The classic saying “correlation does not imply causation” is still an incredibly important thing to keep in mind when doing data analysis. Spurious regressions will sneak up on you and the next thing you’re doing is trying to predict the value of the Mexican Peso based off of the amount of rainfall in London.
Keep the following in mind when doing data analysis and stating that there is a causal relationship: Does this relationship make sense? That simple question is not asked often enough – don’t make that mistake.
Another common pitfall is discarding outliers in order to establish a model that fits the data better. Tampering with data is very dangerous and needs to be handled in a completely transparent way when presenting your analysis.
After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection in images. As it turns out, Google has done a phenomenal job with their Vision API. It’s absolutely incredible the amount of information it can spit back to you by simply sending it a picture.
Also, it’s 100% free! I believe that includes 1000 images per month. Amazing!