Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series)
$83.38
Description
Book Synopsis: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture.
The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
A complete solutions manual is available to registered instructors who require the text for a course.
Details
Unlock the power of data analysis and prediction algorithms with "Introduction to Data Science: Data Analysis and Prediction Algorithms with R". This comprehensive book is your one-stop guide to tackling real-world data analysis challenges, equipping you with the concepts and skills needed to excel in the field of data science.
What sets this book apart is its practical approach, utilizing motivating case studies that mimic the experiences of a data scientist. Through these case studies, you'll learn how to ask specific questions and answer them through data analysis using R programming. Whether it's analyzing US murder rates by state, building a baseball team, or developing a movie recommendation system, you'll gain valuable insights into the world of data science.
No previous knowledge of R is required to embark on this data science journey. The book is divided into six parts, covering everything from probability and statistical inference to machine learning and productivity tools. Each chapter is designed to be presented as one lecture, making this book an ideal resource for a first course in data science.
But it doesn't stop there. "Introduction to Data Science" goes beyond the basics and helps you develop essential skills such as data wrangling, data visualization, predictive algorithm building, and more. You'll also learn important tools like UNIX/Linux shell for file organization and Git/GitHub for version control, ensuring your data science projects are efficient and reproducible.
Don't miss out on this invaluable resource for aspiring data scientists. Grab your copy of "Introduction to Data Science: Data Analysis and Prediction Algorithms with R" and start your journey towards becoming an expert in the exciting field of data science.
Ready to dive into the world of data science? Get your copy now!
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