GEO.Engineerings

The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes: Explore R, RStudio, and R packages * Use R for math: variable types, vectors, calling functions, and more * Exploit data structures, including data.frames, matrices, and lists * Read many different types of data * Create attractive, intuitive statistical graphics * Write user-defined functions * Control program flow with if, ifelse, and complex checks * Improve program efficiency with group manipulations * Combine and reshape multiple datasets * Manipulate strings using R’s facilities and regular expressions * Create normal, binomial, and Poisson probability distributions * Build linear, generalized linear, and nonlinear models *Program basic statistics: mean, standard deviation, and t-tests * Train machine learning models * Assess the quality of models and variable selection * Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods * Analyze univariate and multivariate time series data * Group data via K-means and hierarchical clustering * Prepare reports, slideshows, and web pages with knitr * Display interactive data with RMarkdown and htmlwidgets * Implement dashboards with Shiny * Build reusable R packages with devtools and Rcpp.