LEADER 03937nam  22005895  450 
001 9910483326403321
005 20251113185159.0
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(MiAaPQ)EBC29134245
035   $a(EXLCZ)994100000011558765
100   $a20201103d2020      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aPython for Marketing Research and Analytics /$fby Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (XI, 272 p. 90 illus., 79 illus. in color.)
311 08$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   $aMathematical statistics$xData processing
606   $aStatistics
606   $aSocial sciences$xStatistical methods
606   $aStatistics and Computing
606   $aStatistics in Business, Management, Economics, Finance, Insurance
606   $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
615  0$aMathematical statistics$xData processing.
615  0$aStatistics.
615  0$aSocial sciences$xStatistical methods.
615 14$aStatistics and Computing.
615 24$aStatistics in Business, Management, Economics, Finance, Insurance.
615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
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$bMiAaPQ
906   $aBOOK
912   $a9910483326403321
996   $aPython for marketing research and analytics$92547738
997   $aUNINA