Defining a Data Science Model
RasonTM DM is a comprehensive data science modeling language. Data Science is a discovery-driven data analysis technology
used for identifying patterns and relationships in data sets. With overwhelming amounts of data now available from transaction systems and external data
sources, organizations are presented with increasing opportunities to understand and gain insights into their data. Data science is still an emerging field,
and is a convergence of Statistics, Machine Learning and Artificial Intelligence.
Often, there may be more than one approach to a problem. Rason DM provides a high-level API “tool belt” that offers a variety of methods to analyze data.
It has extensive coverage of statistical and machine learning techniques for classification, regression, affinity analysis, data exploration and reduction.
The worlds of commerce, research and government are huge and varied. No single data analysis pattern can possibly be right for everyone. Rason DM
provides a fast, solid and well-tested foundation on which organizations can build and execute data analysis tasks to suit their needs precisely.
Rason DM focuses on Data Science tasks and provides robust implementations of industry-standard Data Science algorithms.
This series of topics assumes that you're familiar with data science methods and techniques, but you've never used the RASON Modeling language to define
such a problem. This series begins with a discussion of the different components that comprise a Rason model and then moves on to several example models
illustrating how to sample from a dataset, create training and validation partitions, perform feature selection and fit a classification model.
For more specific information such as what types of options are available for each data science method, see the Rason Reference Guide.
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