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7 Ways to Take Your Visualizations to The Next Level

Better visualizations help prove your effort

Photo by fauxels from Pexels
Photo by fauxels from Pexels

If you read some of my other articles, you must have noticed that I tend to repeat a couple of sentences, like "Data Science is all about data," or " you can’t spell data science without data," or my personal favorite "as a data scientist, you’re just attempting to deliver your data’s story."

In my opinion, data scientists are a special type of storyteller. They get handed a dataset, often messy and unstructured, and then they are required to refine this dataset, find its patterns and trends, and help deliver the story it tells. This story is then used to help make better business decisions and help enhance a service or a product for the users.

But, storytelling is one skill that is unlike other skills required from a data scientist. It makes use of your creativity, design skills, and ability to explain and deliver information in the simplest, most efficient way. Although it may seem like a challenging skill to improve at first, once you know the basics of efficient visualizations, you can improve your skills quickly.

Data Visualization 101: How to Choose a Chart Type

If you want to create effective visualization that helps deliver the story of your data best, then you need to pay attention to some specific pieces of information. Once you consider these pieces, you will create beautiful, efficient, and simple visualizations that anyone can understand and that prove the effort you have invested in finding the hidden story.

№1: Choose the best graph type for your data

The first step to better visualizations is knowing your data and determining which chart fits it best. There are various types of charts and graphs out there, each designed and created to describe a specific form of data. For example, a bar graph is a good option if you’re comparing few values of data within the same category, like car sales for two or more companies over a specific period of time.

A line plot is used to visualize numerical trends over a time interval. Scatter plots are used to show relationships between variables. Histograms show the distribution of data through an interval, and a pie chart shows the proportional distribution of data points within a category.

So, by knowing what each chart is used for, you’ll be able to use the one the enhances your data’s story and help deliver it best. When it comes to graphs, you shouldn’t be afraid to combine different types or try new ones to help deliver your story even better.

№2: Keep things simple

Simplicity is the best strategy always. I know that sometimes we tend to choose complex visualization because if it looked complex, it would convey the feeling that we worked hard on analyzing and building that visualization, which is never the case. If you understand something fully, then you can explain it.

Choose simple charts; if you have multiple types within the same visualization, make sure you structure them in a way that’s easy to follow and understand. The easier it is to understand your work, the better it will be perceived by others.

№3: Pay Extra attention to the text in your visualizations

I know a picture is worth 1000 words, but we can’t create a visualization that’s completely text-free. We also can’t fill our visualization with redundant text, which will take away from its value and make it look messy. So, once you decided what graphs and charts to use, you need to take some time and decide on everything text-related.

When creating a visualization, you will often include three types of text, titles, levels, and legends. A visualization title needs to be descriptive, precise, and to the point. Your audience should understand what the visualization presents by only reading the title without needing any extra information.

Data Visualization 101: How to Choose a Python Plotting Library

Next up, label, make sure your labels are clear, have a readable size and simple fonts, and don’t forget to label your axes. Legends should be used wisely and should make understanding your graphs easier. For all these texts, you need to use simple phrases, avoid annotation unless necessary, avoid fancy distractive fonts, and avoid repeating your message.

№4: Make use of your colors

Next up, colors. Let’s dig into our creative and artistic side – everyone has one, trust me. Colors are powerful tools that can transform your visualization. You can choose the correct graphs, use simple, efficient texts, yet use the wrong colors to highlight your story, causing your visualization to end up being confusing and misleading.

So, utilizing color theory can be the step you need to start creating better visualization. When dealing with colors, make sure to use the same color for the same kind of data and if you want to match text with specific parts of a graph, make sure to use the same color for both. Avoid using too many colors because they gat get confusing.

You can also utilize shades, hues, and color temperature to help differentiate data intensity. If you need help deciding colors or thinking of a matching palate, you can use tools like Colors help and Coolors.

№5: Go for dynamic visualization more than static

When we are dealing with data, we often need to visualize data over time, and we need to compare the different instances of the data. One approach you need to consider is using interactive visualizations, where you can remove some categories of data to make the graph better to understand.

Another option is to create a Gif of the same base graph over different time intervals or different conditions to highlight the changes in the data. This strategy can also be used as a part of a presentation so you can take your audience through the changes step-by-step.

Data Visualization 101: 7 Steps for Effective Visualizations

№6: Always keep your audience in mind

Some may put knowing your audience as the first step to create better visualizations, but there’s a reason I decided not to. If you create good visualization, it should be easy to understand for different people with different backgrounds. This is similar to user experience design; often, companies try to address the largest population when they design their interfaces.

That being said, generalization is not always the best option to go with. Sometimes, you need to create visualizations for a specific audience. For example, if you work in a company, they might have specific requirements for the visualizations you need to create. In such cases, you will need to follow these rules while making your data understandable.

№7: Get inspired by data visualization designers

Data Visualization is a form of art, and some of the best ways to learn and improve your storytelling are to study and get inspired by other people’s work. The field of data visualization design has so many great artists that spend their time creating stunning visualizations that you can use for inspiration or gather ideas. If you need inspiration, this list is a great place to start.

Takeaways

Sometimes when I look at other data scientists and data visualization designers’ work, I feel like they are artists rather than data scientists. The way they choose their charts, their colors, and their fonts make me aspire to become a better data scientist and a more efficient storyteller.

4 Data Visualization Tools To Transform Your Data Storytelling

My journey to improve my data visualization skills was driven by more than just the desire to become a better data scientist. See, improving your visualization skills means you will become a better presenter, a better speaker, and a better researcher if you’re in academia. We have all been on the receiving end of a lousy data visualization, and we all had thought, "this could’ve been much more efficient if the chart type was…".

But, just like any other skill, improving your storytelling and visualization needs practice, time, and some inspiration. In this article, I walked you through the steps I followed to improve my visualization skills. Although my skills are still not the best they could be, by following the different ways proposed in this article, you and I can take steady steps towards better visualizations.


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