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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.
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.
Jason Schwarz PhD is a Quantitative Researcher at Google and a former systems neurobiologist. His areas of research include perception, attention, motivation, behavioral pattern formation, and data visualization which he studies at scale at Google. Prior to joining Google, he was a data scientist at a startup where he ran analytics and developed and deployed production machine learning models on a Python stack.
Chris Chapman PhD is a Quantitative Researcher at Google, and an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015). In the broader industry, he has served as President of the American Marketing Association's Practitioner Council, chaired the AMA Advanced Research Techniques Forum in 2012 and 2017, and is a member of several conference and industry committees. Chris regularly presents research innovations and teaches workshops on R, conjoint analysis, strategic modeling, and other analytics topics.
EleaMcDonnell Feit is an Assistant Professor of Marketing at Drexel University and a Senior Fellow of Marketing at The Wharton School. She enjoys making quantitative methods accessible to a broad audience and teaches workshops and courses on advertising measurement, marketing experiments, marketing analytics in R, discrete choice modeling and hierarchical Bayes methods. She is an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015).
Introduces Python specifically for advanced quantitative marketing and analytics
Presents the concept of shareable reproducible research enabled by notebooks
Applies Python to the building of statistical models using open source libraries such as sklearn and statsmodels
| Erscheinungsjahr: | 2020 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xi
272 S. 11 s/w Illustr. 79 farbige Illustr. 272 p. 90 illus. 79 illus. in color. |
| ISBN-13: | 9783030497194 |
| ISBN-10: | 3030497194 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: |
Schwarz, Jason S.
Feit, Elea McDonnell Chapman, Chris |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG |
| Maße: | 285 x 215 x 21 mm |
| Von/Mit: | Jason S. Schwarz (u. a.) |
| Erscheinungsdatum: | 03.11.2020 |
| Gewicht: | 0,952 kg |
Jason Schwarz PhD is a Quantitative Researcher at Google and a former systems neurobiologist. His areas of research include perception, attention, motivation, behavioral pattern formation, and data visualization which he studies at scale at Google. Prior to joining Google, he was a data scientist at a startup where he ran analytics and developed and deployed production machine learning models on a Python stack.
Chris Chapman PhD is a Quantitative Researcher at Google, and an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015). In the broader industry, he has served as President of the American Marketing Association's Practitioner Council, chaired the AMA Advanced Research Techniques Forum in 2012 and 2017, and is a member of several conference and industry committees. Chris regularly presents research innovations and teaches workshops on R, conjoint analysis, strategic modeling, and other analytics topics.
EleaMcDonnell Feit is an Assistant Professor of Marketing at Drexel University and a Senior Fellow of Marketing at The Wharton School. She enjoys making quantitative methods accessible to a broad audience and teaches workshops and courses on advertising measurement, marketing experiments, marketing analytics in R, discrete choice modeling and hierarchical Bayes methods. She is an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015).
Introduces Python specifically for advanced quantitative marketing and analytics
Presents the concept of shareable reproducible research enabled by notebooks
Applies Python to the building of statistical models using open source libraries such as sklearn and statsmodels
| Erscheinungsjahr: | 2020 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xi
272 S. 11 s/w Illustr. 79 farbige Illustr. 272 p. 90 illus. 79 illus. in color. |
| ISBN-13: | 9783030497194 |
| ISBN-10: | 3030497194 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: |
Schwarz, Jason S.
Feit, Elea McDonnell Chapman, Chris |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG |
| Maße: | 285 x 215 x 21 mm |
| Von/Mit: | Jason S. Schwarz (u. a.) |
| Erscheinungsdatum: | 03.11.2020 |
| Gewicht: | 0,952 kg |
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