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03739aam a2200457 i 4500 001 FC5C39A43D8C11EE8AE814B62EECA4DB 003 SILO 005 20230818010103 008 191126n20212021maud 001 0 eng d 010 $a 2019955670 020 $a 9781284180909 020 $a 1284180905 035 $a (OCoLC)1128104662 040 $a YDX $b eng $e rda $c DLC $d NRM $d OCLCF $d FIE $d SAT $d YDX $d OCLCO $d OCL $d AAA $d OCLCO $d AKC $d IaU $d SILO 042 $a lccopycat 050 00 $a QA76.9.D343 $b J36 2021 082 14 $a 006.313 100 1 $a Jamsa, Kris, $d 1960- $e author. 245 10 $a Introduction to data mining and analytics with machine learning in R and Python / $c Kris Jamsa. 264 1 $a Burlington, Massachusetts : $b Jones & Bartlett Learning, $c [2021] 300 $a xviii, 668 pages : $b charts ; $c 24 cm 546 $a Includes index. 520 $a Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation. With a dual focus on concepts and operations, this text comprises a complete how-to and is an excellent resource for anyone considering the field. Case studies and hands-on activities incorporate real-world data sets and allow students the opportunity to exercise their new skills. Our Cloud Desktop integrates popular data mining tools, giving students a valuable familiarity with industry-standard applications. After defining the concepts of data mining and machine learning, Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use. The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world context through a practical application to the current issues of "big data" and of text and image data mining. The text concludes by describing an analyst's steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace. -- Amazon.com 505 0 $a Data mining and analytics -- Machine learning -- Databases and data warehouses -- Data visualization -- Keep Excel in your toolset -- Keep SQL in your toolset -- NoSQL data analytics -- Programming data mining and analytic solutions -- Data preprocessing and cleansing -- Data clustering -- Classification -- Predictive anlytics -- Data association -- Mining text and images -- Big data mining -- Planning and launching a data-mining and data-anaytics project. 541 1 $c Gift; $a Johannes Ledolter; $d 2023. $5 IaU 650 0 $a Data mining. 650 0 $a Quantitative research. 650 0 $a Machine learning. 650 0 $a R (Computer program language) 650 0 $a Python (Computer program language) 650 2 $a Data Mining 650 7 $a R (Computer program language) $2 fast $0 (OCoLC)fst01086207 650 7 $a Python (Computer program language) $2 fast $0 (OCoLC)fst01084736 650 7 $a Machine learning. $2 fast $0 (OCoLC)fst01004795 650 7 $a Data mining. $2 fast $0 (OCoLC)fst00887946 650 7 $a Quantitative research. $2 fast $0 (OCoLC)fst01742283 700 1 $a Ledolter, Johannes $e donor. $5 IaU 856 42 $u https://www.lib.uiowa.edu/giving/bookplates/?p=1455 $z Donor Bookplate 941 $a 1 952 $l OVUX522 $d 20231117022134.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=FC5C39A43D8C11EE8AE814B62EECA4DBInitiate Another SILO Locator Search