Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas: Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself. The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research. Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.

    You will learn about the basics of networking like the OSI Model, the difference between IPv4, IPv6, TCP, UDP and more. You will also learn how to configure protocols like spanning-tree, VLANs and trunking on your switches and how routers use routing protocols to build their routing table. This book covers everything for the ICND1 (100-101), ICND2 (200-101) and CCNA combined exam (200-120).

    

    The difficulty of doing without fossil fuels is daunting, possibly even insurmountable. So there is an urgent need to rethink our responses to the crisis. To meet that need, a small but increasingly influential group of scientists is exploring proposals for planned human intervention in the climate system: a stratospheric veil against the sun, the cultivation of photosynthetic plankton, fleets of unmanned ships seeding the clouds. These are the technologies of geoengineerin--and as Oliver Morton argues in this visionary book, it would be as irresponsible to ignore them as it would be foolish to see them as a simple solution to the problem."The Planet Remade" explores the history, politics, and cutting-edge science of geoengineering. Morton weighs both the promise and perils of these controversial strategies and puts them in the broadest possible context. The past century's changes to the planet--to the clouds and the soils, to the winds and the seas, to the great cycles of nitrogen and carbon--have been far more profound than most of us realize. Appreciating those changes clarifies not just the scale of what needs to be done about global warming, but also our relationship to nature.Climate change is not just one of the twenty-first century's defining political challenges. Morton untangles the implications of our failure to meet the challenge of climate change and reintroduces the hope that we might. He addresses the deep fear that comes with seeing humans as a force of nature, and asks what it might mean--and what it might require of us--to try and use that force for good.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    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 advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    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

    This book is going to provide you with everything you need to know about Ethereum and whether it is worth investing in now. Like many people, I became interested in Bitcoin years ago, when Bitcoin was still relatively new, but hinting at a huge profit margin for those who took the risk and invested. It was after a couple of years playing around on the Bitcoin market that I heard about a new type of blockchain technology, one that wasn’t just a digital form of payment, but one that could support potentially endless different types of applications. Not only that, but it comes with its own currency. This, to me, sounded like a potentially profitable situation, so I decided to dig a little deeper. Unlike Bitcoin, Ethereum is still largely unknown to those who don’t keep up with the cryptocurrency world, so the amount of information available is limited or highly technical. Still, it was fascinating and the more I read about Ethereum, the more I began to see its huge potential. And I’m not alone. More and more Fortune 500 companies are investing in Ethereum technology as it becomes increasingly lucrative and poises to change business processes as we know them. I decided to condense my research and share my knowledge on Ethereum by writing this book. The book is designed for those who are new to cryptocurrency, but want to invest in it or learn more about it, as well as for more experienced traders looking to expand their portfolios. With a 5,000% increase in value in the first few months of 2017, Ethereum is proving to be a profitable currency. Still, as it is so new – it was only launched in 2015 – it comes with many infancy-related risks. It’s partly this that makes it so exciting. This book will help you make your own investment decisions and decide if Ethereum is the right coin for you after weighing up the pros and cons that are presented here. So far, Ethereum has made me good money and I was lucky to make the investment when I did. However, now is not too late to invest, not by a long shot. In fact, now is the perfect moment to make the most of Ethereum’s infancy and gain potential first-mover advantages. Ethereum’s technology is only at the beginning of its potential growth stages, possibly reaching to dozens of industries and thousands of services. If its technology is adopted the way it is expected to be, Ethereum will enjoy a long and lucrative spot at the top. The profits are ripe for the taking. Here Is A Preview Of What’s Included… What Is Ethereum? Smart Contracts, Dapps, And DAOs The Technology Behind Ethereum What Is Ethereum Mining? Uses Of Ethereum What Is Ether? The Financial History Of Ether How to Buy, Sell, And Store Ether The Mining Process Of Ether Should I Invest In Ether? The Future Of Ethereum Much, Much More! Get your copy today!

    "Web Analytics 2.0" presents a new framework that will permanently change how you think about analytics. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. The book will help your organization become more data driven while you become a super analysis ninja Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Maybe you re going for MCSA first, then MCSE. Maybe you need to upgrade your current credentials. Now, direct from Microsoft, this set brings together all the study resources you ll need. You get the brand-new Second Edition of all four books: for Exam 70-290 (Managing and Maintaining a Windows Server Environment), 70-291 and 70-293 (Network Infrastructure), and 70-294 (Active Directory). What s new here? Deeper coverage, more case studies, more troubleshooting, plus significant new coverage: Emergency Management Services, DNS, WSUS, Post-Setup Security Updates, traffic monitoring, Network Access Quarantine Control, and much more. There are more than 1,200 highly customizable CD-based practice questions. And, for those who don t have easy acess to Windows Server 2003, there s a 180-day eval version. This package isn t cheap, but there s help there, too: 15% discount coupons good toward all four exams. Bill Camarda, from the August 2006 href="http://www.barnesandnoble.com/newslet... Only

    No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could.What’s more, building good charts is quickly becoming a need-to-have skill for managers. If you’re not doing it, other managers are, and they’re getting noticed for it and getting credit for contributing to your company’s success.In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz today is where spreadsheets and word processors were in the early 1980s—on the cusp of changing how we work. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping.This book is much more than a set of static rules for making visualizations. It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts (and bad ones) create “feelings behind our eyes.” Along the way, Berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice.Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.

    Worries about the dangers that nuclear plants and their radioactive waste posed to nearby communities grew over time, and plant construction in the UnitedStates virtually died after the early 1980s. The 1986 disaster at Chernobyl only reinforced nuclear power's negative image. Yet in the decade prior to the Japanese nuclear crisis of 2011, sentiment about nuclear power underwent a marked change. The alarming acceleration of global warming due to theburning of fossil fuels and concern about dependence on foreign fuel has led policymakers, climate scientists, and energy experts to look once again at nuclear power as a source of energy.In this accessible overview, Charles D. Ferguson provides an authoritative account of the key facts about nuclear energy. What is the origin of nuclear energy? What countries use commercial nuclear power, and how much electricity do they obtain from it? How can future nuclear power plants be madesafer? What can countries do to protect their nuclear facilities from military attacks? How hazardous is radioactive waste? Is nuclear energy a renewable energy source? Featuring a discussion of the recent nuclear crisis in Japan and its ramifications, Ferguson addresses these questions and more inNuclear Energy: What Everyone Needs to Know(R), a book that is essential for anyone looking to learn more about this important issue.What Everyone Needs to Know(R) is a registered trademark of Oxford University Press.