Online College Courses for Credit

+
Data Analysis

Data Analysis

Author: Katherine Williams
Description:

Identify the four components of data analysis.

(more)
See More

Try Our College Algebra Course. For FREE.

Sophia’s self-paced online courses are a great way to save time and money as you earn credits eligible for transfer to many different colleges and universities.*

Begin Free Trial
No credit card required

37 Sophia partners guarantee credit transfer.

299 Institutions have accepted or given pre-approval for credit transfer.

* The American Council on Education's College Credit Recommendation Service (ACE Credit®) has evaluated and recommended college credit for 32 of Sophia’s online courses. Many different colleges and universities consider ACE CREDIT recommendations in determining the applicability to their course and degree programs.

Tutorial

Video Transcription

Download PDF

This tutorial covers data analysis. Data analysis is the process of describing data. Typically, when we're describing data, we're going to use three different things to talk about it. We're going to talk about the shape. We're going to talk about the center. And we're going to talk about the spread.

Now, when we're doing the data analysis and trying to talk about shape, center, and spread, we're going to both use graphs and numerical calculations. We can't rely just on graphs or the calculations alone. We need the two in tandem in order to accurately describe and analyze our data.

Now, the first thing that we can talk about is shape. And that's essentially how the data looks in a graph. Later tutorials will cover exactly how we talk about shape and what kinds of things we can describe.

As far as center goes, that's describing the middle of the data. Now, we can describe this in general like the middle is around here. And again, there are a couple of different ways that we can describe middle, or we can use numerical calculations.

For spread, that's talking about how close or how far values are from the center. And again, we can do this in general like the values are far away or very close to, or we can use a numerical calculation in order to describe it more precisely. One other thing to note with data analysis is the presence of outliers. Outliers are data values that are significantly higher or lower than a majority of the values.

And it's important to note that it's not just an extreme value. So it's not just that you have a really big number, like one million. There must be a big gap between that higher low value and the bulk of the data. So if our data set was the net value of millionaires, a million or a billion dollars wouldn't be that extreme because most of the data would be in that range, in that high range. In order to have an outlier, we'd need to have one value that was significantly higher than the rest of it, and where there's a big gap for that. This has been your tutorial on data analysis.

Terms to Know
Center

The "middle" of the data set. There are many measures of center.

Data Analysis

The understanding of the key features of a set of data - shape, center, spread, and outliers.

Outliers

Points in a data set that are so high or so low as to be unusual, given the rest of the values.

Shape

The qualitative description of the clustering of data points in a certain location when the data are graphed.

Spread

The numerical description of how close the numbers are to the center.