Draft:Data Driven Astronomy

Data-Driven Astronomy (DDA) is the contribution of Data science in the field of Astronomy, particularly through the emergence of Astronomical Data Science. These disciplines focus on using advanced statistical methods, Digital image processing, and Data mining to analyze vast datasets from astronomical observations. It plays a role in various aspects of Astronomy, including galaxy classification, anomaly detection, and feature extraction. In 2007, Galaxy Zoo project utilized DDA techniques to classify galaxies based on their shapes and spin, a task that would have been extremely time-consuming using traditional methods. The processes in DDA involve Data collection, Data analysis and Normalization which are used in Classifications and Predictions using Machine learning to cluster and classify Astronomical object like Galaxy, Meteoroid, Nebula and Star. The processes involve manipulation of Big data which is obtained from several repositories and outputs of Astronomical survey conducted by several laboratories and organizations. The heaviest data is released by the Pan-STARRS comprising of 1.6 Petabytes of data. DDA impacts reducing the workloads of processing these datasets and enhances Astronomers and Space Scientists to focus on patterns and conclusions to work on the advancement of existing theories.

Methodology
One of the main challenges in Data-Driven Astronomy is the processing and analysis of large datasets, which can range from Terabytes to Exabytes in size. To address this challenge, astronomers use Parallel computing, Cloud computing, and advanced Data processing techniques to efficiently handle and analyze massive amounts of data.

Technical Requirements Involved
Data mining techniques, such as Cluster analysis and Anomaly detection, are widely used to analyze astronomical data. These methods help astronomers identify patterns, outliers, and rare objects in the universe, leading to discoveries and insights.