03 Mar 2017
Clustering is a broad set of techniques for finding subgroups of observations within a data set. When we cluster observations, we want observations in the same group to be similar and observations in different groups to be dissimilar. Clustering allows us to identify which observations (i.e. customers, students, states) are alike, and potentially categorize them therein. Check out our new tutorials covering k-means and hierarchical clustering.
17 Feb 2017
Principal components analysis (PCA) reduces the dimensionality of our data, allowing most of the variability to be explained using fewer variables than the original data set. This allows us to understand the primary features that can best represent our data. Check out the latest tutorial which covers PCA
03 Feb 2017
Logistic regression is a foundational analytic technique for classification problems. It allows us to estimate the probability of a categorical response based on one or more predictor variables and tells us if the presence of a predictor increases (or decreases) the probability of a given outcome by a specific percentage. Check out the recently added tutorial on logistic regression.
20 Jan 2017
Linear regression is a useful and widely used statistical learning method and serves as a good jumping-off point for newer predictive analytic approaches. Check out the newly added linear regression tutorial that covers the basics of this power analytic technique.
07 Jan 2017
Analysts are often trained to handle tabular or rectangular data that are mostly numeric, but much of the data proliferating today is unstructured and typically text-heavy. Many of us who work in analytic fields are not trained in even simple interpretation of natural language. Fortunately, many of the principles used to organize, analyze, and visualize tabular data can be applied to unstructured text to extract meaning from this information.
Check out the first couple text mining tutorials that have been released. Additional tutorials will be released in the coming weeks.