Which chart should you use for your data? | Datasketch
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Which chart should you use for your data?

Choosing the right chart is necessary to communicate data effectively, as it acts as a bridge between the complexity of information and the clarity of the visualization. With various chart options, it’s essential to understand each one to avoid mistakes in visual communication. Learn to understand charts in a simple way to select the most suitable one and bring your data to life in a comprehensible and effective way. Let your data speak for itself! #DataVizForKids

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Data visualization, or DataViz, is a discipline that simplifies the transmission of information from a wide set of data, in a clear and effective manner. Using visual elements like charts and diagrams, it aims to capture attention and tell purposeful stories. Through these visual representations, we can simplify complex data so the human brain can absorb, analyze, and, if necessary, make decisions based on it.

Learn more at: What is Data Visualization?

It’s important to note that a poor selection or design of a chart not only makes data harder to understand, but it can also make the information more complex to grasp, distorting the meaning of the message. While visualizations can sometimes be manipulated to influence the perception of information, the most common risk is that an incorrect selection leads to misinterpretation.

In our daily lives, we’re surrounded by data that is presented to us in different visual forms. However, despite data visualization being present in our everyday lives, not everyone has the knowledge to select, create, or correctly interpret a visualization.

To avoid potential risks of misinformation or poor communication, we need to consider several factors: the type of information being represented, the dataset we’re working with (including its source, structure, quality, and any possible limitations), and the various chart options available.

For the last point, it’s essential to consider who will consume the information, what is being communicated, and how it’s reaching the audience—that is, what kind of information they are receiving. This includes choosing colors, adjusting text sizes, and other visual elements that can influence perception and attention.

That’s why this resource is designed to help everyone working with or interested in data to understand and use each chart correctly and simply, making their data or its interpretation come to life through clear, accessible, and effective visualizations, capable of conveying complex information in a comprehensible way for all audiences. This edition of #datavizfordummies aims to democratize access to data science.

We primarily focus on simple charts, which are the most common and the ones many data are presented with every day. These basic charts are essential for communicating information concisely and effectively, using simple elements like bars, shapes, lines, or dots. Each chart offers a unique way to explore and present data, and by being simple, they become an accessible resource for everyone.

Learn to use simple charts with our app.

Below, we present a catalog of different types of simple charts. In this catalog, you’ll find descriptions of each chart, detailed information, and most importantly, visualizations that will help you understand and teach these concepts. This resource is for anyone who wants to transform how they present and understand their data.

Bar Chart

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A bar chart is a visual representation of information that is useful for comparing and analyzing data across two axes: the X-axis and the Y-axis. This type of chart is particularly helpful for clearly and concisely showing differences between categories or data groups. Learn more

Pie Chart

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Pie charts are used to represent the proportion of parts relative to a whole. The colors within a pie chart make it easy to distinguish which categories are larger or smaller in a dataset. Learn more

Treemap

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A treemap is a type of visualization that organizes data hierarchically. Its structure resembles a tree (hence the name), and the data is represented by nested rectangles, one inside the other. The size of each rectangle corresponds to the value of the category or subcategory, and their classification is shown through different colors, allowing intuitive identification of the categories. Learn more

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