Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
Seiten
2021
|
2nd Revised edition
Packt Publishing Limited (Verlag)
978-1-80056-047-5 (ISBN)
Packt Publishing Limited (Verlag)
978-1-80056-047-5 (ISBN)
This book on marketing analytics with Python will quickly get you up and running using practical data science and machine learning to improve your approach to marketing. You'll learn how to analyze sales, understand customer data, predict outcomes, and present conclusions with clear visualizations.
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.
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.
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.
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
| Erscheinungsdatum | 10.09.2021 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| Wirtschaft ► Betriebswirtschaft / Management ► Marketing / Vertrieb | |
| ISBN-10 | 1-80056-047-8 / 1800560478 |
| ISBN-13 | 978-1-80056-047-5 / 9781800560475 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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