Master the art of data science with this comprehensive textbook on practical data analytics. "Algorithms for Data Science" by Brian Steele, John Chandler, and Swarna Reddy offers clear explanations of fundamental principles and algorithms, making them transparent through intuitive mathematical and statistical foundations. Immerse yourself in Python and R with real-world data analysis to gain the ability to adapt algorithms to new problems and conduct innovative analyses. This book is structured into three parts: Data Reduction, Extracting Information from Data, and Predictive Analytics. It's ideal for upper-division undergraduate and graduate students in mathematics, statistics, and computer science, and is also an exceptional resource for practitioners seeking to utilize large datasets. Key Features: - Focuses on algorithms as the keystone of data analytics - Provides clear, intuitive explanations of mathematical and statistical foundations - Immerses readers in Python and R with real data analysis - Covers Data Reduction, Information Extraction, and Predictive Analytics - Includes practical aspects of distributed computing with Hadoop and MapReduce - Features a chapter on Healthcare Analytics for an extended example - Explores foundational algorithms like k-nearest neighbors and naive Bayes - Discusses forecasting and streaming data analysis using Twitter API and NASDAQ data Product Summary: Algorithms for Data Science, Hardcover, English, First edition, 430 pages, written by Brian Steele, John Chandler, Swarna Reddy, published by Springer Cham, ISBN 9783319457956.