Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company´s data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward´s time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management Includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards
Dieses Lehrbuch behandelt die wichtigsten Methoden zur Erkennung und Extraktion von ´´Wissen´´ aus numerischen und nicht-numerischen Datenbanken in Technik und Wirtschaft. Der Autor vermittelt einen kompakten und zugleich fundierten Überblick über die verschiedenen Methoden sowie deren Zielsetzungen und Eigenschaften. Dadurch werden Leser befähigt, Data Mining eigenständig anzuwenden.
You are under surveillance right now. Your cell phone provider tracks your location and knows who´s with you. Your online and in-store purchasing patterns are recorded, and reveal if you´re unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you´re thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it. The powers that surveil us do more than simply store this information. Corporations use surveillance to manipulate not only the news articles and advertisements we each see, but also the prices we´re offered. Governments use surveillance to discriminate, censor, chill free speech, and put people in danger worldwide. And both sides share this information with each other or, even worse, lose it to cybercriminals in huge data breaches. Much of this is voluntary: we cooperate with corporate surveillance because it promises us convenience, and we submit to government surveillance because it promises us protection. The result is a mass surveillance society of our own making. But have we given up more than we´ve gained? In Data and Goliath, security expert Bruce Schneier offers another path, one that values both security and privacy. He shows us exactly what we can do to reform our government surveillance programs and shake up surveillance-based business models, while also providing tips for you to protect your privacy every day. You´ll never look at your phone, your computer, your credit cards, or even your car in the same way again.
Suitable for any manager or team leader that has the green light to implement a data governance program, this book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. It also includes case studies to detail ´do´s´ and ´don´ts´ in real-world situations.
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 process * Algorithms * Spam filters, Naive Bayes, and data wrangling * Logistic regression * Financial modeling * Recommendation engines and causality * Data visualization * Social networks and data journalism * Data engineering, MapReduce, Pregel, and Hadoop Doing 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.
Learn how to use R to turn raw data into insight, knowledge, and understanding. 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.
From an award-winning project comes an inspiring, collaborative book that makes data artistic, personal - and open to all Each week for a year, Giorgia and Stefanie sent each other a postcard describing what had happened to them during that week around a particular theme. But they didn´t write it, they drew it: a week of smiling, a week of apologies, a week of desires. Presenting their fifty-two cards, along with thoughts and ideas about the data-drawing process, Dear Data hopes to inspire you to draw, slow down and make connections with other people, to see the world through a new lens, where everything and anything can be a creative starting point for play and expression.