• Complain

Mike Barlow - Learning to Love Data Science

Here you can read online Mike Barlow - Learning to Love Data Science full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2015, publisher: OReilly Media, genre: Politics. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Mike Barlow Learning to Love Data Science
  • Book:
    Learning to Love Data Science
  • Author:
  • Publisher:
    OReilly Media
  • Genre:
  • Year:
    2015
  • Rating:
    5 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 100
    • 1
    • 2
    • 3
    • 4
    • 5

Learning to Love Data Science: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Learning to Love Data Science" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

Until recently, many people thought big data was a passing fad. Data science was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, youll appreciate how data science is fundamentally altering our world, for better and for worse.

Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, youll find out how far data science reaches.

With this anthology, youll learn how:

  • Analysts can now get results from their data queries in near real time
  • Indie manufacturers are blurring the lines between hardware and software
  • Companies try to balance their desire for rapid innovation with the need to tighten data security
  • Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center
  • CIOs have gradually evolved from order takers to business innovators
  • New analytics tools let businesses go beyond data analysis and straight to decision-making

Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.

Mike Barlow: author's other books


Who wrote Learning to Love Data Science? Find out the surname, the name of the author of the book and a list of all author's works by series.

Learning to Love Data Science — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Learning to Love Data Science" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Learning to Love Data Science

by Mike Barlow

Copyright 2015 Mike Barlow. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

Editor: Marie BeaugureauInterior Designer: David Futato
Production Editor: Nicholas AdamsCover Designer: Ellie Volckhausen
Copyeditor: Sharon WilkeyIllustrator: Rebecca Demarest
Proofreader: Sonia Saruba
  • November 2015: First Edition
Revision History for the First Edition
  • 2015-10-26: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491936580 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Learning to Love Data Science, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-491-93658-0

[LSI]

Dedication

For Darlene, Janine, and Paul

Foreword

I met Mike Barlow a couple of years ago at an industry conference in New York. Our mutual interest in the Industrial Internet of Things (IIoT) has led to many interesting conversations, and I have observed some parallels in our experiences as authors.

We have both written about the convergence of key trends such as big data analytics, digital manufacturing, and high-speed networks. We both believe in the IIoTs potential to create new jobs, open new markets, and usher in a new age of global prosperity.

And both of us are glad he landed on the name Learning to Love Data Science for his book. He easily could have named it How Data Science Is Helping Us Build a Better, Safer, and Cleaner World.

Mike and I agree that information captured from machines, fleets of vehicles, and factories can be harnessed to drive new levels of efficiency and productivity gains. As much as I love data science, what I love even more is how it can unleash the power of innovation and creativity across product development, manufacturing, maintenance, and asset performance management.

Were not talking about ordinary analytics, like the kind that serve up recommendations when you use a search engine, but the complex physics-based analytics that detect meaningful patterns before they become an unforeseen problem, pitfall, or missed opportunity. This enables us to deliver positive outcomes like predicting service disruptions before they occur, across a wider spectrum of industries, affecting more people in more places than we could have dreamed of even three years ago.

Recently, Ive read about how data science and advanced analytics are replacing traditional science. Commentary like, All you need to do is look at the data, or The data will tell you everything you need to know, is espoused without really understanding or appreciating what is happening in the background.

Data science isnt replacing anything; to the contrary, data science is adding to our appreciation of the world around us. Data science helps us make better decisions in a complex universe. And I cannot imagine a scenario in which the data itself will simply tell you everything you need to know.

In the future, I envision a day in which data science is so thoroughly embedded into our daily routines that it might seem as though the data itself is magically generating useful insights. As Arthur C. Clarke famously observed, Any sufficiently advanced technology is indistinguishable from magic. Perhaps in the future, data science will indeed seem like magic.

Today, however, heavy lifting of data science is still done by real people. Personally, I believe human beings will always be in the loop, helping us interpret streams of information and finding meaning in the numbers. We will move higher up in the food chain, not be pushed out of the picture by automation. The future of work enhanced by data will enable us to focus on higher-level tasks.

From my perspective, data is a foundational element in a new and exciting era of connected devices, real-time analytics, machine learning, digital manufacturing, synthetic biology, and smart networks. At GE, were taking a leadership role in driving the IIoT because we truly believe data will become a natural resource that ignites the next industrial revolution and helps humanity by making a positive difference in communities around the world.

How much will the IIoT contribute to the global economic picture? Theres a range of estimates. The McKinsey Global Institute estimates it will generate somewhere between $3.4 trillion and $11.1 trillion annually in economic value by 2025. The World Economic Forum (WEF) predicts it will generate $14.2 trillion in 2030. I think its safe to say were on the cusp of something big.

Of course, it involves more than just embracing the next wave of disruptive innovation and technology. The people, processes, and culture around the technology and innovation also have to change. Frankly, the technology part is easy.

Standing up a couple of Hadoop clusters and building a data lake doesnt automatically make your company a data-driven enterprise. Heres a brief list of what youll really need to think about, understand, and accept:

  • How the cultural transformation from analogue to digital impacts people and fundamentally changes how they use data.
  • Why its imperative to deliver contextually relevant insights to people anywhere in the world, precisely when those insights are needed to achieve real business outcomes.
  • Creating minimally viable products and getting them to market before your competitors know what youre doing.
  • Understanding how real machines work in the real world.
  • Rewarding extreme teamwork and incenting risk-takers who know how to create disruptive innovation while staying focused on long-term strategic goals.

The Industrial Internet of Things isnt just about data and analytics. Its about creating a new wave of operational efficiencies that result in smarter cities, zero unplanned outages of power and critical machinery, enormous savings of fuel and energy, and exponentially better management of natural resources. Achieving those goals requires more than just programming skillsyou also need domain expertise, business experience, imagination, and the ability to lead. Thats when the real magic begins.

This collection of reports will expand your understanding of the opportunities and perils facing us at this particular moment in history. Consider it your head start on a journey of discovery, as we traverse the boundary zone between the past, present, and future.

William Ruh, Chief Digital Officer , GE Software

Editors Note

This book is a collection of reports that Mike Barlow wrote for OReilly Media in 2013, 2014, and 2015. The reports focused on topics that are generally associated with data science, machine learning, predictive analytics, and big data, a term that has largely fallen from favor.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Learning to Love Data Science»

Look at similar books to Learning to Love Data Science. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Learning to Love Data Science»

Discussion, reviews of the book Learning to Love Data Science and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.