{"product_id":"9781119674689","title":"Data Science in Theory and Practice Techniques for Big Data Analytics and Complex Data Sets","description":"\u003ch1\u003eData Science in Theory and Practice\u003c\/h1\u003e\u003ch2\u003eTechniques for Big Data Analytics and Complex Data Sets\u003c\/h2\u003e\u003ch3\u003eMaria Cristina Mariani | Osei Kofi Tweneboah | Maria Pia Beccar-Varela\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cb\u003eDATA SCIENCE IN THEORY AND PRACTICE\u003c\/b\u003e \u003cp\u003e\u003cb\u003eEXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eData Science in Theory and Practice\u003c\/i\u003e delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. \u003c\/p\u003e\n\u003cp\u003eThe book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. \u003c\/p\u003e\n\u003cp\u003eIdeal for students and practitioners without a strong background in data science, readers will also learn from topics like: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAnalyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis\u003c\/li\u003e \u003cli\u003eA comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity\u003c\/li\u003e \u003cli\u003eIntroductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages\u003c\/li\u003e \u003cli\u003eAn exploration of algorithms, including how to write one and how to perform an asymptotic analysis\u003c\/li\u003e \u003cli\u003eA comprehensive discussion of several techniques for analyzing and predicting complex data sets\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, \u003ci\u003eData Science in Theory and Practice\u003c\/i\u003e will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003eMARIA CRISTINA MARIANI, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Shigeko K. Chan Distinguished Professor and Chair in the Department of Mathematical Sciences at The University of Texas at El Paso. She currently focuses her research on Stochastic Analysis, Differential Equations and Machine Learning with applications to Big Data and Complex Data sets arising in Public Health, Geophysics, Finance and others. Dr. Mariani is co-author of other Wiley books including \u003ci\u003eQuantitative Finance.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eOSEI KOFI TWENEBOAH, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Assistant Professor of Data Science at Ramapo College of New Jersey. His main research is Stochastic Analysis, Machine Learning and Scientific Computing with applications to Finance, Health Sciences, and Geophysics.  \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eMARIA PIA BECCAR-VARELA, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Associate Professor of Instruction in the Department of Mathematical Sciences at the University of Texas at El Paso. Her research interests include Differential Equations, Stochastic Differential Equations, Wavelet Analysis and Discriminant Analysis applied to Finance, Health Sciences, and Earthquake Studies​.  \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e12 October 2021\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119674689\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e400\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e16.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44378590380172,"sku":"9781119674689","price":129.56,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119674689_adf7d6b6-cabb-407b-bf1f-402314fdd716.jpg?v=1780240886","url":"https:\/\/lateknightbooks.com\/products\/9781119674689","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}