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Author: Abdolreza Abhari
Publisher: Createspace Independent Publishing Platform
ISBN: 9781727124842
Category :
Languages : en
Pages : 148
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Book Description
Data Science, sometimes known as methods of processing and analyzing massive data sets (Big Data), is a rapidly evolving field. This book teaches important topics of the emerging data science by providing simple and practical examples in R language. Initial chapters are about data collection and management at large scale, and then data analytics and applying statistical and machine learning models on the collected data are discussed in rest of the book. Ten important topics in data science are explained in ten chapters of this book with practical examples in Oracle SQL, R, Hadoop, and MapReduce. The fundamental of data management such as relational database systems, data mining and distributed computing with practical examples of SQL and implementing Hadoop and MapReduce are detailed in chapters 1 to 3. Regression and statistical analysis, neural networks, support vector machines and machine learning are explained in simple language together with R programming examples, in chapter 4 to 7. Natural language processing, recommendation systems and analyzing social networks graphs are explained in chapters 8 to 10 of this book. Dr. Abdolreza Abhari, a professor of computer science department at Ryerson University, has collected the material of this book after many years of teaching Data Science. With the background in computer science dating back to before the invention of the world wide web, professor Abhari has extensive experience in analyzing web and social network data and creating database systems for the companies and industrial sectors in Europe and North America. His teaching area in academia includes database systems, distributed systems, and data science for graduate and undergraduate students. Although this book is written for professionals and graduated students who have a university or college degree, it is also useful for whoever considers working in the data science industry.
Author: Abdolreza Abhari
Publisher: Createspace Independent Publishing Platform
ISBN: 9781727124842
Category :
Languages : en
Pages : 148
Get Book Here
Book Description
Data Science, sometimes known as methods of processing and analyzing massive data sets (Big Data), is a rapidly evolving field. This book teaches important topics of the emerging data science by providing simple and practical examples in R language. Initial chapters are about data collection and management at large scale, and then data analytics and applying statistical and machine learning models on the collected data are discussed in rest of the book. Ten important topics in data science are explained in ten chapters of this book with practical examples in Oracle SQL, R, Hadoop, and MapReduce. The fundamental of data management such as relational database systems, data mining and distributed computing with practical examples of SQL and implementing Hadoop and MapReduce are detailed in chapters 1 to 3. Regression and statistical analysis, neural networks, support vector machines and machine learning are explained in simple language together with R programming examples, in chapter 4 to 7. Natural language processing, recommendation systems and analyzing social networks graphs are explained in chapters 8 to 10 of this book. Dr. Abdolreza Abhari, a professor of computer science department at Ryerson University, has collected the material of this book after many years of teaching Data Science. With the background in computer science dating back to before the invention of the world wide web, professor Abhari has extensive experience in analyzing web and social network data and creating database systems for the companies and industrial sectors in Europe and North America. His teaching area in academia includes database systems, distributed systems, and data science for graduate and undergraduate students. Although this book is written for professionals and graduated students who have a university or college degree, it is also useful for whoever considers working in the data science industry.
Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538
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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author: Nina Zumel
Publisher: Manning Publications
ISBN: 9781617291562
Category : Computers
Languages : en
Pages : 416
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Book Description
Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations
Author: Joel Grus
Publisher: "O'Reilly Media, Inc."
ISBN: 1491904399
Category : Computers
Languages : en
Pages : 336
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Book Description
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 them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952911
Category : Computers
Languages : en
Pages : 322
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Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author: Benjamin S. Baumer
Publisher: CRC Press
ISBN: 0429575394
Category : Business & Economics
Languages : en
Pages : 830
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Book Description
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Author: Laura Igual
Publisher: Springer
ISBN: 3319500171
Category : Computers
Languages : en
Pages : 227
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Book Description
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 as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
Author: David Stuart
Publisher: Facet Publishing
ISBN: 1783303441
Category : Language Arts & Disciplines
Languages : en
Pages : 200
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Book Description
Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.
Author: Ryan A. Estrellado
Publisher: Routledge
ISBN: 1000200906
Category : Education
Languages : en
Pages : 331
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Book Description
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
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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
