Rlogo-1R is an open source software designed for statistical computing and graphics. It’s popular because it can run on a wide variety of UNIX platforms, Windows and MacOS software. It also supports a wide range of statistical and graphical techniques and includes conditionals, loops, user-defined functions, and input-output facilities. Because R was developed with statisticians in mind, it includes many features that enable data visualizations.

Our data scientists use R in many projects.  For a pharmaceutical company, they designed and predicted end dates for clinical trials. For an insurance company, they built predictive models to calculate risk profiles and set premiums. And, for a telecommunication company, they used the robust capabilities of R to analyze cybersecurity data.



Python-logoOne of the most popular languages in data science, Python is often used for rapid application development applications. Python includes many libraries used to implement high-value-added data analysis and mining techniques such as neural networks, and random forests. In business applications, Python simplifies user interface extensions such as Iron Python in TIBCO™ Spotfire®.

Python uses an easy-to-understand syntax. It’s considered a general-purpose programming language that emphasizes code readability and programmer productivity. The Python learning curve is fairly low—it’s a good choice for programming novices. The flexibility of Python enables data scientists to perform what, which has never been done before. And, developers often use it for scripting applications.

Our data scientists use Python for solutions in many industries and business functions. Recent solutions include integrating production and accounting systems for manufacturing groups, analysis of live production data and real-time pricing simulations. The team has also helped clients in the financial services industry, where real-time, Big Data analysis is common. They used Python to deliver solutions that increased process efficiency, employee productivity, and data quality.


Want to learn more about our applications of R and Python? Let’s talk!

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