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008			190907s2019 xxu| o |||| 0|eng d
020			_a9781484248584
040			_dWaSeSS _cAIMIT LIBRARY
050		4	_aQA76.73.P98
072		7	_aUMX _2bicssc
072		7	_aCOM051360 _2bisacsh
072		7	_aUMX _2thema
082	0	4	_a005.76 _21 _bVARE
092			_aEBOOK
100	1		_aVarga, Ervin _930379
245	1	0	_aPractical Data Science with Python 3 : _bsynthesizing actionable insights from Data / _cBy Ervin Varga.
250			_a1st ed.
260			_aBerkeley : _bApress : _c2019.
300			_aXvii, 462p. _c23.5 cm.
347			_atext file _bPDF _2rda
505	0		_aChapter 1.Introduction to Data Science -- Chapter 2.Data Acquisition -- Chapter 3.Basic Data Processing -- Chapter 4.Documenting Work -- Chapter 5.Transformation and Packaging of Data -- Chapter 6.Visualization -- Chapter 7.Prediction and Inference -- Chapter 8.Network Analysis -- Chapter 9.Data Science Process Engineering -- Chapter 10. Multi-agent Systems, Game Theory and Machine Learning -- Chapter 11. Probabilistic Graphical Models -- Chapter 12. Security in Data Science.
506			_aRequires an SPL library card.
520			_aGain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.
538			_aMode of access: World Wide Web.
650		0	_aEstimating the edge betweenness centrality _930380
650		0	_aPartitioning the model into a bipartite graph _930381
655		7	_aElectronic books. _2local _930382
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781484248584
776	0	8	_iPrinted edition: _z9781484248607
856	4	0	_yView this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. _uhttps://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484248591/?ar _zAn e-book available through full-text database.
942			_2ddc _cBK _e1st _k005.76 VARE
999			_c222479 _d222479
999			_b03658530