000			02021nam a2200397Ia 4500
005			20260803094233.0
008			251212s9999 xx 000 0 und d[2017][2017][2017]
245	1		_aData science / _cby Lillian Pierson ; foreword by Jake Porway.
240	1		_aData science for dummies.
100			_aPierson, Lillian,|eauthor
700			_aPorway, Jake,|ewriter of introductory text.
260			_aHoboken, NJ : _bJohn Wiley and Sons, Inc., _c[2017].
250			_a2nd edition.
300			_axvi, 364 pages: _billustrations, charts
300			_c24 cm.
490			_a--For dummies.
020			_a9781119327639
650			_aInformation technology.
650			_aInformation retrieval.
650			_aDatabases.
650			_aData mining.
944			_aCIR
050			_a004 P61d c2017
943			_b0 _c0 _d0 _e0 _f1 _g1 _h1 _i0 _j0
944			_b1 _c1 _d0
953			_pHS1714
546			_ain English
953			_cHigh School Library
505			_aBegins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations.
591			_a0
336			_astill image sti|2rdacontent.
337			_aunmediated c|2rdamedia.
338			_avolume cr|2rdacarrier.
942			_2lcc _cBK _ajderpo _b2022-03-03 09:05:33 _djderpo _e2022-03-03 09:05:33
998			_a11756
999			_c11649 _d11649