A data scientist from New Zealand has developed innovative SQL syntax for non-random sampling. This method identifies ‘interesting’ data points from large datasets. The approach includes commands like ‘select typical 10’, ‘select unusual 10’, ‘select comprehensive 10’, and ‘select representative 10’. These commands help in exploratory data analysis by finding average, unique, comprehensive, and representative records respectively. The techniques use Gower’s distance in a difference matrix to measure dissimilarity. When applied to a dataset of countries and territories, the results were striking. Five typical countries identified were the Dominican Republic, the Philippines, Mongolia, Malaysia, and Thailand. The most unique countries included Afghanistan, Cuba, Fiji, Botswana, Tunisia, and Libya. A comprehensive list of seven countries, representing a wide range of conditions, comprised Mauritania, Cote d’Ivoire, Kazakhstan, the Dominican Republic, Kuwait, Slovenia, and Germany. The six territories most different from each other were Sweden, the USA, the Democratic Republic of the Congo, Palestine, and Taiwan. Conversely, the six most similar countries were Denmark, Finland, Germany, Sweden, Norway, and the Netherlands.
Source: www.reddit.com

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