From the course: Ethics and Law in Data Analytics

From analytics to AI

- As I begin I'd like to help define some of these terms we're using analytics, big data, data science, just so we can have some context as we proceed in these modules. So to begin I like to think of an axis of scale and complexity for these terms. And I think of analytics or business intelligence as you've heard kind of on the lower end of scale and complexity, not to say that it's not hard or we're not doing things that are complex in nature, but just that to date we deal with small to medium data that's maybe it's kind of relative by gigabytes or below perhaps, enterprise reporting systems, things you could do on your computer. But as we get up in the scale axis I like to think of that as big data. It's not too different, it's not really that much more complex, it's really just perhaps faster, bigger, and in higher velocity it's coming in. Might do somethings like real time analytics on top of that, there is some overlap with those two terms. And as we see there's some overlap again with data science so data science is maybe more on the complexity side of things we're using some predictive modeling, you're using some more algorithmically driven things to answer specific questions, maybe at a smaller scale. You're sampling things you're trying to do that more complex analysis. And lastly as we get into artificial intelligence really it incorporates a lot of these different elements of big data of data science but then gets into even more advanced techniques like deep learning. So just to recap, analytics and big data and data science I kind of put on the left here a visualization, reporting, storage, some predictive modeling, regression, classification, and clustering. Whereas artificial intelligence we get into things like predictive modeling with human like augmented behavior. So that's much more complex we're trying to mimic things that are human like like understanding language or seeing things with our eyes, computer vision and the like.

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