This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
Bayesian inference is the re-allocation of credibilities over possibilities [Krutschke 2015]. This means that a bayesian statistician has an “a priori” opinion regarding the probabilities… Read More »Naive Bayes Classifier using Kernel Density Estimation (with example)
As you might realize by now, writing SQL queries is one of the essential skills any inspiring data analyst needs to master. After all, larger… Read More »A beginner’s guide to BigQuery Sandbox and exploring public datasets.
