Some more terminology

At 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 terms that you are likely to encounter:

• Machine learning: This refers to giving computers the ability to learn from data without explicit “rules” being given by a programmer. Earlier in this chapter, we saw the concept of machine learning 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 to automate the discovery of relationships in data and create powerful data models.
• Statistical model: This refers to taking advantage of statistical theorems to formalize relationships between data elements in a (usually) simple mathematical formula.
• Exploratory data analysis (EDA): This refers to preparing data to standardize results and gain quick insights. EDA is concerned with data visualization and preparation. This is where we turn unstructured data into structured data and 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: This is the process of finding relationships between elements of data. Data mining is the part of data science where we try to find relationships between variables (think the spawn-recruit model).

I have tried pretty hard not to use the term big data up until now. This is because I think this term is misused – a lot. Big data is data that is too large to be processed by a single machine (if your laptop crashed, it might be suffering from a case of big data).

The following diagram shows the relationship between these data science concepts.

Figure 1.3 – The state of data science (so far)

With these terms securely stored in our brains, we can move on to the main educational resource in this book: data science case studies.