, without an existing pipeline, it would have taken to me more than one hour (to be optimistic!!) Tester #2:
This Data Science Agent in Colab is so powerful! I created a mock dataset for testing and asked it to calculate the cluster coherence. Then it came out with a plan and executed it. Most amazing part is that it installed the missing packages by itself when running into an error. Finally, it did answer my question (Which cluster has the smallest coherence value?) which is amazing. Tester #3:
It was good at simple analysis but might not work great when given complex problems. 👉 Here are my takeaways:After reviewing the Colab notebooks shared by testers and testing it myself, here's my overall conclusion: Data Science Agent is only useful if you are able to evaluate whether the steps it takes are correct. It will come up with a plan and write the code to execute that plan, but you still need to know enough to assess:
As such, Data Science Agent is most useful for those who could already complete the analysis on their own, but just want help in order to execute the analysis faster. Thus if you use Data Science Agent without sufficient expertise, you run the risk of performing a misleading (or incorrect) analysis! See you Friday! 👋If you enjoyed this week's tip, please considering sharing it with a friend! I'll be back in your inbox on Friday to share the top AI news of the week. - Kevin |
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