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What are some tools and resources that you use for data visualization?

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Data visualization is the process of transforming data into graphical or interactive forms that can communicate insights, patterns, or trends. Data visualization can help you explore, analyze, and present data in a more engaging and effective way. In this article, we will introduce some basic concepts, tools, and resources for data visualization in the context of statistics.

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1 Why data visualization?

Data visualization can help you explore and understand data, analyze and interpret data, as well as present and communicate data. Through visualization, you can discover patterns, outliers, relationships, or distributions in your data and generate hypotheses for further analysis. It is also useful for testing hypotheses, comparing groups, identifying correlations, or evaluating models. Furthermore, it can be used to tell a story, persuade an audience, or make a recommendation. Data visualization is a great way to highlight the key messages of your data and draw out the insights and implications.

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Hatem Yazidi, PhD

Senior Data Scientist
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I recommend using Seaborn a popular data visualization library in Python that provides a high-level interface for creating informative and attractive statistical graphics. Seaborn's benefits include its ability to create complex and aesthetically pleasing plots with just a few lines of code. For example, violin plots are a graph that combines a box plot and a kernel density plot. This makes it easier to visualize a dataset's distribution. Alternatively, heat maps can be used to visualize the correlation between two or more variables. These tools can help data analysts understand and present their data more intuitively and efficiently. Seaborn is also flexible, allowing users to tweak and modify plots according to their needs.

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2 What are some data visualization types?

Data visualization comes in many forms, each with its own advantages and disadvantages. Popular examples include charts, which are graphical representations of data; maps, which are spatial representations of data; tables, which are tabular representations of data; diagrams, which are schematic representations of data; and dashboards, which are interactive representations of data. Charts can be used to compare, trend, proportion, or categorize data; maps can be used to show location, distance, direction, or distribution; tables can display values, numbers, or text; diagrams can illustrate processes, flows, structures, or hierarchies; and dashboards can provide summaries, overviews, or details.

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3 How to choose a data visualization type?

When selecting a data visualization type, you must take into account the characteristics of your data, such as the type, size, format, quality, or source. Additionally, you need to be aware of your goal; what is the purpose of the data visualization? What question, problem, or message are you trying to address? You should also be cognizant of your audience and their level of interest, knowledge or expertise about the data. All these factors will affect the visual representation and design of your data visualization as well as its communication to your audience.

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Javi Bravo-Alegria, Ph.D.

Analytical Development, Quality Control, QbD, Control Strategies, Raw Materials, Cell Therapy expert | PhD in Biomedicine
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I think one of the most importan things is to tell the story you planed to tell. You have your planned ideas, your numbers, results all over. Plots are a way to simplify the complexities of Science. The idea of graphics is to make people visually interested, and bring up their own ideas and thoughts after they see your lines.

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4 What are some data visualization tools?

Creating data visualizations can be done with a variety of tools, ranging from simple to complex, and from general to specific. Excel is a widely available spreadsheet software that can produce charts, tables, or pivot tables. Tableau is a powerful and user-friendly software that enables interactive dashboards, charts, maps, or stories. R and Python are both programming languages that are versatile and open-source, and they both have many packages and libraries for data visualization.

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Mark Everitt
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Almost all my plots in my papers including 3d visualisation is done in gnuplot. It’s free, multi platform and open source. It is also hugely powerful. For those who use latex PGF plots, is also really very useful indeed, especially as it makes it very easy to update plots within a manuscript without using external packages. Both take a little while to learn, but have a very high return on time invested

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Jacob Rossner

Data Analyst/Scientist
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Using the Plotly.js module in JavaScript within an HTML file is another great way to create & embed powerful visualizations that can be interacted with. Viewers can choose to view subsets of the data, enlarge or shrink portions of the object, and investigate metadata at the discretion of the creator.

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5 What are some data visualization resources?

If you're looking to learn and improve your data visualization skills, there are many resources available. Books like The Visual Display of Quantitative Information by Edward Tufte, Storytelling with Data by Cole Nussbaumer Knaflic, and Data Visualization: A Practical Introduction by Kieran Healy can help you understand the theory and practice of data visualization. There are also blogs such as FlowingData by Nathan Yau, The Functional Art by Alberto Cairo, and Visualising Data by Andy Kirk that offer tips, examples, or critiques of data visualization. If you're looking for courses, Duke University's Data Visualization and Communication with Tableau on Coursera, Johns Hopkins University's Data Visualization with R on Coursera, and DataCamp's Python for Data Visualization are all great options.

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John Agosta

Talent Management, Organizational Effectiveness & HR Professional | Consultant | Trainer | Adjunct Professor | DE&I Advocate
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Honestly, I follow some analytics thought leaders like Keith McNulty for example and he posts tons of content that shows different visualizations and methods to use in programs like R.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some tools and resources that you use for data visualization?

1

2

3

4

5

6

1 Why data visualization?

2 What are some data visualization types?

3 How to choose a data visualization type?

4 What are some data visualization tools?

5 What are some data visualization resources?

6 Here’s what else to consider

Statistics

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What are some tools and resources that you use for data visualization?

1

2

3

4

5

6

1 Why data visualization?

2 What are some data visualization types?

3 How to choose a data visualization type?

4 What are some data visualization tools?

5 What are some data visualization resources?

6 Here’s what else to consider

Statistics

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