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.
Bayesian statistics and marketing. New York: Wiley. Rossini, A., Heiberger, R., Hornik, K., Maechler, M., Sparapani, R., Eglen, S., et al. (2013). ESS – Emacs speaks statistics (13th ed.). Noida: The ESS Developers. Rouder, J. N., Morey ...
What you will learnAnalyze and visualize data in Python using pandas and MatplotlibStudy clustering techniques, such as hierarchical and k-means clusteringCreate customer segments based on manipulated data Predict customer lifetime value ...
Houston, B., L. Bruzzese, and S. Weinberg 2002. The Investigative Reporter's Handbook: A Guide to Documents, Databases and Techniques (fourth ed.). Boston: Bedford/St. Martin's. Huber, J. and K. Zwerina 1996. The importance of utility ...
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and ...
Excel is that tool. Every example in this book features step-by-step instructions, a downloadable Excel file containing data and solutions, and plenty of screenshots. To sharpen your marketing analytics, you just need this guide and Excel.
You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python.
The starting point in learning marketing analytics is to understand the marketing problem. The second is asking the right business question. The data will help you tell the story.
Complete with downloadable data sets and test bank resources, this book supplies a concrete foundation to optimize marketing analytics for day-to-day business advantage.
add_lines ( x = c ( 6,4 ) , y = c ( 8,5 ) , name = " Shortest Line Between the Convex Clusters ( A ) " , line = list ... y = 1 , yend = 5 , line = list ( color = " gray " , dash = ' dash ' ) , showlegend = F ) % > % add_segments ( x = 1 ...
This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.