Data visualization takes your data (numbers) and places it in a visual context, such as a chart, graph, or map. It also helps create data stories that communicate insights with clarity.

You can use the filtered data set as you would a static data set, such as: Inputs.table(my_filtered_data2) Numerical and date filters work similarly to Input.select() .

The vastness and distributed nature of modern data in enterprises have given a sharp rise to the development of sophisticated business intelligence (BI) tools such as PowerBI, Tableau, Sisense and ...

Handling Large Datasets. Employ data manipulation libraries like pandas in Python or dplyr in R to preprocess and clean large datasets before visualization. Consider using data streaming techniques ...

We’re inviting you to show off your data visualization and storytelling skills. Create a data visualization using a dataset of CU Boulder trees using your preferred software or tools. (You can even ...