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Processing and Analyzing Financial Data with R is een boek van Marcelo S Perlin;
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Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline;
Vergelijkbare producten zoals Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R
Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline;
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will provide even the novice programmer with the tools necessary to process and analyze their data using the R environment. Introductory;
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characterize modern financial markets. The first two sections appeal to readers with technical backgrounds, describing data analysis and ways;
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insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R;
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Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices;
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This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the;
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Working with big data can be complex and challenging, in part because of the multiple analysis frameworks and tools required;
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of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate;
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applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to;
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A PROVEN APPROACH FOR CREATING and IMPLEMENTING EFFECTIVE GOVERNANCE for DATA and ANALYTICS Financial Institution Advantage and the;
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Take control of your finances with Accounting and Financial Management. This solid resource for builders, remodelers, developers, and;
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, modifying, and writing image, audio, and video files using the data analysis and visualization tool MATLAB (R). Key Features;
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, modifying, and writing image, audio, and video files using the data analysis and visualization tool MATLAB (R). Key Features;
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, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes;
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of Big Data processing using R programming language and its powerful statistical capabilities * Deploy Big Data analytics platforms with;
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The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited;
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, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics;
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illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and;
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how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. Code and a variety of additional;
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applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and;
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scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as;
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how to implement sentiment text analysis * Install data analysis tools such as Pandas, MongoDB, and Apache Spark * Get to grips with Apache;
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This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The;
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This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The;
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and Data Security, FC 2017, held in Sliema, Malta, in April 2017. The 30 revised full papers and 5 short papers were carefully selected and;
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