001463107 000__ 04914cam\a22006257i\4500
001463107 001__ 1463107
001463107 003__ OCoLC
001463107 005__ 20230601003306.0
001463107 006__ m\\\\\o\\d\\\\\\\\
001463107 007__ cr\cn\nnnunnun
001463107 008__ 230411s2023\\\\nyua\\\\ob\\\\001\0\eng\d
001463107 019__ $$a1375289350$$a1375292262$$a1378155063
001463107 020__ $$a9781484287545$$q(electronic bk.)
001463107 020__ $$a1484287541$$q(electronic bk.)
001463107 020__ $$z9781484287538
001463107 020__ $$z1484287533
001463107 0247_ $$a10.1007/978-1-4842-8754-5$$2doi
001463107 035__ $$aSP(OCoLC)1375475805
001463107 040__ $$aORMDA$$beng$$erda$$epn$$cORMDA$$dGW5XE$$dEBLCP$$dYDX$$dN$T
001463107 049__ $$aISEA
001463107 050_4 $$aHD30.23
001463107 08204 $$a658.4/03$$223/eng/20230411
001463107 1001_ $$aHodeghatta, Umesh R.,$$eauthor.
001463107 24510 $$aPractical business analytics using R and Python :$$bsolve business problems using a data-driven approach /$$cUmesh R. Hodeghatta and Umesha Nayak.
001463107 250__ $$aSecond edition.
001463107 264_1 $$aNew York, NY :$$bApress,$$c[2023]
001463107 300__ $$a1 online resource (716 pages) :$$billustrations
001463107 336__ $$atext$$btxt$$2rdacontent
001463107 337__ $$acomputer$$bc$$2rdamedia
001463107 338__ $$aonline resource$$bcr$$2rdacarrier
001463107 504__ $$aIncludes bibliographical references and index.
001463107 5050_ $$aSection 1: Introduction to Analytics -- Chapter 1: Business Analytics Revolution -- Chapter 2: Foundations of Business Analytics -- Chapter 3: Structured Query Language (SQL) Analytics -- Chapter 4: Business Analytics Process -- Chapter 5: Exploratory Data Analysis (EDA) -- Chapter 6: Evaluating Analytics Model Performance -- Section II: Supervised Learning and Predictive Analytics -- Chapter 7: Simple Linear Regressions -- Chapter 8: Multiple Linear Regressions -- Chapter 9: Classification -- Chapter 10: Neural Networks -- Chapter 11: Logistic Regression -- Section III: Time Series Models -- Chapter 12: Time Series Forecasting -- Section IV: Unsupervised Model and Text Mining -- Chapter 13: Cluster Analysis -- Chapter 14: Relationship Data Mining -- Chapter 15: Mining Text and Text Analytics -- Chapter 16: Big Data and Big Data Analytics -- Section V: Business Analytics Tools -- Chapter 17: R programming for Analytics -- Chapter 18: Python Programming for Analytics.
001463107 506__ $$aAccess limited to authorized users.
001463107 520__ $$aThis book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing. Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.
001463107 650_0 $$aDecision making$$xData processing.
001463107 650_0 $$aBusiness planning$$xData processing.
001463107 650_0 $$aR (Computer program language)
001463107 650_0 $$aPython (Computer program language)
001463107 655_0 $$aElectronic books.
001463107 7001_ $$aNayak, Umesha,$$eauthor.
001463107 77608 $$iPrint version:$$aHodeghatta, Umesh R.$$tPractical Business Analytics Using R and Python$$dBerkeley, CA : Apress L. P.,c2023$$z9781484287538
001463107 852__ $$bebk
001463107 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8754-5$$zOnline Access$$91397441.1
001463107 909CO $$ooai:library.usi.edu:1463107$$pGLOBAL_SET
001463107 980__ $$aBIB
001463107 980__ $$aEBOOK
001463107 982__ $$aEbook
001463107 983__ $$aOnline
001463107 994__ $$a92$$bISE