000			02918nam a2200313Ia 4500
003			OSt
005			20250916130052.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9780128147610
037			_cTextual
040			_beng _cCSL
041			_aeng.
084			_aB28 Q9 _qCSL
100			_aKotu, Vijay _eauthor. _9821564
245		0	_aData Science _b: Concepts and Practice
250			_a2
260			_aCambridge : _bMorgan Kaufmann Publishers, _c2019.
300			_axix, 548p. ill. _ccm
500			_aIndex 533-544p.
520			_aLearn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain languag Describes the commonly used setup options for the open source tool RapidMiner
650			_a Rapid miner. _9821565
650			_aRegression methods. _9821566
650			_aText mining. _9821567
650			_aTime series forcasting. _9821568
650			_aStatistics. _9821569
700			_a Deshpande, Bala _eco-author. _9821570
942			_hB28 Q9 _cTEXL _2CC _n0
999			_c1196 _d1196