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Data analytics is the process of analyzing data sets to make decisions about the information they contain, while data visualization is the graphical representation of data and information in a pictorial or graphical format. Data visualization tools help users see and understand trends, patterns, and outliers in data. Data analytics can help identify areas that need attention or improvement.
Data visualization can help tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting useful information. Data visualization tools include: Charts, Maps, Heatmaps, Pie charts, and Scatter plots. Data visualization and storytelling can be done using various tools and languages, such as Excel, PowerPoint, Tableau, Power BI, or Python.
Here are some types of data visuals: Conceptual-Declarative: Use common visual metaphors to illustrate complex ideas and relationships Conceptual-Exploratory: Idea generation Data-Driven-Declarative: Everyday data viz Data-Driven-Exploratory: Visual discovery
The three Cs of data visualization are correlation, clustering, and color.
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