Data science for marketing analytics : a practical guide to forming a killer marketing strategy through data analysis with Python / Mirza Rahim Baig, Gururajan Govindan, and Vishwesh Ravi Shrimali.

Second edition.

Birmingham, UK : Packt Publishing, 2021.

1 online resource : illustrations (chiefly color)

Access is available to the Yale community.

Access restricted by licensing agreement.

Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language. Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learn: Load, clean, and explore sales and marketing data using pandas; Form and test hypotheses using real data sets and analytics tools; Visualize patterns in customer behavior using Matplotlib; Use advanced machine learning models like random forest and SVM; Use various unsupervised learning algorithms for customer segmentation; Use supervised learning techniques for sales prediction; Evaluate and compare different models to get the best outcomes; Optimize models with hyperparameter tuning and SMOTE. Who this book is for: This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.

O'Reilly Safari. OCLC KB.

Print version: Baig, Mirza Rahim. Data science for marketing analytics. Second edition. Birmingham : Packt Publishing, 2021

Books / Online

English

August 28, 2023

Table of Contents Data Preparation and Cleaning Data Exploration and Visualization Unsupervised Learning and Customer Segmentation Evaluating and Choosing the Best Segmentation Approach Predicting Customer Revenue Using Linear Regression More Tools and Techniques for Evaluating Regression Models Supervised Learning: Predicting Customer Churn Fine Tuning Classification Algorithms Multiclass Classification Algorithms.