Doprava zdarma pre objednávky nad 59,99 € s Packetou a SPS Boxmi.

Zistiť stav objednávky

Staňte sa súčasťou komunity milovníkov kníh z celého sveta a získajte hromadu výhod. Založiť účet zdarma

Doprava zadarmo s Packetou nad 59.99 €

Kuriér DPD 2.99 € Zberné miesto GLS 2.99 € SPS 3.99 € Kuriér GLS 3.49 € SPS Parcel Shop 2.99 € Packeta kurýr 3.99 € Pošta 3.99 € Zberné miesto DPD 2.99 € Zberné miesto DPD 0.00 € Packeta 2.99 €

Kontakt

Ako nakupovať

Pomoc

Môj účet

▸ Prázdny :-(

Doprava zdarma pre objednávky nad 59,99 € s Packetou a SPS Boxmi.

Python for Marketing Research and Analytics

Name: Python for Marketing Research and Analytics
Brand: Springer Nature Switzerland AG
SKU: 37363291
Price: 60.66 EUR
Availability: InStock
Author: Elea McDonnell Feit
ISBN: 9783030497224

Elea McDonnell Feit

Chris Chapman

Jazyk

Angličtina

Kniha Brožovaná

Libristo kód: 37363291

Nakladateľstvo Springer Nature Switzerland AG, november 2021

This book provides an introduction to quantitative marketing with Python. The book presents a hands-... Celý popis

Libristo kód: 37363291

147 b

60.66 €

Skladom u dodávateľa Odosielame za 5-8 dní

30 dní na vrátenie tovaru

Zákazníci tiež kúpili

Hitmakeři Derek Thompson

Brožovaná

11.54 €

Herodot Robert Rollinger

Brožovaná

19.33 €

Grundlagen Fur Eine Allgemeine Theorie Der Functionen Einer Veranderlichen Complexen Grosse (1867) Bernhard Riemann

Brožovaná

21.97 €

Orchestralklang im Ring des Nibelungen Susanne Becker

Brožovaná

16.20 €

Mercatique Terminale STMG - Toutes les matières - N°4 Réflexe - 2017 Nancy Baranes

Brožovaná

19.44 €

Israele e Palestina. Un conflitto senza fine? Giovanni Sale

Brožovaná

21.66 €

This 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.