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Self-Service BI vs Traditional BI: What’s Next?

Alation

Today, the term describes that same activity, but on a much larger scale, as organizations race to collect, analyze, and act on data first. But there have always been limits on who can access valuable data, as well as how it can be used. In the 1970s, data was confined to mainframes and primitive databases.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? One challenge in applying data science is to identify pertinent business issues.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. 1998) and others).