One core motivation to analyze big data is to find intrinsic patterns. This chapter contains recipes that answer questions about data deviation from the norm, existence of linear and quadratic trends, and probabilistic values of a network. Some of the most fascinating results can be uncovered by the following recipes:
Calculating a moving average
Calculating a moving median
Approximating a linear regression
Approximating a quadratic regression
Obtaining the covariance matrix from samples
Finding all unique pairings in a list
Using the Pearson correlation coefficient
Evaluating a Bayesian network
Creating a data structure for playing cards
Using a Markov chain to generate text
Creating n-grams from a list
Constructing a neural network perception