Data analysis has become an increasingly important and complex endeavor over the last few years.
In fact, data scientists are a hot commodity – the Bureau of Labor Statistics predicts that the field of computer and data science will grow by 15 percent through the end of the 2020s.
One of the key tasks of data scientists is to gather, sort, analyze, and report vast amounts of data. To do so, data analysis procedures are used to examine a wealth of information in a short amount of time.
Don't get left behind as the data science industry evolves. In as few as 18 months, earn your MS in Applied Data Science from Syracuse University, ranked #19 in Best Online Graduate Computer Information Technology Programs. Apply today and join Syracuse iSchool’s growing alumni network of over 10,000 data professionals. No GRE required.Visit Site
No GRE is required for the University of Denver's online MS in Data Science. In as few as 18 months, you'll gain knowledge and skills in critical competencies such as programming, data mining, machine learning, database management, and data visualization. The program is available to students from all academic and professional backgrounds.Visit Site
The online Master of Information and Data Science (MIDS) program is preparing the next generation of experts and leaders in the data science field and providing students with a UC Berkeley education without having to relocate. Students graduate with connections to UC Berkeley’s extensive alumni network in the Bay Area and across the world. All international applicants will be required to submit official Test of English as a Foreign Language (TOEFL) scores.Visit Site
Gain technical skills in SQL, Python, and R and learn to drive successful business outcomes with your master's in business analytics online from UD. In 12 months, you will be prepared to pursue professional analytics roles. No GRE required.Visit Site
Related resource: 15 MOST AFFORDABLE BACHELOR’S IN DATA SCIENCE ONLINE
Complementary to that is data visualization. The short definition of this process is the practice of creating an image or a graph to represent numerical data. As you’re about to see, though, data visualization is much more complex.
Data Visualization Defined
Data visualization is a way of presenting abstract information in visual form.
For many people, looking at rows and columns of numbers does not make a lot of sense. Complex statistics can be confusing as well. But translating abstract data into a visual medium makes it easier to convey information to others.
Data may be translated into size, length, width, color, shape, and position, just to name a few. Seeing attributes like these in the form of an image or graph often makes the data much easier to digest. For many people, it is easier to think about an abstract topic when they have an image that puts all of the information together in a meaningful way.
What’s more, data visualization allows people to identify patterns, examine trends, and identify outliers amongst the data. In other words, data visualization makes it easier for data scientists (and normal, everyday consumers of information) to draw conclusions based on the data that has been collected.
In fact, data visualization is a critical component of the data science process. Data is gathered, processed, modeled, and analyzed. At that point, data can then be visualized to assist in the process of drawing conclusions about the data. Think of data visualization as the vehicle by which data scientists can quickly and efficiently disseminate information.
History of Data Visualization
If we look at the history of data visualization, we get a better sense of how long humankind has been trying to help others better understand data.
The first instance of the use of visualization tools dates to the Turin Papyrus Map in 1160 B.C. The map was an accurate representation of the geological resources in the area. Other ancient examples of data visualization include thematic maps, ideograms, and hieroglyphs.
Fast-forward to the invention of parchment and paper, and we see a relative explosion in the use of data visualizations. Graphs, illustrations, and charts became much more common and accessible.
In the 1600s, for example, Michael Florent van Langren, a Flemish astronomer, created what is believed to be the first representation of statistical data. This was followed in the 1700s by thematic mapping and abstract graphs of functions.
The next steps in the history of data visualization involved famed French philosopher and mathematician Réne Descartes. Along with Pierre de Fermat, Descartes developed a two-dimensional coordinate system that enabled mathematicians to display and calculate values more easily. And Fermat, with the help of Blaise Pascal, worked in the fields of statistics and probability, and their work laid the foundation for what we know today as data.
From there, William Playfair developed graphical methods of statistics. In 1854, Dr. John Snow, a physician in London, used data visualization to show the outbreaks of cholera across the city.
In the early 20th Century, newspapers, magazines, and textbooks began including more and more charts and graphs. The public found these graphical representations of data to be much more understandable than long passages of text.
Later in the 20th Century, John W. Tukey developed a new statistical method of exploratory data analysis as well as the science of data visualization as it pertains to statistics. Around the same time, Jacques Bertin began using quantitative graphs as a means to represent information in cartography. These breakthroughs, along with the publication of Edward Tufte’s book The Visual Display of Quantitative Information in the 1980s, were instrumental in defining what data visualization means in the modern world.
So, in a very real way, data visualization developed as crude drawings of the surrounding lands and evolved over the centuries into a highly technical method of representing complex data.
Today, data visualization is big business, with many different companies offering software suites that help data scientists and others bring their data to life. SAS, Minitab, SOFA, and Cornerstone are just a few statistical data visualization tools.
Who Uses Data Visualization?
Data visualization isn’t a process that a few data scientists working in a laboratory use on a regular basis. Instead, data visualization has a place in virtually every type of career.
For example, meteorologists use maps to help their viewers understand the current weather. Imagine how difficult it would be for weather forecasters to tell their audience about when and how a storm will develop without being able to show it on a map!
