PacktPublishing / Hands On Data Science For Marketing
Labels
Projects that are alternatives of or similar to Hands On Data Science For Marketing
Hands-On Data Science for Marketing
This is the code repository for Hands-On Data Science for Marketing, published by Packt.
Improve your marketing strategies with machine learning using Python and R
What is this book about?
Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies.
This book covers the following exciting features:
- Learn how to compute and visualize marketing KPIs in Python and R
- Master what drives successful marketing campaigns with data science
- Use machine learning to predict customer engagement and lifetime value
- Make product recommendations that customers are most likely to buy
- Learn how to use A/B testing for better marketing decision making
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
# total number of conversions
df.conversion.sum()
# total number of clients in the data (= number of rows in the data)
df.shape[0]
Following is what you need for this book: If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. 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.
With the following software and hardware list you can run all code files present in the book.
Software and Hardware List
| Chapter | Software required | OS required |
|---|---|---|
| 01 - 12 | Python >= 3.6 -or- R >= 3.5.1 | Windows, macOS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Related products
-
Data Science Algorithms in a Week - Second Edition [Packt] [Amazon]
-
Python Data Science Essentials - Third Edition [Packt] [Amazon]
Get to Know the Author
Yoon Hyup Hwang is a seasoned data scientist in the marketing and financial sectors with expertise in predictive modeling, machine learning, statistical analysis, and data engineering. He has 8+ years' experience of building numerous machine learning models and data products using Python and R. He holds an MSE in computer and information technology from the University of Pennsylvania and a BA in economics from the University of Chicago. In his spare time, he enjoys practicing various martial arts, snowboarding, and roasting coffee. Born and raised in Busan, South Korea, he currently works in New York and lives in New Jersey with his artist wife, Sunyoung, and a playful dog, Dali (named after Salvador Dali).
Suggestions and Feedback
Click here if you have any feedback or suggestions.