Introduction To Data Science

Introduction to Data Science
Publisher CRC Press
Release Date
Category Mathematics
Total Pages 713
ISBN 9781000708035
Rating 4/5 from 21 reviews
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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
  • Release Date : 2019-11-20

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,

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A Hands On Introduction to Data Science
  • Author : Chirag Shah
  • Publisher : Cambridge University Press
  • Release Date : 2020-04-02

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

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R for Data Science
  • Author : Hadley Wickham,Garrett Grolemund
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2016-12-12

"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"--

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Introduction to Data Science
  • Author : Laura Igual,Santi Seguí
  • Publisher : Springer
  • Release Date : 2017-02-22

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks

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An Introduction to Data Science
  • Author : Jeffrey S. Saltz,Jeffrey M. Stanton
  • Publisher : SAGE Publications
  • Release Date : 2017-08-25

An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using

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Introducing Data Science
  • Author : Davy Cielen,Arno Meysman
  • Publisher : Simon and Schuster
  • Release Date : 2016-05-02

Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a

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Introduction to Data Science and Machine Learning
  • Author : Keshav Sud,Pakize Erdogmus,Seifedine Kadry
  • Publisher : BoD – Books on Demand
  • Release Date : 2020-03-25

Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four

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Data Science
  • Author : John D. Kelleher,Brendan Tierney
  • Publisher : MIT Press
  • Release Date : 2018-04-13

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the

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Doing Data Science
  • Author : Cathy O'Neil,Rachel Schutt
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2013-10-09

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia

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Introduction to Statistical and Machine Learning Methods for Data Science
  • Author : Carlos Andre Reis Pinheiro,Mike Patetta
  • Publisher : SAS Institute
  • Release Date : 2021-08-06

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation

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Data Science from Scratch
  • Author : Joel Grus
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2015-04-14

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing

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Getting Started with Data Science
  • Author : Murtaza Haider
  • Publisher : IBM Press
  • Release Date : 2015-12-14

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’

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A General Introduction to Data Analytics
  • Author : João Moreira,Andre Carvalho,Tomás Horvath
  • Publisher : John Wiley & Sons
  • Release Date : 2018-07-18

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or

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Introduction to Data Science for Social and Policy Research
  • Author : Jose Manuel Magallanes Reyes
  • Publisher : Cambridge University Press
  • Release Date : 2017-09-21

This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.

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Data Science and Data Analytics
  • Author : Amit Kumar Tyagi
  • Publisher : CRC Press
  • Release Date : 2021-09-22

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms,

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