There are a couple of myths that I see more an more these days. Like many myths they seem plausible on the surface but experienced data scientist know that the reality is more nuanced (and sadly requires more work).
- Deep learning (or Cognitive Analytics) is an easy button. You can throw massive amounts of data and the algorithm will deliver a near optimal model.
- Big data is always better than small data. More rows of data always results in a significantly better model than less rows of data.
Both of these myths lead some (lately it seems many) people to conclude that data scientist will eventually become superfluous. With enough data and advanced algorithms maybe we don’t need these expensive data scientists…
Read on for a dismantling of these myths. There’s a lot more than “collect all of the data and throw it at an algorithm” (and even then, “all” the data rarely really means all, which I think deserves to be a third myth). H/T R-bloggers