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Textbook: Data Science for Marketing Analytics: A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
Author(s): Mirza Rahim Baig
Description:
This course provides 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.
Chapter 1, Data Preparation and Cleaning, teaches you skills related to data cleaning along with various data preprocessing techniques using real-world examples.
Chapter 2, Data Exploration and Visualization, teaches you how to explore and analyze data with the help of various aggregation techniques and visualizations using Matplotlib and Seaborn.
Chapter 3, Unsupervised Learning and Customer Segmentation, teaches you customer segmentation, one of the most important skills for a data science professional
in marketing. You will learn how to use machine learning to perform customer
segmentation with the help of scikit-learn. You will also learn to evaluate segments from a business perspective.
Chapter 4, Evaluating and Choosing the Best Segmentation Approach, expands your repertoire to various advanced clustering techniques and teaches principled
numerical methods of evaluating clustering performance.
Chapter 5, Predicting Customer Revenue using Linear Regression, gets you started on
predictive modeling of quantities by introducing you to regression and teaching
simple linear regression in a hands-on manner using scikit-learn.
Chapter 6, More Tools and Techniques for Evaluating Regression Models, goes into more details of regression techniques, along with different regularization methods available to prevent overfitting. You will also discover the various evaluation metrics available
to identify model performance.
Chapter 7, Supervised Learning: Predicting Customer Churn, uses a churn prediction problem as the central problem statement throughout the chapter to cover different classification algorithms and their implementation using scikit-learn.
Chapter 8, Fine-Tuning Classification Algorithms, introduces support vector machines
and tree-based classifiers along with the evaluation metrics for classification
algorithms. You will also learn about the process of hyperparameter tuning which will help you obtain better results using these algorithms.
Chapter 9, Multiclass Classification Algorithms, introduces a multiclass classification problem statement and the classifiers that can be used to solve such problems.
You will learn about imbalanced datasets and their treatment in detail. You will
also discover the micro- and macro-evaluation metrics available in scikit-learn for these classifiers.
Data Science for Marketing Analytics_ A practical guide to forming a killer mark.pdf
(26.6 MB, 需要: RMB 19 元)
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