Kirk Borne

Outlier Detection in Python — https://t.co/l5qPXcl0ul —— #Statistics #DataScientist #DataScience —— I have always appreciated the unusual, unexpected, and surprising in science and in data. As famous science author Arthur C. Clarke once said, “The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ (I found it) but ‘That’s funny!’” This is the primary reason that I motivated most of the doctoral students that I mentored at GMU to work on some variation of Novelty Discovery (or Surprise Discovery) for their Ph.D. dissertations. “Surprise discovery” for me is a much more positive, exciting phrase than “outlier detection” or “anomaly detection”, and it is much richer in meaning, in algorithms, and in new opportunities. Finding the surprising unexpected thing in your data is what inspires our exclamation “That’s funny!”, which may be signaling a great discovery (either about your data’s quality, or about your data pipeline’s deficiencies, or about some wholly new scientific concept = SURPRISE DISCOVERY!). As famous astronomer Vera Rubin said, “Science progresses best when new observations (data) force us to alter our preconceptions.”