LEADER 03435oam 2200517 450 001 996418198303316 005 20220630164749.0 010 $a3-030-49720-8 024 7 $a10.1007/978-3-030-49720-0 035 $a(CKB)4100000011558765 035 $a(DE-He213)978-3-030-49720-0 035 $a(MiAaPQ)EBC6383549 035 $a(PPN)252509544 035 $a(EXLCZ)994100000011558765 100 $a20210416d2020 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPython for marketing research and analytics /$fJason S. Schwarz, Chris Chapman and Elea McDonnell Feit 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$d©2020 215 $a1 online resource (XI, 272 p. 90 illus., 79 illus. in color.) 311 $a3-030-49719-4 320 $aIncludes bibliographical references and index. 327 $aPart I: Basics of Python -- Chapter 1: Welcome to Python -- Chapter 2: The Python Language -- Part II Fundamentals of Data Analysis -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Additional Linear Modeling Topics -- Part III Advanced data analysis -- Chapter 9: Reducing Data Complexity -- Chapter 10: Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations -- Chapter 11: Classification: Assigning observations to known categories -- Chapter 12: Conclusion -- Index. 330 $aThis book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics. . 606 $aPython (Computer program language) 606 $aMarketing research$vComputer programs 606 $aR (Computer program language) 615 0$aPython (Computer program language) 615 0$aMarketing research 615 0$aR (Computer program language). 676 $a519.5 700 $aSchwarz$b Jason S.$01065735 702 $aFeit$b Elea McDonnell 702 $aChapman$b Chris 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a996418198303316 996 $aPython for marketing research and analytics$92547738 997 $aUNISA LEADER 02043nam 2200445 n 450 001 996387912703316 005 20221108040057.0 035 $a(CKB)1000000000624836 035 $a(EEBO)2240917296 035 $a(UnM)99867381 035 $a(EXLCZ)991000000000624836 100 $a19940511d1660 uh | 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aAnno regni Caroli II. regis Angliæ, Scotiæ, Franciæ, & Hiberniæ, duodecimno$b[electronic resource] $eAt the parliament begun at Westminster, the 25th day of April, Anno Domini, 1660. 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