Statistics are numbers that result from measuring empirical data over time and then infers relationships within the data for all of you. I know probably almost all of you have had statistics. I had to learn statistics. I never took statistics in college, and then teach myself enough statistics to get through all of this material, and be able to turn around and share it with you. It can provide a basis for us to infer hypotheses from. The discipline is both descriptive. This is a min value, this is a max value, this is the median value, this is the variance. So, we've got some numerical lead descriptive notions at play here. But it's also inferential and allows us to make predictions about a whole dataset from a smaller sample or make predictions as new data horizons. Okay. So, that second case, that was user-driven. Data mining is a term that's used to refer to analyzing data and making some sense out of it. We want to uncover hidden insights in this data. That's what we're after. That's why we want to look at all this data. I'm going to make sense of it. We want to find patterns, we want to learn something from it. It is mainly concerned with classifying and clustering the data. We saw like k-means. K-means can look for clusters in your data and hopefully that will tell you something about the questions you're seeking answers to. Often the first step in analytics is attempting to uncover these hidden insights. You may not even go to a machine-learning algorithm, you may use something like k-means, and then there's other techniques out there as well. Just start taking a look at the data, and turning it around and looking at it in different dimensions, and so forth, to uncover any insights that you see any structure in the data. You may hear, when you get out in the business world and you're going to get a job and get employed, and so forth, you might hear this term business intelligence so you'll read about it in an article, Wall Street Journal or something. Business this is different from data mining, I just throw it in here as a note. Business intelligence is about building a model that answers very specific business question. For example, the one I have here. What's my expected product volume in a given average selling price? The people who own a business unit and companies ask this question all the time, as the price goes up, the volume goes down, how much margin is there on that and they do the calculation and they can calculate profit. So, doing that analysis probably won't uncover hidden insights in previous sales data, for instance. Just as an example. So, there's a distinct difference between business intelligence and data mining.
