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Product Description
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: “Valuing Doing the Simple Things Right”, laying the groundwork of what really matters in analyzing data; “Developing Mathematical Intuition”, so that readers can understand on an intuitive level why these concepts were developed, how they are useful and when they work best, and; “Thinking Like a Computer Scientist, but Acting Like a Statistician”, 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 “Introduction to Data Science” 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: “War Stories” offering perspectives on how data science techniques apply in the real world; “False Starts” revealing the subtle reasons why certain approaches fail; “Take-Home Lessons” emphasizing the big-picture concepts to learn from each chapter; “Homework Problems” providing a wide range of exercises for self-study; “Kaggle Challenges” from the online platform Kaggle; examples taken from the data science television show “The Quant Shop”, and; concluding notes in each tutorial chapter pointing readers to primary sources and aThis 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 world
- Includes "Homework Problems," providing a wide range of exercises and projects for self-study
- Provides a complete set of lecture slides and online video lectures at www.data-manual.com
- Provides "Take-Home Lessons," emphasizing the big-picture concepts to learn from each chapter
- Recommends exciting "Kaggle Challenges" from the online platform Kaggle
- Highlights "False Starts," revealing the subtle reasons why certain approaches fail
- Offers examples taken from the data science television show "The Quant Shop" (www.quant-shop.com)
Dr. Steven S. Skiena is Distinguished Teaching Professor of Computer Science at Stony Brook University, with research interests in data science, natural language processing, and algorithms. He was awarded the IEEE Computer Science and Engineering Undergraduate Teaching Award "for outstanding contributions to undergraduate education ...and for influential textbooks and software." Dr. Skiena is the author of six books, including the popular Springer titles The Algorithm Design Manual and Programming Challenges: The Programming Contest Training Manual.
"The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki." (P. Navrat, Computing Reviews, February, 23, 2018)
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