Interest in and adoption of data science and machine learning is booming with a profusion of new tools, new algorithms, new libraries, and new data sets such as Twitter and more.
The challenge companies have to extract business insights from their data is exacerbated by a continued shortage of skilled data scientists. That skills limitation, coupled with the fact that many existing tools are siloed, requiring different skill sets, has made it increasingly difficult for companies to derive valuable insights that could drive decision-making and business results.
Data Science Experience Local is designed by data scientists to increase their productivity. It solves many of the current challenges, enabling companies to quickly ramp up the impact of data science to get more out of their data from existing resources. Cross-functional teams can now collaborate on the same machine learning project, and the resulting models can be easily deployed, evaluated, and monitored over time.
SPSS Modeler for Data Science Experience offers an integration of a new interface for IBM SPSS Modeler into Data Science Experience Local. With Modeler’s comprehensive visual data science capabilities and intuitive drag-and-drop interface, users can easily accomplish many data science tasks, ranging from data preparation to many machine learning algorithms. Those new to data science can take advantage of automated techniques, including data preparation and modeling. The visual interface has been updated to be accessible with a web browser and shares the same look and feel as Data Science Experience Local. This update to IBM SPSS Modeler includes new interactive visualizations as well. With this integration, both coders and noncoders on the same team can work on different aspects of a data science project.
Read the IBM Software Announcement.
