Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python
2nd Edition
Published on 3. June 2024
636 pages
978-1-80056-388-9 (ISBN)
Description
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming languageKey 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.
- 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.
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Additional editions
Mirza Rahim Baig | Gururajan Govindan | Vishwesh Ravi Shrimali
Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
Book
09/2021
2nd Edition
Packt Publishing
€48.70
Shipment within 3-4 weeks
Previous edition
Tommy Blanchard | Debasish Behera | Pranshu Bhatnagar
Data Science for Marketing Analytics
Achieve your marketing goals with the data analytics power of Python
E-Book
09/2024
Packt Publishing
from
€46.39
Available for download
Persons
Baig Mirza Rahim :
Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.Govindan Gururajan :
Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.Ravi Shrimali Vishwesh :
Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.
Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.Govindan Gururajan :
Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.Ravi Shrimali Vishwesh :
Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.
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 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
