000			01912nam a22002537a 4500
005			20250702095413.0
008			250702b |||||||| |||| 00| 0 eng d
020			_a978103294561
040			_aCSL _cCSL
041			_2eng _aeng
084			_aB28 R1 _qCSL
245			_aData Science for Mathematicians
260			_aNew york : _bCRC Press/Taylor & Francis, _c2021.
300			_axv,528p. _b: ill. _c; 24 cm.
490			_aCRC Press/ Chapman and Hall Handbooks in Mathematics Series
500			_aIncludes bibliography and index.
520			_aData Science for Mathematicians presents the experience and insight of mathematicians who have retrained themselves as data scientists. Readers with a mathematical background and some computing expe-rience can use this book as a pathway to teaching in a data science program or starting research in the field.Students of data science, as they learn techniques and best practices, often ask why the techniques work and how they became best practices. A. mathematical mindset is uniquely qualified to answer those why questions, and thus mathematicians' involvement makes the teaching of data science more robust.The next generation of data scientists will be trained by faculty in the related disciplines of statistics, computer science, and mathematics. While pure mathematicians may be unfamiliar with data science, they have powerful skills that, if deepened in the ways set out in this book, would prepare them not only to teach the next generation of data scientists, but also to answer compelling questions with data. Gaining such power has reinvigorated the careers of many mathematicians
650			_aLinear algebra.
650			_aClustering. _9733203
650			_aMachine Learning. _9480917
650			_aDimensionality Reduction. _9459955
700			_aCarter, Nathan _eeditor. _9815135
942			_2CC _cTEXL _hB28 R1 _n0
999			_c1433394 _d1433394