![]() Different types of databases and systems and architectures and applications for storing data. As we'll see when we look at our sample data in the hands-on portion, you may have tables that store customer details separate from transactional detail. Things like relational databases where we're storing our information in independent tables and writing in logic and conditions that relate those tables together. Just as data itself can come in many different flavors and types, databases too are varied. Those rows and columns and tables generally make up our databases. Those rows and columns of data, we can think of as rows and columns in tables and those tables in a database and the use of all of these make up our different system. But much of data analytics is done using spreadsheets, looking at rows and columns of data. But at a very simple level, data is descriptive, numeric, or other, and I say other because it could be an image, it could be a audio file. It can be structured, it can be unstructured, it can be at rest, it can be in transit, it can be natural language, it can be programmatic language. That data can come in many, many different forms. In a moment of philosophical introspection the other day, I was pondering what came first, data or analysis? But wherever you fall on that question, data is a requisite of conducting data analytics. But of course, to conduct any data analytics we need data. One of the great things about data analytics is that it takes very little to get going, and that which it does take is often very accessible in a public or free manner. In this lesson, we'll identify tools essential for conducting data analysis, including spreadsheets and databases.
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