Nasdaq Acquires Quandl to Advance the Use of Alternative Data

Re:Nasdaq Acquires Quandl to Advance the Use of Alternative ... by rainboiboi on Tuesday December 04, 2018 @10:04AM (#18599980)

I think data scientists needs to produce more actionable insights as oppose to living in their own world. I suspect there will be an rising group of people who can understand data science techniques and communicate them effectively to drive business decisions. These people will be the ones who can clinch the top posts.

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Re:Nasdaq Acquires Quandl to Advance the Use of Alternative ... by pplonski86 on Tuesday December 04, 2018 @10:21AM (#18600153)

I'm running automatic machine learning SaaS for 2 years, and after this time, I can tell you that it is huge problem that data scientist are living in their own world (including me! and including data science tools).

I had such situation: my user created 50 ML models (xgboost, lightgbm, NN, rf) and ensemble of them. Let's say that single best model was 5% better than single best model with default hyper-params and ensemble was 2% better than tuned best single model. For me it was a huge success, but the user didn't care about model performance. He wants to have insights about data, not tuned blackbox.

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Re:Nasdaq Acquires Quandl to Advance the Use of Alternative ... by rainboiboi on Tuesday December 04, 2018 @10:37AM (#18600323)

I understand every single word you said and fully agree with you. Good point!

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Re:Nasdaq Acquires Quandl to Advance the Use of Alternative ... by cpb on Tuesday December 04, 2018 @10:54AM (#18600493)

In an interview, Ryan Caldbeck from Circle Up describes two categories of models: brainy models and brawny models. The ensemble described above sounds like a brawny model: you don't care how it made the decision, you're glad it did the heavy lifting and you might even double check the result.

However, the user's concern about the black box suggests they wanted what Ryan refers to as a brainy model, one with explicable decisions. Even within the features of the model there could be things to learn about the data.

How else are data scientists stuck within their own world?

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