Introduction to Data Science
Data Analysis and Prediction Algorithms with R
Book Details
| Author | Rafael A. Irizarry |
| Publisher | CRC Press |
| Published | 2019 |
| Edition | 1st |
| Paperback | 722 pages |
| Language | English |
| ISBN-13 | 9780367357986 |
| ISBN-10 | 0367357984 |
| License | Creative Commons Attribution-NonCommercial-ShareAlike |
Book Description
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.
This book is available under a Creative Commons Attribution-NonCommercial-ShareAlike license (CC BY-NC-SA), which means that you are free to copy, distribute, and modify it, as long as you credit the original author, don't use it for commercial purposes, and share any adaptations under the same license.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
Download and Read Links
Share this Book
[localhost]# find . -name "*Similar_Books*"
Algorithms
Algorithms are the lifeblood of computer science. They are the machines that proofs build and the music that programs play. Their history is as old as mathematics itself. This book is a wide-ranging, idiosyncratic treatise on the design and analysis of algorithms, covering several fundamental techniques, with an emphasis on intuition and the proble
Introduction to Data Science Using Python
Data science is the process of representing models that fit data. Its goal is to predict future output based on past observations of inputs. In data science, one collects information and interprets it to make decisions. This open book contains two parts, the first is designed to be used in an introductory programming course for students looking to
Introduction to Computer Science
Introduction to Computer Science provides a comprehensive foundation in core computer science concepts and principles, aligning with the scope and sequence of most introductory computer science courses. The offering serves as an engaging entry point for students pursuing diverse fields of study and employment, including computer science, business,
Data Structures and Algorithms
This book provides implementations of common and uncommon algorithms in pseudocode which is language independent and provides for easy porting to most imperative programming languages. It is not a definitive book on the theory of data structures and algorithms. For the most part this book presents implementations devised by the authors themselves b
Open Data Structures
Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Mor
Principles of Data Science
Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important. The authors have include