World Programming Unveils New Industrial Analytics and Data Science Features for 2017

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world_programming_logoWorld Programming, a leading industrial analytics and data science platform provider, unveiled new features for data scientists and data science teams including those working within life sciences, pharmaceuticals, and financial services. Designed to deliver more analytical capability and higher productivity, WPS version 3.3 includes support for the Python programming language, joining the platform’s existing support for the data science and scientific computing languages of SAS, SQL and R. Additionally, WPS now provides users with access to a wider variety of open source and commercial libraries and packages within SAS programs, including the widely used scikit-learn—a free machine-learning software library for the Python programming language.

World Programming’s extensive team of software engineers and mathematicians has worked to build one of the industry’s highest-performing, most reliable platforms for data science and production analytics. To date, the World Programming product development team has written more than five million lines of code for the WPS software platform. Six hundred thousand lines of code alone were written for the company’s latest WPS release.

We’re proud of the hard work that have put into the WPS software during the past eighteen months. We’ve made some core improvements, while also adding industry-specific features for our more sophisticated users,” said Oliver Robinson, CEO at World Programming. “Industries such as the life sciences and pharmaceuticals use complex methods to analyse life-saving clinical trials whilst banks and insurers are working to protect your money and reduce premium levels by combatting fraud. WPS version 3.3 aims to make these processes more accessible to the growing field of data science.

Survival Analysis

WPS version 3.3 has a complete set of capabilities for conducting survival analysis. This popular technique has been used in many areas where time-to-event is important to predict. Widely adopted in Medical Health, survival analysis has been the main approach in predicting patients’ survival rate after medical interventions.

Many other verticals have been using this sophisticated statistical technique including manufacturing engineers to study the expected time-to-failure of manufactured components or marketing world to predict customer tenure.

Fraud Detection

Fraud analysis can be taken to a new level with WPS version 3.3. Users can now prepare data using WPS and easily leverage the python language to apply widely available advanced deep-learning models with the Scikit-learn library and the validated Tensorflow and Theano frameworks. Staying within one analytic environment provides a direct lift in performance of fraud models and a reduction in the time taken to perform the work.

Insurance Pricing

WPS version 3.3 includes new methods for building insurance pricing models including a comprehensive set of options for creating insurance rating plans using Generalised Linear Modelling (GLM), a widely adopted modelling approach used by insurance actuaries. The extensive collection of options included can be used for a range of predictive models including claim frequency, claim severity, pure premium, loss ratio, fraud detection and prevention and renewal prediction. Additionally, the new matrix programming features offer iterative methods for computationally intensive operations, and can effectively be utilised in price optimisation algorithms.

WPS version 3.3 runs on Linux on both ARM’s new 64-bit (AARCH64) hardware platform and also the Power Architecture recently opened-up by IBM. Both platforms deliver higher performance and lower power consumption to WPS users allowing for more work to be completed at lower cost. WPS version 3.3 is available now.


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