In our first subtopic, we will start to build our fundamentals in dealing with data. By understanding the data in front of us, we can start to have a better idea of where to go next. We will begin to explore the different types of data out there as well as how to recognize the type of data inside datasets. We will look at datasets from several domains and identify how they are different from each other and how they are similar to each other. Once we are able to comfortably examine data and identify the characteristics of different attributes, we can start to understand the types of transformations that are allowed and that promise to improve our machine learning algorithms.
Among the different methods of understanding, we will be looking at:
- Structured versus unstructured data
- The four levels of data
- Identifying missing data values
- Exploratory data analysis
- Descriptive statistics
- Data visualizations
We will begin at a basic level by identifying the structure of, and then the types of data in front of us. Once we are able to understand what the data is, we can start to fix problems with the data. As an example, we must know how much of our data is missing and what to do when we have missing data.
Make no mistake, data visualizations, descriptive statistics, and exploratory data analysis are all a part of feature engineering. We will be exploring each of these procedures from the perspective of the machine learning engineer. Each of these procedures has the ability to enhance our machine learning pipelines and we will test and alter hypotheses about our data using them.