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[Free] Descriptive Statistics And Visualizing Data

A Beginner’s Guide To Using Descriptive Statistics, Creating Charts and Pivot Tables, and Visualization for Business – Free Course

Added on February 11, 2023 Development 3 min read

What you’ll learn

• Calculate descriptive statistics using Excel
• Create graphical representations of data using Excel
• Create Pivot Tables
• Create Bar Charts
• Create Forecasted Trend Lines
• Describe measures of center
• Describe variability with the range and interquartile range (IQR)
• Describe variability with the variance and standard deviation

Requirements

• This course is intended for beginners. No prior statistics knowledge is required.

Description

The data visualization process aims to make sense of the raw data, presenting it in a manner that is easy to understand even for non-experts.

Not everyone you are presenting data to will understand statistics, but most people can look at a visual and tell what the story is!

For example:

• Pie charts and bar charts are the most commonly used data visualization techniques; they can be created easily using standard spreadsheet programs like Microsoft Excel.

• Numerical data can be represented in histograms, while categorical data can be visualized using frequency tables and charts.

• Cumulative frequencies count the number of data points up to a given value and are used to find the median, quartile, and percentiles in a data group.

• Relative frequency represents how often something happens relative to some total. For example, the relative frequency may be used to show that a given sales team won 10 of its last 13 contracts, while the cumulative frequency will show the median number of contracts won across the entire year.

You can get a sense of the importance of the data by looking at numbers on a spreadsheet, but a well-chosen visual representation of that data can be much more helpful.

Descriptive Statistics and Numerical Data Summary

Charts and graphs are useful for representing data in a user-friendly format, but there are times when it is more important to summarize data numerically.

A quality numerical data summary has meaning even to individuals who have no previous experience with the data being presented. You do not have to be a scientist or a doctor to understand that the average cholesterol for a given male population is 220, while the cholesterol level for women in the same population is 190.

For example:

To understand the power of numerical data summary, it is important to understand the difference between the median and the mean:

• the mean is the average of all observations, while the median is the point at which half the numbers are larger, and half are smaller.

• The mode is another important numerical data representation: it is the observation that occurs most frequently.

• The mean, median, and mode are important, but so is the variance.

• The term variance is used to measure how widely, or narrowly, spread out a group of numbers is. For instance, a set of numbers in which all values are 7 has zero variance. These terms summarize a set of numbers and help the observer make sense of the results.

Whether the data being represented is a list of baseball statistics, salary data for a Fortune 500 company, or the cholesterol levels of heart patients, the summarization techniques are the same.