
Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
Packt Publishing
2nd Edition
Published on 7. September 2021
Book
Paperback/Softback
636 pages
978-1-80056-047-5 (ISBN)
Description
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language
Key Features
Use data analytics and machine learning in a sales and marketing context
Gain insights from data to make better business decisions
Build your experience and confidence with realistic hands-on practice
Book DescriptionUnleash 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 forThis 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.
Key Features
Use data analytics and machine learning in a sales and marketing context
Gain insights from data to make better business decisions
Build your experience and confidence with realistic hands-on practice
Book DescriptionUnleash 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 forThis 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.
More details
Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 34 mm
Weight
1169 gr
ISBN-13
978-1-80056-047-5 (9781800560475)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Mirza Rahim Baig Baig | Gururajan Govindan Govindan | Vishwesh Ravi Shrimali Shrimali
Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python
E-Book
06/2024
2nd Edition
Packt Publishing Limited
from
€27.59
Available for download
Persons
Mirza Rahim Baig is an avid problem solver who uses deep learning and artificial intelligence to solve complex business problems. Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering. He has a keen interest in programming and AI and has applied that interest in mechanical engineering projects.
Content
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
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