The Data Science Design Manual

This book serves an introduction to data science, focusing on the skills and principles needed to build systems for collecting, analyzing, and interpreting data. As a discipline, data science sits at the intersection of statistics, computer science, and machine learning, but it is building a distinct heft and character of its own.

In particular, the book stresses the following basic principles as fundamental to becoming a good data scientist: âValuing Doing the Simple Things Rightâ, laying the groundwork of what really matters in analyzing data; âDeveloping Mathematical Intuitionâ, so that readers can understand on an intuitive level why these concepts were developed, how they are useful and when they work best, and; âThinking Like a Computer Scientist, but Acting Like a Statisticianâ, following approaches which come most naturally to computer scientists while maintaining the core values of statistical reasoning. The book does not emphasize any particular language or suite of data analysis tools, but instead provides a high-level discussion of important design principles.

This book covers enough material for an âIntroduction to Data Scienceâ course at the undergraduate or early graduate student levels. A full set of lecture slides for teaching this course are available at an associated website, along with data resources for projects and assignments, and online video lectures.

Other Pedagogical features of this book include: âWar Storiesâ offering perspectives on how data science techniques apply in the real world; âFalse Startsâ revealing the subtle reasons why certain approaches fail; âTake-Home Lessonsâ emphasizing the big-picture concepts to learn from each chapter; âHomework Problemsâ providing a wide range of exercises for self-study; âKaggle Challengesâ from the online platform Kaggle; examples taken from the data science television show âThe Quant Shopâ, and; concluding notes in each tutorial chapter pointing readers to primary sources and additional references.