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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
It was observed that data scientists would write machine learning algorithms in languages such as R and Python for small data. When it came time to scale to big data, a systems programmer would be needed to scale the algorithm in a language such as Scala. This process typically involved days or weeks per iteration, and errors would occur ...
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
A simple API gives Leo scripts full access to all data in loaded outlines, as well as full access to Leo's own source code. The API includes Python iterators that allow scripts to traverse outlines easily. Scripts may be composed of any tree of nodes. A markup language similar to noweb tells Leo how to create scripts from (parts of) an outline ...
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [32] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...
Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [ 2 ] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to ...
RapidMiner provides a variety of learning schemes, models, and algorithms that can be extended using R and Python scripts. [5] RapidMiner can also use plugins available through the RapidMiner Marketplace. The RapidMiner Marketplace is a platform for developers to create data analysis algorithms and publish them to the community. [6]
Templating engines encourage clean separation of content, graphic design, and program code. This leads to more modular, flexible, and reusable site architectures, shorter development time, and code that is easier to understand and maintain. Cheetah compiles templates into optimized, yet readable, Python code.