Acknowledgments -- Introduction -- Chapter 1: Exploratory data analysis -- Chapter 2: Forecasting -- Chapter 3: Group comparisons -- Chapter 4: A/B testing -- Chapter 5: Binary classification -- Chapter 6: Supervised learning -- Chapter 7: Unsupervised learning -- Chapter 8: Web scraping -- Chapter 9: Recommendation systems -- Chapter 10: Natural language processing -- Chapter 11: Data science in other languages -- Index.
Summary:
"Learn how to apply the principles of data science to improve business strategies. Chapters cover concepts such as A/B testing, supervised and unsupervised machine learning, web scraping, and more. Each concept is illustrated using real-world business applications, real-world data, and useful Python code examples"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.