This is a good time to define some more vocabulary. By this point, you're probably excitedly looking up a lot of data science material and seeing words and phrases I haven't used yet. Here are some common terminologies you are likely to come across:
We have seen the concept of machine learning earlier in this chapter as the union of someone who has both coding and math skills. Here, we are attempting to formalize this definition. Machine learning combines the power of computers with intelligent learning algorithms in order to automate the discovery of relationships in data and create of powerful data models. Speaking of data models, we will concern ourselves with the following two basic types of data models:
While both the statistical and probabilistic models can be run on computers and might be considered machine learning in that regard, we will keep these definitions separate as machine learning algorithms generally attempt to learn relationships in different ways.
We will take a look at the statistical and probabilistic models in the later chapters.
EDA is concerned with data visualization and preparation. This is where we turn unorganized data into organized data and also clean up missing/incorrect data points. During EDA, we will create many types of plots and use these plots to identify key features and relationships to exploit in our data models.
Data mining is the part of data science where we try to find relationships between variables (think spawn-recruit model).