Data Visualization Basics

Data visualization is a powerful technique for conveying data insights visually. Using graphs simplifies the understanding of complex data sets by highlighting trends, patterns, and anomalies. It makes the data more accessible to audiences without requiring them to analyze raw numbers.

Different chart types, such as bar, line, and pie charts, offer unique ways to visualize relationships in datasets. Selecting the right type illuminates specific patterns or comparisons in the data: for example, a line chart is a great way to show change over time, since the continuous line mirrors the continuity of time (as it is conventionally experienced and understood).

Bivariate and multivariate data visualizations represent more than one variable of interest. Bivariate visualizations compare two variables, while multivariate visualizations handle three or more. For example, scatter plots and single-line charts are bivariate charts, while bubble charts, multi-line charts, and stacked or grouped bar charts are all multivariate charts.

Univariate data visualizations depict a single variable and show characteristics like distribution, central tendency, or variability. Common examples include histograms, displaying frequency distribution, and boxplots, visualizing data spread and identifying outliers.

Data visualizations reveal relationships in data through visual properties like position, shape, size, and color. Effective use of these properties makes it faster to identify correlations or data patterns in visualizations. For example, color is commonly used to differentiate categories, size might indicate magnitude, and position can show patterns or trends.

Information redundancy is a technique that uses multiple visual cues to convey the same information. This enhances readability and makes visualizations universally accessible. By communicating data through multiple different forms like text, color, and shapes, we ensure that the audience is more likely to correctly and easily interpret and understand the information. Universal design principles underpin this practice, promoting better organization and prioritization of data.

Adjusting data visualizations to the intended audience enhances understanding. Tailoring the complexity to fit the audience can be helpful, since general audiences often benefit from straightforward graphics, and experts may appreciate detailed charts.

When designing data visualizations, it’s crucial to consider vision accessibility. This involves using colorblind-friendly palettes, ensuring readable fonts, and providing alt text for non-decorative visual elements. Color palettes must be checked for contrast and sufficient value difference. Recommended font practices include using sans-serif types and maintaining a minimum font size of 12-14pt for increased readability.

Annotations on data visualizations enhance the viewer’s understanding by providing context. They may highlight trends, indicate outliers, or explain interesting or unusual points in the data. They guide the viewer’s interpretation of the data, so they should be used thoughtfully.

Generative AI can help fill gaps in coding knowledge and increase efficiency when creating data visualizations. However, critical thinking and understanding real-world context are essential for creating meaningful visualizations, which LLMs cannot replace.

Use LLMs to explore new languages as coding buddies, but learn data visualization and design theory first. Engage in conversation with the LLM, iterating based on outputs, but maintain data privacy and check for plagiarism.