A Data Science Central Community
Data scientists rely on tools/products/solutions to help them get insights from data. Gregory Piatetsky of KDNuggets conducts an annual survey of data data professionals to better understand the different types of tools they use. Here are the results of the 2015 survey. He followed his initial posting with additional analyses to better understand which tools go together. In the latter posting, he looked at pairs of tools and found that some tools tend to go together (usage of tools are correlated with each other). He offered the anonymized raw data set for free to encourage other people to analyze the data, which I did.
His approach looked at pairs of tools to understand their relationship with the other. I took a slightly different approach. I applied principal components analysis. The current approach groups the tools by looking at the relationship among all tools simultaneously. In general, principal components analysis examines the statistical relationships (e.g., covariances) among a large set of variables and tries to explain these correlations using a smaller number of variables (components).
Click here to read the full, rather long and great article, with illustrations, clickable links, and access to data sets. It is written by Bob Hayes. Below is a small extract of the products comparison table to solve this problem; the full version is available in the article.