000			02039nam a2200229 4500
005			20251017161238.0
008			241213b |||||||| |||| 00| 0 eng d
020			_a9783031174414
041			_aeng
082			_a006.312 _bQamD2
100			_aQamar, Usman
245			_aData Science Concepts and Techniques with Applications [2nd ed.] / _cUsman Qamar and Muhammad Summair Raza
250			_a2nd ed.
260			_aSwitzerland : _bSpringer, _c©2020.
300			_axxiv, 474p.
520			_aThis textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book.
650			_aMachine learning
650			_aPython (computer program language)
650			_aBig Data
700			_aRaza, Muhammad Summair
942			_cBK
999			_c7199 _d7199