It’s happened to all of us sooner or later: The hypothesis seemed plausible, the data was clean, the conclusions sound. Our recommendation was damn near foolproof. Yet when put into practice, the result was anything but favorable.
How could that happen? Data science is about, well, DATA. And science, which implies a reliable method. We have the information, we have the models, we aren’t just shooting in the dark here. Where did it go wrong?
The truth is there are lots of things…Continue
When learning data science a lot of people will use sanitized datasets they downloaded from somewhere on the internet, or the data provided as part of a class or book. This is all well and good, but working with “perfect” datasets that are ideally suited to the task prevents them from getting into the habit of checking data for completeness and accuracy.
Out in the real world, while working with data for an employer or client, you will undoubtedly run into issues with data that you…Continue
In order to write a tutorial about classification, it was necessary to find an example that was broad enough that it would need to be sub-divided. Since I actually care about whether you remember this stuff, it needed to be something that a lot of people like and would relate to. And since I have a lot of international subscribers, it needed to be cross-cultural as well. So what is universal, cross-cultural, and dearly loved?
There’s American beer,…Continue
What do Target and the NSA have in common?
They both used Big Data and analytics in a way that inspired a major slap on the hand. Target was predicting which of their customers were pregnant and sending them targeted coupons for baby products, which prompted the ire of a father whose teenage daughter hadn’t told him yet. The NSA incurred the wrath of world leaders, the US Congress, and even a good portion of the American public when the…Continue
Added by Randal Scott King on July 20, 2014 at 3:53pm — No Comments