Data Science for Marketing Analytics: A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
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Publication
Packt Publishing
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.
About the Author
Mirza Rahim Baig is an avid problem solver who uses deep learning and artificial intelligence to solve complex business problems. He has more than a decade of experience in creating value from data, harnessing the power of the latest in machine learning and AI with proficiency in using unstructured and structured data across areas like marketing, customer experience, catalog, supply chain, and other eCommerce sub-domains. Rahim is also a teacher - designing, creating, teaching data science for various learning platforms. He loves making the complex easy to understand. He is also the co-author of The Deep Learning Workshop, a hands-on guide to start your deep learning journey and build your own next-generation deep learning models.
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.
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. He has also written multiple blogs on OpenCV, deep learning, and computer vision. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar. He is also an author of Computer Vision Workshop, a book focusing on OpenCV and its applications in real-world scenarios; as well as, Machine Learning for OpenCV (2nd edition) - which introduces how to use OpenCV for machine learning applications.
- 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
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.
- 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
