K-means clustering is a very popular unsupervised learning algorithm. It takes your data and learns how it can be grouped. Through a series of iterations, the algorithm creates groups of data points — referred to as clusters — that have similar variance and that minimize a specific cost function. By using the within-cluster sum of squares as cost function, data points in the same cluster will be similar to each other, whereas data points in different clusters will have a lower level of similarity.
Linear regression is a linear approach to modelling the relationship between a dependent variable and one or more explanatory variables. In simple linear regression, a single independent variable is used to predict the value of a dependent variable. In multiple linear regressions, two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. In a situation where you need to estimate a quantity based on a number of factors that can be described by a straight line — you know you can use a Linear Regression Model.
Hypothesis Tests, or Statistical Hypothesis Testing, is a technique used to compare two datasets or a sample from a dataset. It is a statistical inference method so, in the end of the test, you'll draw a conclusion — you'll infer something — about the characteristics of what you're comparing. Before even thinking about what test you are going to use, you need to define your hypothesis to set the significance level of the statistical test, and then you're good to pick the statistical test!
In domains like Science and Engineering, documenting your experiments is crucial. Scientists too relied on notes, drawings, annotations and later on pictures — virtually any kind of record — to support their hypotheses and advance the collective knowledge of the scientific community. This article shares and celebrates the work of the Pioneers of Data Visualization, the people who paved the way to in Infographs and data visualization techniques that are so popular and widespread today.