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based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to;
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This volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The reader will find numerous;
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Explore the different data mining techniques using the libraries and packages offered by Python Key Features Grasp the basics of data;
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* Implement data cleaning and validation tasks to get your data ready for data mining activities * Execute Exploratory Data Analysis both the;
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Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that;
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that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It;
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The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner;
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Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features;
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first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and;
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involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field;
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involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field;
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data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which;
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scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and;
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This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to;
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> Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest;
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Discover and practice graphical exploratory analysis techniques across domains Book DescriptionHands-On Exploratory Data Analysis with R will;
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cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean;
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Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on;
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of a comprehensive exploratory data evaluation, and the importance of a comprehensive geostatistical approach to the estimation methodology;
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some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use;
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analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and;
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fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different;
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Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting;
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If you are a data scientist of any level, beginners included, and interested in cleaning up your data, this is the book for you! Experience;
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and tricks Key Features Get well-versed with various data cleaning techniques to reveal key insights Manipulate data of different;
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This book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held;
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Find errors and clean up data easily using SASĀ®! Thoroughly updated, Cody's Data Cleaning Techniques Using SAS, Third Edition, addresses;
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