The 12 dark secrets of data science

From hidden costs to highly suspect conclusions, data science is not without its detractions and limitations — despite the ongoing hype.

The 12 dark secrets of data science

Data science is revolutionizing computational fields and providing a foundation for enabling computers to solve problems. From drug design to machine vision, smart algorithms are enriching our lives and sometimes even saving them. But beyond the success stories, there’s a vast amount of questionable and unreliable results. Everyone who approaches a new collection of data with the job of extracting meaningful insights needs to keep this dark side in mind.

Here are 12 rarely discussed downsides of data science that are obscured by the hype and should be kept in mind when mining data for insights.

Many data science discoveries are obvious

When the bank looked for a way to predict loan defaults, they found that people with no savings were more likely to stop paying their debts. When the hospitals looked for causes of doctor error, they found lack of sleep was a big indicator. Tall people hit their head more often. Bicyclists die from head injuries more often than couch potatoes.

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