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4 Essential Resources to Help Improve Your Data Visualisations

Creating compelling data visualisations is essential for any data scientist, geoscientist or petrophysicist. By learning these skills, we can ensure that we can convey our research and analysis results to our intended audience. By understanding our audience, we can tweak the charts and the messages accordingly. For example, we could have a more simplified and artistic chart for the general public but a more complex one for presenting at a conference with our peers.

Developing data visualisation skills can take time and practice; however, numerous free resources available on the internet can help you with the process.

These resources range from explaining data visualisation best practices to selecting appropriate colour schemes and providing inspiration for your next visualisation.

Within this article, I share four of my favourite resources for working with data visualisations, which you may find valuable and interesting.

I am not affiliated with any of the resources listed below — I want to share various tools to help fellow data scientists improve their data visualisation skills.

Learn the Do’s and Don’ts for Effective Data Visualization — 99 Data Viz Rules

Making effective charts involves making your data and message as straightforward as possible. This includes avoiding 3D pie charts, not presenting too many bars on a bar chart and avoiding too many colours to represent the data.

However, these rules should sometimes be used as guidance, as there may be times when you need to trend away from them to create the effect you are after.

This is where the 99 Data Viz Rules Project comes in. It is a project developed by a UK-based digital agency, addtwo digital, that specialises in delivering lectures and seminars to help people communicate more effectively through data visualisations.

As a result of a student question about creating a checklist to follow when creating data visualisations, the company developed the “99 Data Viz Rules and Why It’s OK To Break Them” series.

This consists of several commonly used rules when creating data visualisations and also shows examples of how and when they could be broken to create more intriguing visualisations. Additionally, the authors also present suitable alternatives to present the data.

An example of how arranging your slices on a pie chart can influence its intuitiveness. Image by

If you are ever presented with a data visualisation rule, ask yourself what the intended outcome is for your data visualisation, who your audience is and what message you want to get across.

Also, check out AddTwo’s series below, which is nicely documented and easy to explore.

Choosing Colour Palettes to Make Your Data Pop With Adobe Leonardo

Choosing colours for your data visualisation can sometimes become a huge time suck. However, ensuring the right and most appropriate colour scheme is essential to creating compelling and readable data visualisations.

When selecting the right colour palette for our data visualisations, we must consider various factors. This includes ensuring there is suitable contrast between the chosen colours and making sure our palette is appropriate for readers with colour blindness and related issues.

Understanding these various factors will ensure we get our message across to the audience as best as possible.

I have covered numerous colour palette tools in my past articles.

However, out of the ones I have discussed, I always find myself returning to Adobe’s Leonardo when I need to select a colour palette.

It is a comprehensive tool that allows you to analyze the colours in colour space, contrast and accessibility.

When you visit the website, you can choose whether to focus on colour design for user interfaces, data visualisations or access tools to compare and evaluate colours.

Available tools within Adobe Leonardo.

As we are focusing on data visualisations within this article, we would be best accessing the Color Scales options.

When the Color Scales page appears, we can visualise and customise our colour palettes using three data visualisations: a heatmap, scatterplot and a map. This allows you to get a feel of how your chosen colour palette will appear on these visualisations.

Leonardo in action when selecting colour palettes for data visualisation.

To see how to use Leonardo effectively, I highly recommend checking out this article by Nate Baldwin

Find Inspiration for Taking Data Viz to the Next Level — Information is Beautiful

Sometimes, creating data visualisations for scientific research and analysis involves creating detailed charts to help you understand your data and to derive key findings from it.

However, when presenting that data to a different audience, such as the public, who may have limited knowledge of your subject, you have to think entirely differently about how you present your data.

This can be a daunting task, especially if you are used to creating scientific figures for your colleagues or as part of your studies.

What I find helpful when I need inspiration is turning to websites that focus on making powerful, beautiful and impactful data visualisations. Even if you are not looking for inspiration, it can be great to look at ways that data can be turned into art.

The Information is Beautiful website is run by David McCandless and showcases various data visualisations that go beyond basic scatter plots and bar charts.

For example, David has taken simple country population data and converted it into this interactive and unique data visualisation.

Chart showing population differences between countries within different regions of the world. Image created by David McCandless and data sourced from the United Nations.

Check out the Information is Beautiful website below for more charts like this:

Unlock the Full Potential of Matplotlib For Stunning Visualisations — Matplotlib Documentation

Matplotlib is one of my favourite and most used data visualisation libraries within Python. However, it does have a reputation for creating boring figures and being difficult to work with.

But, with a little bit of work and some extra Python code, you can easily transform some basic matplotlib-generated figures into something that is significantly better.

It can often be challenging to know what charts or styles are possible within matplotlib; however, I have found matplotlib’s documentation to be really great to look through and easy to understand

When you go to the Plot Types page of the documentation, you are presented with visuals of the different chart types, which is handy if you have forgotten the name of a specific chart.

Matplotlib documentation for different plot types that are available. Image from the matplotlib documentation

Also, the matplotlib team has put together a gallery page that you can browse through different chart types and styles. Each item within the gallery comes with complete code examples, which you can use to recreate and modify to suit your needs.

Examples of different charts and effects that can be generated with matplotlib. Image from the matplotlib image gallery.

Think of this page as a giant cheatsheet that you can use to help you when selecting charts, styles, markers and much more. Check it out at


Within this article, we have seen four separate but very useful resources that can help transform the way you create data visualisations. These range from improving the colour schemes to getting inspired by others.

If you have encountered any resources you regularly use and want to share, it would be great to hear about them in the comments section.

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