Python for Marketing Research and Analytics
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 reproduci
- PDF / 15,109,500 Bytes
- 273 Pages / 595.277 x 807.875 pts Page_size
- 114 Downloads / 439 Views
Python for Marketing Research and Analytics
Python for Marketing Research and Analytics
Jason S. Schwarz • Chris Chapman • Elea McDonnell Feit
Python for Marketing Research and Analytics
123
Jason S. Schwarz Google Nashville, TN, USA
Chris Chapman Google Seattle, WA, USA
Elea McDonnell Feit Drexel University Philadelphia, PA, USA
ISBN 978-3-030-49719-4 ISBN 978-3-030-49720-0 (eBook) https://doi.org/10.1007/978-3-030-49720-0 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
We are here to help you learn Python for marketing research and analytics. Python is a great choice for marketing analysts. It offers advanced capabilities for fitting statistical models. It is extensible and is able to process data from many different systems, in a variety of forms, for both small and large datasets. The Python ecosystem includes a vast range of established and emerging statistical methods as well as visualization techniques. Yet its use in marketing lags other fields such as econometrics, bioinformatics, and computer science. With your help, we hope to change that! This book is designed for two audiences: practicing marketing researchers and analysts who want to learn Python and students or researchers from other fields who want to review selected marketing topics in a Python context. What are the prerequisites? Simply that you are interested in Python for marketing, are conceptually familiar with basic statistical models such as linear regression, and are willing to engage in hands-on learning. This book will be particularly helpful to analysts who have some degree of programming experience and wish to learn Python. In Chap. 1, we describe additional
Data Loading...
