A data visualization, while creatively pleasing, should also serve a functional purpose in effectively communicating data - which refer as combining both art and science. In this context, we can refer to data visualization as a modern art form. Instead of providing step-by-step instructions for enhancing your dashboard, this article will introduce seven design principles as a solid foundation for this “modern art” field.
We delve into the intricacies of Gaussian Naive Bayes classification. The focus is on determining the probability of a data point belonging to a specific class among several, emphasizing probabilistic assessment over precise labeling. The article breaks down key concepts, from Bayesian decision theory to Bayes' theorem, and provides a step-by-step implementation using the Iris dataset.
The topics covered include Degenerate Dimension, Conformed Dimension, Role-Playing Dimension, Junk Dimension, Outrigger Dimension, and Slowly Changing Dimensions (SCD). The SCD category further delves into different types, such as Type 0 to Type 7, each with its unique approach to handling historical and changing data.
In this article, I will introduce the concept of the Basic Fact table in Dimensional data modeling. To understand this technique, we will explore the different types of data modeling and recap some fundamental knowledge, including the star and snowflake schemas, and the concepts of normalization.
Some folks asked me about statistics - probability, toward which I answered that I had only studied a bit and knew very little about statistics. Then they said are these are one?. In fact, Statistics and Probability are distinct from one another
