What strategies can help you explain the data Visualisation effectively select all that apply?

Tell your data’s story with compelling visuals.

One of the essential aspects of being a data scientist is the ability to communicate data analysis results using various kinds of visualizations effectively.

Data is a story told in numbers, visualizing it is how you’re telling the story.

Unfortunately, we pay more attention to learning new analysis methods, libraries, and approaches, getting familiar with new datasets or trending machine learning and artificial intelligence algorithms, and ignore improving our visualization skills.

Don’t misunderstand me, being up to date with new technology is very important to have a successful career in DS. But we need to devote some time to get better at visualization and storytelling as well.

Imagine this; you spend hours upon hours cleaning data, exploring it, and modeling it. It’s interesting; your results are valid and of significant meaning. But, your data visualization is dull and ineffective. That leads to your audience overlooking your hard work.

Learning how to effectively visualize your data is like learning how to tell a compelling story.

Your choice of chart type, of colors, of style, will make a tremendous difference in how others will perceive your data.

Fortunately, there are simple guidelines that, if you follow, can make your data visualization both visually appealing, compelling, and captivating.

This article will present 7 simple tips to level-up your visualization based on scientific experiments and research.

Without further ado, let’s get into effectively telling a story with our data.

Tip №1: Simple is always better

The goal of using visualization is to make information easier to read and understand by others. So, having complex, crowded visualization is something to be avoided.

Whenever you’re creating a visualization, you need to pay attention to the data-ink ratio. Data-ink ratio is a term used to refer to the amount of data vs. redundant ink in the graph, such as background effects/ colors and 3D representation of the data.

Instead of using multi-dimensional graphs, you can use visualization properties, such as shape, color, and thickness, to differentiate and distinguish your various datasets.

For your visualization to be simple and effective, your data-ink ration needs to be high.

Tip №2: Choose the right chart type

Whenever you try to create a graph, you need to pay attention to your data type to select the correct chart to represent it accurately.

Based on the data you’re using, the type of chart you will use will differ. A good rule of thumb is:

1. If you have categorical data, use a bar chart if you have more than 5 categories or a pie chart otherwise.
2. If you have nominal data, use bar charts or histograms if your data is discrete, or line/ area charts if it is continuous.
3. If you want to show the relationship between values in your dataset, use a scatter plot, bubble chart, or line charts.
4. If you want to compare values, use a pie chart — for relative comparison — or bar charts — for precise comparison.

Tip №3: Visualize one aspect per chart

Before creating a chart, you need to decide what exactly you want to show. Do you want to show patterns or details? To make your visuals more effective, try to display only one aspect at a time.

If you need to show two sides of your data, a pattern and some details, use two different plots. For example, you can use a line chart to show details and a heatmap or horizon graph to show the pattern within the data.

Horizon graphs display multiple time-series in parallel. Horizon graphs are similar to a time-series plot. However, in horizon graphs use color to highlight differences and extreme across time-series.

Tip №4: Make your axis ranges interesting

The range of your vertical and horizontal axes depends on the type of chart and the story you’re trying to tell with it.

For example, if you’re using a bar chart and only to show the maximum values of different datasets, your axes need to start from 0.

However, if you want to show fluctuation in your data in precise numbers, you need to zoom in your axes to make this fluctuation clear. It is easier to see variations in a dataset when the plot limits are closer to the fluctuation range.

Tip №5: Emphasize change rate with data transformation

The decision to use a transformation in your visualization depends on both your dataset and the intent of the plot. Applying transformations on your graph can change the impression and the information conveyed by your chart.

Generally speaking, you can transform two aspects of your graphs. Your axes or your data itself.

Transforming your axes

When plotting a set of data, you can either use a linear or a logarithmic scale. A logarithmic scale is often used to display the percentage of change during a period of time, so the points on the scale are not positioned equidistantly.

A linear scale, on the other hand, is used to display the absolute difference between various unique points of your dataset.

Transforming your data

Logarithmic scales are sometimes challenging to understand by people, so a way to avoid it is to transform your data. For example, instead of displaying absolute values, you can normalize your values to the mean or a specific value.

Tip №6: Be careful with overlapping points in Scatter plots

When using a scatter plot, sometimes two or more circles may overlap each other, which could make reading the data more complex. It can also hide the actual size of a specific cluster within the graph.

One thing you can do to avoid this problem and make your scatter plot more meaningful is to use different opacities for your circles to visualize all of your data points clearly.

Another strategy to achieve a similar effect is to plot unfilled circles. This approach may not be beneficial in the case of large datasets, then, using the opacity option may be a better choice. You can also change the sizes of the circles to have an overall clearer visualization.

Tip №7: Be careful with your color scheme

Colors can make or break your graphs. When you’re creating new visuals, you need to be careful when selecting a color scheme. To choose the best color scheme, you need to ask yourself two questions.

Is the color visible on different platforms?

Sometimes when we build charts on our devices to use in a presentation or a meeting, we forget to test how this chart will appear on different platforms.

Will they be clear when displayed on a computer or a phone? What about the lighting? Do I have to use high screen brightness to see the chart clearly, or does it work regardless?

What media will I use to display my chart?

If you’re creating charts to be printed, the type of paper may affect your choice of colors. Sometimes a color that is clear on your screen may not be apparent when printing on a specific kind of paper.

Moreover, try to use fewer colors or related colors to deliver your message. If you’re creating a heatmap, you need to use the gradient of one color and not different colors. Using different colors may confuse and make your map difficult to understand.

Conclusion

Visualizing data is often the best and most straightforward approach to communicate this data across to a broad audience. Whenever we try to create charts and figures, we need to make them simple, direct, and easy to read.

Remember, your data tells a story, and your choice of visualization can either make this story exciting or downright dull.

So, following 7 simple steps, you can quickly improve the quality and readability of your visualization:

1. Simple is always better.
2. Your axes ranges make a huge difference.
3. Focus on one aspect per chart.
4. Choose the right chart type for your data.
5. Use transformations to emphasize change.
6. Be careful with overlapping circles in a scatter plot.
7. Don’t overdo it with color schemes.

References

[1] Healey, C. G. (1996, October). Choosing effective colors for data visualization. In Proceedings of Seventh Annual IEEE Visualization’96 (pp. 263–270). IEEE.

[2] Evergreen, S. D. (2019). Effective data visualization: The right chart for the right data. Sage Publications.

[3] Kelleher, C., & Wagener, T. (2011). Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software, 26(6), 822–827.

What strategies can help you explain the data visualizations effectively select all that?

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Which strategy would allow the audience to absorb the data visualizations select all that apply?