• Internet
• 31.03.2018
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eBook des Monats: O'Reilly: Data

We’ve compiled the best data insights from O’Reilly editors, authors, and Strata speakers for you in one place, so you can dive deep into the latest of what’s happening in data science and big data.

• Business & Industry

  • Big Data Now
    Now in its sixth edition, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve examined throughout 2016. This collection of blog posts, authored by leading thinkers and experts in the field, reflects a unique set of themes we’ve identified as gaining significant attention and traction.
     
  • The Big Data Market
    Which companies have adopted technologies such as Hadoop and Spark, as well as data science in general? And which industries are lagging behind? This O’Reilly ebook provides the results of a unique, data—driven analysis of the market for big data products and technologies.
     
  • Integrated Analytics
    In this O’Reilly report, author Courtney Webster presents a roadmap to data centralization that will help your organization make data accessible, flexible, and actionable. Building a genuine data-driven culture depends on your company’s ability to quickly act upon new findings. This report explains how.
     
  • Data Infrastructure for Next-Gen Finance
    Examine the tools and best practices that leading financial firms are using to migrate data to the cloud, build customer event hubs, and adhere to new rules for governance and security. This report explains how Capital One, MasterCard Advisors, and the Financial Industry Regulatory Authority (FINRA) tackled major data projects with help from technology leaders such as Cloudera and Intel.
     
  • Advancing Procurement Analytics
    This report examines how your company can significantly improve procurement analytics to solve business questions quickly and effectively. Author Federico Castanedo, Chief Data Scientist at WiseAthena.com, explains how a probabilistic, bottom-up approach can significantly increase the quality, speed, and scalability of your data preparation operations--whether you're integrating datasets or cleaning and classifying them.
     
  • The Global Impact of Open Data
    This comprehensive report, developed with support from Omidyar Network, presents detailed case studies of open data projects throughout the world, along with in-depth analysis of what works and what doesn’t.
     
  • Data Science for Modern Manufacturing
    At the precipice of the next great manufacturing revolution—one in which the Industrial Internet of Things (IIoT) and big data analytics are already making a major impact—author Li Ping Chu explores recent manufacturing initiatives in China, Germany, and the US to provide a succinct, hype-free overview of related technologies and applications.
     
  • Mapping Big Data
    To discover the shape and structure of the big data market, the San Francisco-based startup Relato took a unique approach to market research and created the first fully data-driven market report. Company CEO Russell Jurney and his team collected and analyzed raw data from a variety of sources to reveal a boatload of business insights about the big data space. This exceptional report is now available for free download.
     
  • Business Models for the Data Economy
    You’re sitting on a pile of interesting data. How do you transform that into money? It’s easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply collecting and reselling it in raw form. While that’s certainly profitable right now, you’d do well to explore other opportunities if you expect to be in the data business long-term.
     

• Data Science

  • 2016 Data Science Salary Survey
    In this fourth edition of O’Reilly’s Data Science Salary Survey, 983 respondents working across a variety of industries answered questions about the tools they use, the tasks they engage in, and the salaries they make. This year’s survey includes data scientists, engineers, and others in the data space from 45 countries and 45 US states.
     
  • Embedding Analytics in Modern Applications
    In this report, author Courtney Webster reviews several approaches and methods for embedding analytics capabilities into your applications. Should you implement a separate reporting portal, an in-application reporting tab, or go all in with a fully embedded in-page analytics solution? And do you build your own or buy a solution out of the box?
     
  • Data Science in the Cloud with Microsoft Azure Machine Learning and Python
    Learn how to construct and evaluate machine learning models with Azure ML by going through a complete data science example in this ebook—as well as extend Azure ML with downloadable Python code to perform data munging, visualizations, and performance evaluation. At the end, you'll learn how to publish your trained models as web services in the Azure cloud.
     
  • Self-Service Analytics
    Find out how the self-service analytics approach balances greater data access and usage with concerns about security and data governance. See how companies like Yahoo, the City of Chicago, Warby Parker, and Talend are handling self-service analytics in practice.
     
  • Going Pro in Data Science
    Global IT leader CSC's Distinguished Engineer Jerry Overton outlines what he simply calls: data science that works. Each chapter is ideal for current and aspiring data scientists who want to go pro, as well as IT execs and managers hiring in the field.
     
  • Evaluating Machine Learning Models
    If you're new to data science and applied machine learning, this ebook by expert Alice Zheng takes you through an introduction of the machine-learning workflow and evaluating metrics and model selection.
     
  • What is Data Science?
    Why do we suddenly care about statistics and data? This ebook examines the many sides of data science—the technologies, the companies and the unique skill sets.
     

• AI Ebooks

  • Artificial Intelligence Now
    What exactly is current commercial adoption of AI beyond academic labs? In this ten-page report, Spiderbook cofounder Aman Naimat provides the results of a data-driven analysis into the U.S. industries and companies using or building AI products right now.
     
