This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an ¿Introduction to Data Science¿ course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinctheft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.Additional learning tools:Contains ¿War Stories,¿ offering perspectives on how data science applies in the real worldIncludes ¿Homework Problems,¿ providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at data-manual.comProvides ¿Take-Home Lessons,¿ emphasizing the big-picture concepts to learn from each chapterRecommends exciting ¿Kaggle Challenges¿ from the online platform KaggleHighlights ¿False Starts,¿ revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show ¿The Quant Shop¿ (quant-shop.com)

Provides an introduction to data science, focusing on the fundamental skills and principles needed to build systems for collecting, analyzing, and interpreting data

What is Data Science?.- Mathematical Preliminaries.- Data Munging.- Scores and Rankings.- Statistical Analysis.- Visualizing Data.- Mathematical Models.- Linear Algebra.- Linear and Logistic Regression.- Distance and Network Methods.- Machine Learning.- Big Data: Achieving Scale.