000			01592nam a2200133 4500
008			250901b |||||||| |||| 00| 0 eng d
100			_aSangarsu Raghavendra*
245			_aScalability of data science algorithms; Empowering big data analytics
300			_aPP1-13
520			_aScalable data science algorithms are required in the dynamic eld of big data analytics due to the exponential growth of data, in order to eciently extract valuable insights. In order to overcome the di culties presented by large datasets, this research investigates the critical role that scalable algorithms play. e study explores machine learning methods designed forlarge data analytics, distributed computing, and parallelization strategies. It starts with the constraints of standard algorithms and ends with the revolutionary inuence of scalability on practical applications. e actual use of scalable techniques is demonstrated through case studies from prominent industry players, including Google, Facebook, and Amazon.ese case studies highlight improved decision-making and superior business strategies. cWith an eye toward the the article looks at new developments in algorithm design, hardware, and soware, making sure scalability is still crucial fort ackling issues with even bigger datasets
654			_aScalable Algorithms _aBig Data Analytics _aExponential Data Growth _aLarge Dataset _aMachine Learning Methods _aCase Studies _aBusiness Strategies
773	0		_0131673 _9113505 _dNew Delhi Enriched Publications _tJournal of Artificial Intelligence and Soft Computing research
942			_cJA
999			_c132685 _d132685