  • Practical Artificial Intelligence in the Cloud
    Only a couple of years ago, artificial intelligence was a cliché, a sad remnant of 1950s-style futurism. Today it’s sexy again, and that far-off future of intelligent bots is just around the corner. Consider the latest trend, AI-as-a-Service (AIaaS). Software vendors such as Google, IBM, and Amazon are rolling out sophisticated cloud-based AI and machine-learning services for a growing market of developers and users in business and academia. Through interviews with consumers and executives of AIaaS vendors, author Mike Barlow examines the primary driver of this new approach: AI is simply too big for any single device or system.
     
  • The New Artificial Intelligence Market
    As with other technologies introduced in the past decade, artificial intelligence is the subject of many market predictions. But what exactly is current commercial adoption of AI beyond academic labs? In this ten-page report, Spiderbook cofounder Aman Naimat provides the results of a data-driven analysis into the U.S. industries and companies using or building AI products right now.
     
  • AI and Medicine
    In this O'Reilly report, you'll explore the potential of--and impediments to--widespread adoption of AI and ML in the medical field. You'll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare.
     
  • What is Artificial Intelligence?
    Explore the state of AI and where we're headed with examples from Google's AlphaGo, self-driving cars, and facial recognition. Learn about 'general intelligence' flexible enough for AIs to learn without supervision, the differences between autonomous and assistive AIs that augment our intelligence, as well as successful systems such as machine learning and computer vision.
     
  • The Future of Machine Intelligence
    Get an exclusive glimpse into the machine intelligence field through the eyes of some of its leading minds. In a series of exclusive interviews, ten leading practitioners and theoreticians cover concepts and innovations that represent the frontiers of ever-smarter machines.
     
  • What Are Conversational Bots?
    Artificial intelligence has made enormous progress in the last five years, and after a decade of texting and messaging on smartphones, people have become comfortable with conversational interfaces. Put together, those two trends mean we’ll soon be chatting with conversational bots: intelligent software designed to make you feel as though you’re talking to a real person. Chatbots are able to automate human tasks by translating fluidly between unstructured language and structured data. Imagine a customer service chat via instant message, email, or voice where the bot has answers before you can ask the question.
     

• Big Data Architecture

  • Architecting for Access: Simplifying Analytics on Big Data Infrastructure
    Fragmented, disparate backend data systems have become the norm in today's enterprise, where you’ll find a mix of relational databases, Hadoop stores, and NoSQL engines, with access and analytics tools bolted on every which way. This mishmash of options presents a real challenge when it comes to choosing frontend analytics and visualization tools.
     
  • Hadoop and Spark Performance for the Enterprise
    It's time to bring Quality of Service (QoS) to distributed processing in multi-tenant Hadoop environments. This O'Reilly report explains how QoS allows operators to assign priorities to jobs, ensuring that higher-priority tasks get the resources needed to meet critical deadlines. Author Andy Oram examines the critical role of performance in the evolution of operating systems, data warehouses, and distributed processing. He also discusses Quasar (part of Mesos) and Pepperdata, two tools that can help improve performance in distributed computing environments.
     
  • In Search of Database Nirvana
    The database pendulum is in full swing. Ten years ago, web-scale companies began moving away from proprietary relational databases to handle big data use cases with NoSQL and Hadoop. Now, for a variety of reasons, the pendulum is swinging back toward SQL-based solutions. What many companies really want is a system that can handle all of their operational, OLTP, BI, and analytic workloads. Could such an all-in-one database exist? This O'Reilly report examines this quest for database nirvana, or what Gartner recently dubbed Hybrid Transaction/Analytical Processing (HTAP). Author Rohit Jain takes an in-depth look at the possibilities and the challenges for companies that long for a single query engine to rule them all.
     
  • Making Sense of Stream Processing
    Martin Kleppmann shows you how stream processing can make your data storage and processing systems more flexible and less complex. Learn how to open up your data for richer analysis and make your applications more scalable and robust.
     
  • Architecting Data Lakes
    Get all the information you need to get started in architecting data lakes: key attributes of a data lake, importance of data management and governance, challenges of building and managing a data lake, self-service options, and emerging trends.
     
  • Hadoop: What You Need to Know
    Learn the basics of technologies such as HDFS, MapReduce, and YARN, without becoming mired in the details. Get the basics of how Hadoop works and why it's such an important technology—and examples of how you should be using it.
     
  • Fast Data: Smart and at Scale
    Examine ways to develop apps for fast data using pre-defined patterns to create a collection of fast data app development recipes.
     
  • Migrating Big Data Analytics into the Cloud
    Our survey of IT and data professionals in finance, healthcare, technology, and telecomm illustrates the way they plan to use big data in the cloud, with predictive analytics leading the charge--and why some are reluctant to join the migration.
     

Source: Free Data Ebooks