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.