As another example, teachers use visual aids all the time – maps, graphs, charts, images, and videos help relay important information in a way that is often much more engaging to students than a simple lecture from their teacher.
Here’s an example from the world of finance: Investors have to track how well (or poorly) their investments are doing, and using graphs and charts to track this data makes it much easier to quickly see how much money has been gained or lost over a period of time.
Data visualization is commonly used in healthcare, too.
For example, choropleth maps enable healthcare professionals to visualize how a specific health variable, like prevalence of diabetes, changes from one geographic area to the next.
In logistics, data visualization is used to determine the most efficient global shipping routes for cargo. In politics, data visualization is used in the form of maps to identify which voting districts voted for what candidate. In sales and marketing, marketing professionals examine visual representations of web traffic to see which web pages and websites are generating the most traffic.
Simple bubble charts are types of data visualization as well. Heat maps, bulleted lists, infographics, and scatter charts are additional examples. Other examples include:
- Time series charts
- Area charts
- Line charts
- Population pyramids
In other words, data visualization is used by many different people in many different contexts. And it needs to be – in a world in which we are constantly bombarded with information, having visual means to make sense of all that information is critical.
Why Data Visualization Works
Earlier, we touched on the fact that data visualization is a vessel by which large amounts of data can be disseminated quickly and efficiently. But why do these methods work so well?
It’s simple – our brains are wired to look for colors and patterns.
Think about it – for people with color vision, it’s easy to spot a red dot in an array of black dots. The pattern created by a line graph is eye-catching as well. These and other forms of data visualization grab our attention, and, more importantly, keep our interest.
This is critical because visualization of data keeps consumers of information on the message. I can write a 2,000-word essay on employment trends in the rural United States, but delivering that data in written form would be a time-consuming process for the reader.
Instead, plotting that data in a graph or chart can accomplish the same task of delivering the needed information, but in a way that’s much more engaging to the end-user.
Why Data Visualization is Important
There are many reasons why data visualization is so important in today’s world.
For starters, it tells a visual story.
It is easier to tell a story when pictures are involved. Visual data stories are also more engaging, especially for people who are not experts in the area of research.
Data visualization also allows a statistician or researcher to tell an integrated story based on information. It is easier to have a discussion about data when there is a visual representation of the information as well.
As noted earlier, people are visual creatures, and having an image to look at in partnership with the hard numbers creates a more interesting narrative and compelling case for planning an action.
Secondly, data visualization is important because it’s a crucial method of communicating with others.
While simple graphs or charts can convey quite a bit of data, we can do one better.
When it comes to data visualization, colorful, detailed pictures with different shapes and different-sized elements are much more engaging and enjoyable to view. And don’t worry about overwhelming the viewer with too much information. Our eyes can take in an incredible amount of information in a very short period of time. Now, obviously there comes a point at which a visual representation has too much going on. But, by and large, we can digest a lot of information very quickly through data visualization techniques.
Of course, data visualization is a great tool for helping build data literacy.
In today’s integrated economy, people have to have some working knowledge of information that is outside of their area of expertise. Data visualization makes this easier.
For example, using data visualization in a meeting could allow a person who is not an epidemiologist to understand how an epidemic of hepatitis A unfolded in a community and spread to surrounding areas. This type of information could be shown to nurses, community planners, pharmacists and others who have some knowledge of this issue, but not in-depth training in the epidemiology of infectious diseases.
Data visualization also enhances the data literacy of non-professionals who may be interested in a subject but who do not want to get too far into the details and numbers that are behind the information.
For example, if you were interested in learning about the vaccination rates against smallpox in various countries around the world, you could easily find graphs, charts, and many other types of visual information that offer that data in an easy-to-understand format.
A further advantage of data visualization is that it allows more people to make sense of numeric information. Putting data together this way makes it easier for a statistician or researcher to tell a convincing story, share information with non-mathematicians, and explain complicated results or studies.
So we can see that data visualization offers many advantages.
On the one hand, it allows us to see the correlation between two or more variables. On the other hand, we can easily see trends over time. Related to that is the ability to understand the frequency of events, like how often customers click on pop-up windows offering a discount when they are buying clothing on a website.
Data visualization offers the benefit of helping analysts compare different markets. By using these techniques, business owners can get a clear picture as to which market segments or demographics they need to focus on with their advertising.
Is Data Visualization for You?
Building an understanding of what data visualization is allows any data scientist, researcher, statistician or related professional to inform a wide community of important results. But it also helps everyday people learn, grow, and develop a better understanding of concepts with which they are unfamiliar.
As you can see, data visualization is a complex undertaking, but one that has great value in many corners of work and everyday life. As such, it is a growing field of study. In fact, you can major in data analytics and visualization as an undergraduate, get a master’s degree in data visualization, and get a PhD. in data informatics with a specialization in data visualization.
With the emergence of Big Data in recent years, and the ever-expanding universe of information we have at our fingertips, trained data visualization specialists will likely be in high demand for the near future. With a better understanding of what data visualization is, you can determine whether this is the right career field for you.