Data-driven astronomy
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Data-driven astronomy (DDA) refers to the use of data science in astronomy. Several outputs of telescopic observations and sky surveys are taken into consideration and approaches related to data mining and big data management are used to analyze, filter, and normalize the data set that are further used for making Classifications, Predictions, and Anomaly detections by advanced Statistical approaches, digital image processing and machine learning. The output of these processes is used by astronomers and space scientists to study and identify patterns, anomalies, and movements in outer space and conclude theories and discoveries in the cosmos.
This article was nominated for deletion. The discussion was closed on 24 May 2024 with a consensus to merge the content into the article Astroinformatics. If you find that such action has not been taken promptly, please consider assisting in the merger instead of re-nominating the article for deletion. To discuss the merger, please use the destination article's talk page. (May 2024) |
It has been suggested that this article be merged into Astroinformatics. (Discuss) Proposed since May 2024. |
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