After taking a 12-week data science bootcamp and in 2016 and then launching into industry, Dan Friedman’s expectations weren’t remotely met:

Over the past few years, I’ve worked as a Data Scientist, a Data Engineer, and as an industry consultant. I’ve also learned from the stories of dozens of data scientists and similar professions, actively read articles on data science and followed data science thought leaders on Twitter.

Across these diverse data experiences, I have noticed common themes.

Below are seven most common (and at times flagrant) ways that data science has failed to meet expectations in industry. Throughout each section, I’ll propose solutions to these shortcomings.

Maybe I’ve been listening to Practical AI too much, but I am not surprised that one of his seven shortcomings is that most of the job is spent cleaning data. That being said, there’s a lot here that is surprising to me and worthy of consideration for anyone thinking about entering the industry.