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R for Data Science

R for Data Science
Import, Tidy, Transform, Visualize, and Model Data

by Hadley Wickham,Garrett Grolemund

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2016-12-12
  • Pages : 492
  • ISBN : 1491910364
  • Language : En, Es, Fr & De
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"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"--

Introduction to Data Science

Introduction to Data Science
Data Analysis and Prediction Algorithms with R

by Rafael A. Irizarry

  • Publisher : CRC Press
  • Release : 2019-11-20
  • Pages : 713
  • ISBN : 1000708039
  • Language : En, Es, Fr & De
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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.

Python Data Science Handbook

Python Data Science Handbook
Essential Tools for Working with Data

by Jake VanderPlas

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2016-11-21
  • Pages : 548
  • ISBN : 1491912138
  • Language : En, Es, Fr & De
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Data Science

Data Science
A Comprehensive Beginner's Guide to Learn the Realms Of Data Science

by William Vance

  • Publisher : joiningthedotstv
  • Release : 2020-07-24
  • Pages : 92
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This book will introduce you to the digital world. Data science is one of the most amazing and trending fields in the digital era. Data science is what makes us humans what we are today. Not limited to computer-driven technologies, this book will guide you to visualize the digital facts and connections of our brain with data science, how to draw conclusions from simple information, and how to develop patterns for understanding different solutions for a similar problem. But our brains can only take us so far when it comes to raw computing. Our brains can't keep up with the amount of data we can capture, and with the extent of our curiosity. So we turned towards machines that are able to capture and store terabytes of information and to do part of the work for us, like recognizing patterns, creating connections, and supplying us with accurate results. Data science is a field where you will be able to get to learn every modern technique. Keeping in mind all these facts, we thought of writing this book targeting the data science beginner. This book provides an overview of data science, teaching you: · What is data science, and how it has emerged · What are the responsibilities of a data scientist and the fundamentals of data science · Overall process with the life cycle of data science · How data science tools, like statistics, probability, etc. · Help to draw insights from data · Basic concept about data modeling, and featurization · How to work with data variables and data science tools · How to visualize the data · How to work with machine learning algorithms and Artificial Neural Networks · Concepts of decision trees and cloud computing. We have included everything a beginner needs to venture into the data science world. Don’t waste another second. Now is your chance to get started!

Roundtable on Data Science Postsecondary Education

Roundtable on Data Science Postsecondary Education
A Compilation of Meeting Highlights

by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Computer Science and Telecommunications Board,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics

  • Publisher : National Academies Press
  • Release : 2020-10-02
  • Pages : 223
  • ISBN : 030967770X
  • Language : En, Es, Fr & De
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Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Practical Data Science

Practical Data Science
A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets

by Andreas François Vermeulen

  • Publisher : Apress
  • Release : 2018-02-21
  • Pages : 805
  • ISBN : 148423054X
  • Language : En, Es, Fr & De
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Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers

Data Science For Dummies

Data Science For Dummies
A Book

by Lillian Pierson,Ryan Swanstrom,Carl Anderson

  • Publisher : John Wiley & Sons
  • Release : 2015-03-09
  • Pages : 408
  • ISBN : 1118841557
  • Language : En, Es, Fr & De
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"Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization."--Provided by publisher.

Introduction to Biomedical Data Science

Introduction to Biomedical Data Science
A Book

by Robert Hoyt,Robert Muenchen

  • Publisher : Lulu.com
  • Release : 2019-11-25
  • Pages : 258
  • ISBN : 179476173X
  • Language : En, Es, Fr & De
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Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback and ebook. Visit the companion website at https: //www.informaticseducation.org for more information. Key features: Real healthcare datasets are used for examples and exercises; Knowledge of a programming language or higher math is not required; Multiple free or open source software programs are presented; YouTube videos are embedded in most chapters; Extensive resources chapter for further reading and learning; PowerPoints and an Instructor Manual

Doing Data Science

Doing Data Science
Straight Talk from the Frontline

by Cathy O'Neil,Rachel Schutt

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2013-10-09
  • Pages : 408
  • ISBN : 1449363903
  • Language : En, Es, Fr & De
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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 University's Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O'Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Building Data Science Teams

Building Data Science Teams
A Book

by DJ Patil

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2011-09-15
  • Pages : 24
  • ISBN : 1449316778
  • Language : En, Es, Fr & De
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As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success. Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

Data Science and Big Data Analytics

Data Science and Big Data Analytics
Discovering, Analyzing, Visualizing and Presenting Data

by EMC Education Services

  • Publisher : John Wiley & Sons
  • Release : 2015-01-05
  • Pages : 432
  • ISBN : 1118876059
  • Language : En, Es, Fr & De
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Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Beginning Data Science with R

Beginning Data Science with R
A Book

by Manas A. Pathak

  • Publisher : Springer
  • Release : 2014-12-08
  • Pages : 157
  • ISBN : 3319120662
  • Language : En, Es, Fr & De
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“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

What Is Data Science?

What Is Data Science?
A Book

by Mike Loukides

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2011-04-10
  • Pages : 22
  • ISBN : 1449336094
  • Language : En, Es, Fr & De
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We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.

Data Science

Data Science
A Book

by John D. Kelleher,Brendan Tierney

  • Publisher : MIT Press
  • Release : 2018-04-13
  • Pages : 280
  • ISBN : 0262347032
  • Language : En, Es, Fr & De
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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 books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Web and Network Data Science

Web and Network Data Science
Modeling Techniques in Predictive Analytics

by Thomas W. Miller

  • Publisher : FT Press
  • Release : 2014-12-19
  • Pages : 384
  • ISBN : 0133887642
  • Language : En, Es, Fr & De
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Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Agile Data Science

Agile Data Science
Building Data Analytics Applications with Hadoop

by Russell Jurney

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2013-10-15
  • Pages : 178
  • ISBN : 1449326927
  • Language : En, Es, Fr & De
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Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Data Science for Business

Data Science for Business
What You Need to Know about Data Mining and Data-Analytic Thinking

by Foster Provost,Tom Fawcett

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2013-07-27
  • Pages : 414
  • ISBN : 144937428X
  • Language : En, Es, Fr & De
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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Head First Statistics

Head First Statistics
A Book

by Dawn Griffiths

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2008-08-26
  • Pages : 716
  • ISBN : 059680086X
  • Language : En, Es, Fr & De
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A comprehensive introduction to statistics that teaches the fundamentals with real-life scenarios, and covers histograms, quartiles, probability, Bayes' theorem, predictions, approximations, random samples, and related topics.

Data Science and Classification

Data Science and Classification
A Book

by Vladimir Batagelj,Hans-Hermann Bock,Anuška Ferligoj,Aleš Žiberna

  • Publisher : Springer Science & Business Media
  • Release : 2006-09-05
  • Pages : 358
  • ISBN : 3540344160
  • Language : En, Es, Fr & De
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Data Science and Classification provides new methodological developments in data analysis and classification. The broad and comprehensive coverage includes the measurement of similarity and dissimilarity, methods for classification and clustering, network and graph analyses, analysis of symbolic data, and web mining. Beyond structural and theoretical results, the book offers application advice for a variety of problems, in medicine, microarray analysis, social network structures, and music.

Innovations in Classification, Data Science, and Information Systems

Innovations in Classification, Data Science, and Information Systems
Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Brandenburg University of Technology, Cottbus, March 12-14, 2003

by Daniel Baier,Klaus-Dieter Wernecke

  • Publisher : Springer Science & Business Media
  • Release : 2006-03-30
  • Pages : 616
  • ISBN : 3540269819
  • Language : En, Es, Fr & De
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The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.