What is Data Analytics?

You hear about the growing need for data analysis, so what is data analysis? There's lots of definitions, but this is pretty close. It's the process of inspecting, cleaning, transforming, and modeling data for business decision-making. So what is a data analyst? Well, it's a person who does data analysis, of course, but their whole job is to help find better answers for better decisions, and the world needs a lot of them. If you search for data analyst jobs, you'll see a wide variety of job descriptions, and they use words like data, calculations, and analysis. 

Learning how to identify data, and group it is a fundamental skill for an analyst. If you want to be an analyst you have to see data even when it's not obvious or provided. And remember, data is everywhere. 

Okay, let's start with a simple example of a chair, can you see any data? Some people would say "Well, I see it's color, it's red." Some would say "Well, it's armless." Others would say "Well, it is a chair." These are all examples of data. A data analyst must dig deeper for the data, though so let's look at some more data options. 

So the chair has dimensions, like height and width. It also has a weight, these things impact how much it costs to ship. There's also type of materials for the chair and even the manufacturer. Bringing in the manufacturer comes with another set of options to consider like, serial number, does the chair have a warranty? If it does have a warranty it might possibly have a warranty date. You might also be curious how many of the chair you ordered?

What does data look like? 

Let's start with types. So Data Types are a classification on one of the various types of data such as real, integer, or Boolean. But, I don't want to oversimplify it but data really only has so many types. Let's take a look at several of them that you'll get familiar with. You have Text or String, things like first name, last name. Date and Time for things like the date of the purchase. 

Number which could be used for unique identifiers or how many or how much of something. And then the Boolean, yes or no, true or false, on or off, zeroes or negative ones. Data analysts work with fields and values as well. The Field Headings are what we usually see as the top row or the label of the data. As you look at these examples, be sure to think of their data types. The Field Heading represents what it is and the Value represents what it actually is.

There is a certain expectation that data analysts can make some interpretations and summarize the data. Knowing that reports can be a high-stakes game, you want to make sure you understand how to get data into its raw format, so that then you can collectively analyze it.