This past month, we here at Decisions created a webinar on integrating R & Python with our Decisions platform. You can watch a recording of the webinar or read on a little further to learn more about what R & Python do and why integrating these with Decisions is both useful and easy to accomplish.
Data analytics is a hot topic these days and analytic “Boot Camps” are springing up around the country. Among the most popular data analytics tools are R and Python. These two tools are open source, free of charge and have extension libraries that include capabilities such as:
- Machine learning (example libraries include: Tensorflow, SciKit Learn, Keras)
- Visualizations and graphing (ggplot2, matplotlib)
- Scientific computing (NumPy)
- Data manipulation (tidyverse, Pandas)
- Mapping (ggmap)
- Image Processing (OpenCV, Pillow)
There are literally thousands of libraries and packages that can be added to R and/or Python and accomplish just about anything you can imagine. In particular, the machine learning packages such as Tensorflow, SciKit Learn and Keras have strong track records and are included in a number of cloud platform machine learning options.
So, given that these applications have all these capabilities, what’s missing?
Application Infrastructure
Well, for starters the world of data analytics and software development are two different worlds. Data analysts will typically take lots and lots of data collected through operations and attempt to model this, looking for insights. These insights typically involve things like customer behavior, operational efficiency or simply detecting anomalies. These insights are important, but there needs to be a way to operationalize this information and that’s where traditional software development comes in.