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Data are an organization's sole, non-depletable, non-degrading, durable asset. Engineered right, data's value increases over time because the added dimensions of time, geography, and precision. To achieve data's full organizational value, there must be dedicated individual to leverage data as assets - a Chief Data Officer or CDO who's three job pillars are: Dedication solely to leveraging data assets,Unconstrained by an IT project mindset, andReports directly to the business Once these three pillars are set into place, organizations can leverage t

Management --- Business & Economics --- Management Theory --- Information technology --- Data mining. --- Management. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Intellectual capital. --- Knowledge management.

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R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are g

Data mining --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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Perform accurate data analysis using the power of KNIME Learn the essentials of KNIME, from importing data to data visualization and reporting Utilize a wide range of data processing solutions Visualize your final data sets using KNIME's powerful data visualization options In Detail KNIME is an open source data analytics, reporting, and integration platform, which allows you to analyze a small or large amount of data without having to reach out to programming languages like R. "KNIME Essentials" teaches you all you need to know to start processing your first data sets using KNIME. It covers topics like installation, data processing, and data visualization including the KNIME reporting features. Data processing forms a fundamental part of KNIME, and KNIME Essentials ensures that you are fully comfortable with this aspect of KNIME before showing you how to visualize this data and generate reports. "KNIME Essentials" guides you through the process of the installation of KNIME through to the generation of reports based on data. The main parts between these two phases are the data processing and the visualization. The KNIME variants of data analysis concepts are introduced, and after the configuration and installation description comes the data processing which has many options to convert or extend it. Visualization makes it easier to get an overview for parts of the data, while reporting offers a way to summarize them in a nice way.

Data mining. --- Open source software. --- Free software (Open source software) --- Open code software --- Opensource software --- Computer software --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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With this book on OpenRefine, managing and cleaning your large datasets suddenly got a lot easier! With a cookbook approach and free datasheets included, you’ll quickly and painlessly improve your data managing capabilities. Create links between your dataset and others in an instant Effectively transform data with regular expressions and the General Refine Expression Language Spot issues in your dataset and take effective action with just a few clicks In Detail Data is supposed to be the new gold, but how can you unlock the value in your data? Managing large datasets used to be a task for specialists, but you don't have to worry about inconsistencies or errors anymore. OpenRefine lets you clean, link, and publish your dataset in a breeze. Using OpenRefine takes you on a practical tour of all the handy features of this well-known data transformation tool. It is a hands-on recipe book that teaches you data techniques by example. Starting from the basics, it gradually transforms you into an OpenRefine expert. This book will teach you all the necessary skills to handle any large dataset and to turn it into high-quality data for the Web. After you learn how to analyze data and spot issues, we'll see how we can solve them to obtain a clean dataset. Messy and inconsistent data is recovered through advanced techniques such as automated clustering. We'll then show extract links from keyword and full-text fields using reconciliation and named-entity extraction. Using OpenRefine is more than a manual: it's a guide stuffed with tips and tricks to get the best out of your data.

COMPUTERS --- General --- Engineering & Applied Sciences --- Computer Science --- Data mining. --- Electronic data processing. --- ADP (Data processing) --- Automatic data processing --- Data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Computers --- Office practice --- Database searching --- Automation

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Paola Gloria Ferrario develops and investigates several methods of nonparametric local variance estimation. The first two methods use regression estimations (plug-in), achieving least squares estimates as well as local averaging estimates (partitioning or kernel type). Furthermore, the author uses a partitioning method for the estimation of the local variance based on first and second nearest neighbors (instead of regression estimation). Approaching specific problems of application fields, all the results are extended and generalised to the case where only censored observations are available. Further, simulations have been executed comparing the performance of two different estimators (R-Code available!). As a possible application of the given theory the author proposes a survival analysis of patients who are treated for a specific illness.   Contents ·         Least Squares Estimation of the Local Variance via Plug-In ·         Local Averaging Estimation of the Local Variance via Plug-In ·         Partitioning Estimation of the Local Variance via Nearest Neighbors ·         Estimation of the Local Variance under Censored Observations     Target Groups ·         Researchers and graduate students in the fields of mathematics and statistics ·         Practitioners in the fields of medicine, reliability, finance, and insurance     Author Paola Gloria Ferrario received her doctorate degree (doctor rerum naturalium) from the University of Stuttgart, Germany, in 2012, after having studied Mathematical Engineering at the Polytechnic of Milano, Italy. She taught mathematics to students of economics at University of Hohenheim and now works as a researcher at the University of Lübeck, Germany.

Analysis of variance. --- Estimation theory. --- Mathematics. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Theory --- Mathematics, general. --- Data mining. --- Variational inequalities. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Math --- Science --- Censored observations (Statistics)