Data visualization techniques help create a visual representation of your information, usually in a graphical presentation. It helps the presenter communicate their data to a viewer that is much easier to digest, interpret, and draw conclusions from. The visuals may help display patterns or trends so that analysts can draw takeaways from said information. Data visualization is used in various fields and industries. For example, business and finance use charts and graphs to show trends in expenses, revenue, and profits; while in healthcare, medical professionals use them to analyze patient data, track disease outbreaks and monitor healthcare outcomes. In government, agencies use visualizations to show demographics, economic indicators, and impacts of policies.
Common Data Visualization Techniques
Regardless of the field of study, data visualization is a helpful tool to present information. There are many types, but the most common are bar charts, line charts, pie charts, scatter plots, and histograms.
Bar Charts
Bar charts are one of the most common and effective data visualization techniques to represent categorical data. In a bar chart, the bars are used to display values associated with groups. The length of the bar corresponds to the magnitude the data represents making it easy to compare the values across categories. They can be either horizontal or vertical with the latter being more common. For instance, say someone surveyed a school classroom about their favorite subjects in school: 9 of the students said language arts, 3 students liked math, 7 people said history was their favorite subject, and 6 preferred history. Below is an example of how the data is displayed in a bar graph.
From the bar chart, we can see the distribution of students’ favorite school subjects—the most favored class is language arts while the least is math, then history and science close in value. It is helpful to also label your x and y-axis to make the graph more straightforward. In this case, the y-axis can be labeled as the number of students and the x-axis is the subjects.
Line Graphs
Another common data visualization is a line graph. Line graphs are used to show trends over a continuous interval, usually time. The key feature of this visualization is the use of lines to connect data points showing progression and changes in values. The x-axis typically displays an independent variable (e.g. time), while the y-axis is the dependent variable. They’re great visualizations to show the rise and fall of values. It’s commonly used in finance, science, and social sciences. For instance, a meteorologist wants to count the amount of rainfall in inches for every month. Below is a line graph to show the trends in rainfall per month.
From the line graph, the views can see the trend of rainfall during the winter and summer months. We could conclude that it rains more during the winter months and straggles during fall and spring.
Pie Charts
Pie charts are circular statistical graphs that are divided into slices to present portions of data. The size of the slice is proportionate to the quantity represents. Most commonly used to illustrate the distribution of categorical data, pie charts emphasize the relationships of each category in relation to the whole set. This is effective for showing proportions. Pie charts are used in every field but are common in business, science, marketing, and education presentations. Consider the following example: research takes a demographic poll on the race/ethnicity of a high school classroom in Southern California. The chart below shows their findings.
The pie chart shows the audience that the majority of the students are Hispanic followed by White as the second most common race. This data visualization is useful to show the ratios of categories condensed into an image.
Scatter Plots
Next, scatter plots are a type of data visualization that displays individual data points on a graph. Each point represents two variables the x-axis and y-axis components. They are useful in showing relationships and/or correlations between two quantitative variables. The data points sometimes show patterns such as trends, outliers, and positive or negative correlations. For example, a researcher is interested in the relationship between how many hours studied and grades received on the final exam. The following scatter plot is an example of such display.
For this scatter plot, the y-axis represents the percent grade one can receive on a test (100%), and the x-axis shows the number of hours spent studying for this final exam. The viewer can see the positive relationship between hours studied and grades received, where the time spent studying, the higher scores those students receive on their exams. If a positive or negative relationship can be depicted through the plots, an analyst sometimes draws a best-fit line to represent the trend.
Histograms
Finally, histograms are graphical representations of the distribution of a quantitative variable. They show the frequency of the data during specific intervals along a continuous scale. Histograms look like bar graphs except the bins (or the rectangles) are touching rather than having gaps. In addition, histograms are show frequencies and not categories. The histogram is effective for showing the shape, central tendency, and general spread of the dataset. It is used a lot in statistical analysis and research. For instance, an epidemiologist is recording a virus’ distribution in affecting age ranges in the population.
The histogram above shows a negatively skewed distribution depicting age (x-axis) and infection (y-axis). We see here that the virus affects mostly the older population.
Importance of Data Visualization Techniques
The use of data visualizations is important across various disciplines for effective communication, analysis, and decision-making. The visual representations of data such as bar graphs, line graphs, pie charts, and histograms enhance our ability to comprehend information, identify patterns, and extract meaningful insights. The importance of data visualizations as a tool for understanding and interpreting information is crucial in multiple aspects of education and employment.
Author: Maerie Morales