CloverETL is Java-based tool/framework for data integration and creation of data transformations. It is component based and follows the concept of transformation graphs which consist of individual nodes/components performing simple (or complex) operations on data. Any transformation can be defined as a set of interconnected nodes through which data flows. CloverETL can be used as a standalone application or be embedded into a larger project.
iLAP (Laboratory data management, Analysis, and Protocol development) is a workflow-driven information management system specifically designed to create and manage experimental protocols and to analyze and share laboratory data. The system combines experimental protocol development, wizard-based data acquisition, and high-throughput data analysis into a single, integrated system.
Cubes is a Python framework for online analytical processing (OLAP), multidimensional analysis, star and snowflake schema denormalization, and cube comptutation. It features a logical model that describes how data are being analyzed and reported, independent of physical data implementation, hierarchical dimensions (attributes that have hierarchical dependencies, such as category-subcategory or country-region), localizable metadata and data localization.
The CloverETL Profiler, software for data profiling, works to examine data in existing data sources and collect statistical information about said data. Through this process, the profiler eliminates the guesswork in analysis, revealing an understanding of what data actually exists and what needs to be improved. Part of the CloverETL Data Integration family, the Profiler is an added tool to the enhanced toolset. Whether employed as a standalone job or part of a greater project, the CloverETL Profiler, as named, operates with the same Engine as CloverETL, offering high performance, speed, and easy deployment in a data environment.
Kst is a fast real-time large-dataset viewing and plotting tool with built-in data analysis functionality. It contains many powerful built-in features and is expandable with plugins and extensions. It features powerful keyboard and mouse plot manipulation, a large selection of built-in plotting and data manipulation functions (such as histograms, equations, and power spectra), built-in filtering and curve fitting capabilities, a convenient command-line interface, a powerful graphical user interface with non-modal dialogs for an optimized workflow, support for several popular data formats, extended annotation objects similar to vector graphics applications, and high-quality export to bitmap or vector formats,
scikits.statsmodels is a Python package which provides a complement to scipy for statistical computations, including descriptive statistics and estimation of statistical models. The main included model categories are linear, discrete, generalized linear, and robust linear models, and, in time series analysis, AR, ARMA, and VAR. It also includes statistical tests mainly for regression diagnostics.
statsmodels is a Python package which provides a complement to scipy for statistical computations, including descriptive statistics and estimation of statistical models. The main included model categories are linear, discrete, generalized linear, and robust linear, and, in time series analysis, AR, ARMA, and VAR. It also includes statistical tests mainly for regression diagnostics. statsmodels was renamed from scikits.statsmodels.