001441640 000__ 03335cam\a2200529\i\4500
001441640 001__ 1441640
001441640 003__ OCoLC
001441640 005__ 20230309003337.0
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001441640 008__ 220108t20212021sz\a\\\\ob\\\\001\0\eng\d
001441640 019__ $$a1290841592$$a1291146658$$a1291172179$$a1294366564
001441640 020__ $$a3030870235$$qelectronic book
001441640 020__ $$a9783030870232$$q(electronic bk.)
001441640 020__ $$z9783030870225
001441640 020__ $$z3030870227
001441640 0247_ $$a10.1007/978-3-030-87023-2$$2doi
001441640 035__ $$aSP(OCoLC)1291318112
001441640 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dYDXIT$$dGW5XE$$dOCLCO$$dDCT$$dOCLCF$$dN$T$$dOCLCO$$dXII$$dOCLCQ
001441640 049__ $$aISEA
001441640 050_4 $$aHD30.23$$b.P33 2021
001441640 08204 $$a658.4033$$223
001441640 1001_ $$aPaczkowski, Walter R.,$$eauthor.
001441640 24510 $$aBusiness analytics :$$bdata science for business problems /$$cWalter R. Paczkowski.
001441640 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001441640 264_4 $$c©2021
001441640 300__ $$a1 online resource (416 pages) :$$billustrations (chiefly color)
001441640 336__ $$atext$$btxt$$2rdacontent
001441640 337__ $$acomputer$$bc$$2rdamedia
001441640 338__ $$aonline resource$$bcr$$2rdacarrier
001441640 347__ $$atext file$$bPDF$$2rda
001441640 500__ $$aDescription based upon print version of record.
001441640 504__ $$aIncludes bibliographical references and index.
001441640 5050_ $$a1. Types of Business Problems -- 2. Data for Business Problems -- 3. Beginning Data Handling -- 4. Data Preprocessing -- 5. Data Visualization: The Basics -- 6. OLS Regression Basics -- 7. Time Series Basics -- 8. Statistical Tables -- 9. Advanced Data Handling -- 10. Advanced OLS -- 11. Logistic Regression -- 12. Classification.
001441640 506__ $$aAccess limited to authorized users.
001441640 520__ $$aThis book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.
001441640 588__ $$aDescription based on online resource; title from digital title page (viewed on January 25, 2022).
001441640 650_0 $$aDecision making$$xMathematical models.
001441640 650_6 $$aPrise de décision$$xModèles mathématiques.
001441640 655_0 $$aElectronic books.
001441640 77608 $$iPrint version:$$aPaczkowski, Walter R.$$tBusiness Analytics$$dCham : Springer International Publishing AG,c2022$$z9783030870225
001441640 852__ $$bebk
001441640 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-87023-2$$zOnline Access$$91397441.1
001441640 909CO $$ooai:library.usi.edu:1441640$$pGLOBAL_SET
001441640 980__ $$aBIB
001441640 980__ $$aEBOOK
001441640 982__ $$aEbook
001441640 983__ $$aOnline
001441640 994__ $$a92$$bISE