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999			_c16708 _d16708
001			21869752
005			20241012062519.0
008			210114s2021 flu b 001 0 eng
010			_a 2021000953
020			_a9780367486846
020			_z9781003042259
040			_aDLC _beng _erda _cDLC
042			_apcc
050	0	0	_aQA185.D37 _bY67 2021
082	0	0	_aDC 512.5 _22021 _bY831
100	1		_aYoshida, Ruriko, _eauthor. _944656
245	1	0	_aLinear algebra and its applications with R / _cRuriko Yoshida, Naval Postgraduate School.
250			_aFirst edition.
260			_aBoca Raton : _bCRC Press, _c©2021.
263			_a2106
300			_axvii, 405 pages : _billustrations ; _c23 cm.
490	0		_aTextbooks in mathematics
500			_aIncludes bibliography and index.
504			_aIncludes bibliographical references and index.
520			_a"The book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs. These students tend not to realize the importance of linear algebra in applied sciences since traditional linear algebra courses tend to cover mathematical contexts but not the computational aspect of linear algebra or its applications to data science and bioinformatics. The author presents the topics in a traditional course yet offers lectures as well as lab exercises on simulated and empirical data sets. This textbook provides students a theoretical basis which can then be applied to the practical R and Python problems, providing the tools needed for real-world applications. Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied science. These exercises start from easy computations, such as computing determinants of matrices, to practical applications on simulated and empirical data sets with R so that students learn how to get started with R along with computational examples in each section and then they learn how to apply what they learn to problems in applied sciences. This book is designed from first principles to demonstrate the importance of linear algebra through working computational examples with R and python including tutorials on how to install R in the Appendix. If a student has never seen R, they can get started without any additional help. Since Python is one of the most popular languages in data science, optimization, and computer science, code supplements are available for students who feel more comfortable with Python. R is used primarily for computational examples to develop student's practical computational skills"--
650		0	_aAlgebras, Linear _xData processing. _944657
650		0	_aR (Computer program language). _944357
776	0	8	_iOnline version: _aYoshida, Ruriko, _tLinear algebra and its applications with R _bFirst edition. _dBoca Raton : Chapman & Hall, CRC Press, 2021. _z9781003042259 _w(DLC) 2021000954
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cDC