Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It

7/20/2014

This webpage on Wired provides an overview of the importance of topology in data analysis. The article explains that topology is a branch of mathematics that studies the properties of space that are preserved under continuous transformations. It highlights how topology can be applied to data analysis to reveal hidden patterns and structures. The article discusses various examples where topology has been used to analyze complex data sets, including the classification of objects, network analysis, and understanding brain activity. It also touches upon the concept of persistent homology, which allows for the identification of topological features that persist across different scales in a dataset. Overall, the article highlights the significance of applying topology in data analysis to gain deeper insights.

topology ꞏ data visualization ꞏ data analysis ꞏ data science ꞏ network analysis ꞏ graph theory ꞏ complex networks ꞏ data modeling ꞏ data processing ꞏ data exploration ꞏ data interpretation ꞏ data representation ꞏ data mapping ꞏ data patterns ꞏ data structures ꞏ data complexity ꞏ data connectivity ꞏ mathematical modeling ꞏ network topology ꞏ data sets ꞏ data research ꞏ data-driven insights ꞏ data algorithms ꞏ data organization ꞏ data transformation ꞏ data integration ꞏ data analytics ꞏ data mining ꞏ data measurement ꞏ data visualization techniques ꞏ data discovery ꞏ data-driven decisions ꞏ network visualization ꞏ network patterns ꞏ network modeling ꞏ network connectivity ꞏ complex systems ꞏ data-driven solutions ꞏ information visualization ꞏ information analysis ꞏ information modeling ꞏ information connectivity ꞏ information retrieval ꞏ information complexity ꞏ information patterns