Data Analysis Using Regression & Multilevel Hierarchical Models first published in 2007 is a comprehensive manual for the applied researcher who wants to perform data analysis using linear & nonlinear regression & multilevel models The book introduces a wide variety of models whilst at the same time instructing the reader in how to fit these models using available software packages The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research with programming codes provided for each one Topics covered include causal inference including regression poststratification matching regression discontinuity & instrumental variables as well as multilevel logistic regression & missing-data imputation Practical tips regarding building fitting & understanding are provided throughout ...

Providing essential information for business managers computer programmers system designers as well as home computer users this book provides a thorough introduction that

Includes:: coverage of the language of computer networks as well as the effects of data communications on business & society 456 pages White Curt M

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Uncover hidden patterns of data & respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches & attacks This careful guide explores two of the most powerful data analysis & visualization You'll soon understand how to harness & wield data from collection & storage to management & analysis as well as visualization & presentation Using a hands-on approach with real-world examples this book shows you how to gather feedback measure the effectiveness of your security methods & make better decisions Everything in this book will have practical application for information security professionals Helps IT & security professionals understand & use data so they can thwart attacks & understand & visualize vulnerabilities in their networks
Includes: more than a dozen real-world examples & hands-on exercises that demonstrate how to analyze security data & intelligence & translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire & prepare security data use simple statistical methods to detect malware predict rogue behavior correlate security events & more Written by a team of well-known experts in the field of security & data analysis Lock down your networks prevent hacks & thwart malware by improving visibility into the environment all through the power of data & Security Using Data Analysis Visualization & Dashboards
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A long-time chief data scientist at Amazon shows how open data can make everyone not just corporations richer Every time we Google something Facebook someone Uber somewhere or even just turn on a light we create data that businesses collect & use to make decisions about us In many ways this has enhanced our lives yet we as individuals do not benefit from this wealth of data as much as we could Moreover whether it is a bank evaluating our credit worthiness an insurance company determining our risk level or a potential employer deciding whether we get a job it is likely that this data will be used against us rather than for us In Data for the People Andreas Weigend draws on his years as a consultant for commerce education healthcare travel & finance companies to outline how Big Data can work better for all of us As of today how much we benefit from Big Data depends on how closely the interests of big companies align with our own Too often outdated standards of control & privacy force us into unfair contracts with data companies but it doesn't have to be this way Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us Only then can we the people get back more from Big Data than we give it Big Data is here to stay Now is the time to find out how we can be empowered by it ...

This collaborative book coordinated by the European Journalism Centre & the Open Knowledge Foundation aims to answer questions like Where can I find data? How can I request data? What tools can I use? How can I find stories in data? How can I make data journalism sustainable? It had a very successful start at the Mozilla Festival in London in November 2011 with fifty contributors including data journalists & professors from the New York Times Financial Times Guardian Chicago Tribune Medill School of Journalism & Cronkite School of Journalism Additional contributors now include leading developers analysts FOI experts & other practitioners from places like the BBC City University London Scraperwiki Zeit Online & many others The project has received coverage in the New York Times Liberation Tech Crunch World Editors Forum & other major publications ...

Data Science & Big Data Analytics is about harnessing the power of data for new insights The book covers the breadth of activities & methods & tools that Data Scientists use 432 pages ...

Written by renowned data science experts Foster Provost & Tom Fawcett Data Science for Business introduces the fundamental principles of data science & walks you through the data-analytic thinking necessary for extracting useful knowledge & 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 & data scientists but also how participate intelligently in your company's data science projects You'll also discover how to think data-analytically & fully appreciate how data science methods can support business decision-making Understand how data science fits in your organization
- & 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
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This is a first-principles-based practical introduction to the fundamentals of data science aimed at the mathematically-comfortable reader with some programming skills The book covers The important parts of Python to know The important parts of Math Probability Statistics to know The basics of data science How commonly-used data science techniques work (learning by implementing them) What is Map-Reduce & how to do it in Python Other applications such as NLP Network Analysis & more ...

Data stewards in business & IT are the backbone of a successful data governance implementation because they do the work to make a company's data trusted dependable & high quality Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance including how to organize train & work with data stewards get high-quality business definitions & other metadata & perform the day-to-day tasks using a minimum of the steward's time & effort David Plotkin has loaded this book with practical advice on stewardship so you can get right to work have early successes & measure & communicate those successes gaining more support for this critical effort ...

Now in its second edition DS Malik brings his proven approach toC programming to the CS2 course Clearly written with the student in mind this text focuses on Data Structures &

Includes:: advanced topics in C such as Linked Lists & the Standard Template Library (STL) The text features abundant visual diagrams examples & extended Programming Examples all of which serve to illuminate difficult concepts Complete programming code & clear display of syntax explanation & example are used throughout the text & each chapter concludes with a robust exercise set

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