Author: Rafael A. Irizarry
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
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.
Introduction to Data Science
Author: Rafael A. Irizarry
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
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.
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
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.
R for Data Science
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
The DAM Book
Author: Peter Krogh
Publisher: "O'Reilly Media, Inc."
ISBN: 1449343716
Category : Photography
Languages : en
Pages : 496
Book Description
One of the main concerns for digital photographers today is asset management: how to file, find, protect, and re-use their photos. The best solutions can be found in The DAM Book, our bestselling guide to managing digital images efficiently and effectively. Anyone who shoots, scans, or stores digital photographs is practicing digital asset management (DAM), but few people do it in a way that makes sense. In this second edition, photographer Peter Krogh -- the leading expert on DAM -- provides new tools and techniques to help professionals, amateurs, and students: Understand the image file lifecycle: from shooting to editing, output, and permanent storage Learn new ways to use metadata and key words to track photo files Create a digital archive and name files clearly Determine a strategy for backing up and validating image data Learn a catalog workflow strategy, using Adobe Bridge, Camera Raw, Adobe Lightroom, Microsoft Expression Media, and Photoshop CS4 together Migrate images from one file format to another, from one storage medium to another, and from film to digital Learn how to copyright images To identify and protect your images in the marketplace, having a solid asset management system is essential. The DAM Book offers the best approach.
Publisher: "O'Reilly Media, Inc."
ISBN: 1449343716
Category : Photography
Languages : en
Pages : 496
Book Description
One of the main concerns for digital photographers today is asset management: how to file, find, protect, and re-use their photos. The best solutions can be found in The DAM Book, our bestselling guide to managing digital images efficiently and effectively. Anyone who shoots, scans, or stores digital photographs is practicing digital asset management (DAM), but few people do it in a way that makes sense. In this second edition, photographer Peter Krogh -- the leading expert on DAM -- provides new tools and techniques to help professionals, amateurs, and students: Understand the image file lifecycle: from shooting to editing, output, and permanent storage Learn new ways to use metadata and key words to track photo files Create a digital archive and name files clearly Determine a strategy for backing up and validating image data Learn a catalog workflow strategy, using Adobe Bridge, Camera Raw, Adobe Lightroom, Microsoft Expression Media, and Photoshop CS4 together Migrate images from one file format to another, from one storage medium to another, and from film to digital Learn how to copyright images To identify and protect your images in the marketplace, having a solid asset management system is essential. The DAM Book offers the best approach.
Directory of Federal Statistics for Local Areas
Author:
Publisher:
ISBN:
Category : Cities and towns
Languages : en
Pages : 380
Book Description
Publisher:
ISBN:
Category : Cities and towns
Languages : en
Pages : 380
Book Description
Directory of Statisticians of the United States Government
Author:
Publisher:
ISBN:
Category : Executive departments
Languages : en
Pages : 408
Book Description
Publisher:
ISBN:
Category : Executive departments
Languages : en
Pages : 408
Book Description
Directory of U.S. Government Datafiles for Mainframes and Microcomputers
Author:
Publisher:
ISBN:
Category : Data centers
Languages : en
Pages : 316
Book Description
Publisher:
ISBN:
Category : Data centers
Languages : en
Pages : 316
Book Description
Directory of Federal Statistics for Local Areas
Author: United States. Bureau of the Census
Publisher:
ISBN:
Category : Cities and towns
Languages : en
Pages : 376
Book Description
Publisher:
ISBN:
Category : Cities and towns
Languages : en
Pages : 376
Book Description
Federal Statistical Directory
Author:
Publisher:
ISBN:
Category : Statisticians
Languages : en
Pages : 976
Book Description
Publisher:
ISBN:
Category : Statisticians
Languages : en
Pages : 976
Book Description
Census Catalog and Guide
Author: United States. Bureau of the Census
Publisher:
ISBN:
Category : United States
Languages : en
Pages : 440
Book Description
Includes subject area sections that describe all pertinent census data products available, i.e. "Business--trade and services", "Geography", "Transportation," etc.
Publisher:
ISBN:
Category : United States
Languages : en
Pages : 440
Book Description
Includes subject area sections that describe all pertinent census data products available, i.e. "Business--trade and services", "Geography", "Transportation," etc.
Statistical Reporter
Author:
Publisher:
ISBN:
Category : United States
Languages : en
Pages : 504
Book Description
Publisher:
ISBN:
Category : United States
Languages : en
Pages : 504
Book Description