// Open source with gre­at com­mu­ni­ty sup­port

R & Python

R and Python are excel­lent solu­ti­ons for a wide ran­ge of sta­tis­ti­cal, ana­ly­ti­cal and tech­ni­cal pro­blems. Both lan­guages are available under open source licen­ces, which means that they are sup­port­ed by a lar­ge com­mu­ni­ty and are con­ti­nuous­ly deve­lo­ped.

The­re is a wide ran­ge of available packa­ges for R and Python. In Python, for exam­p­le, the­re are packa­ges such as Sci­Py, Num­Py, pan­das, Ten­sor­Flow and Ker­as, which can be used in data ana­ly­sis and machi­ne lear­ning. In R, on the other hand, the­re are packa­ges such as ggplot2, dplyr and ran­dom­Fo­rest, which are par­ti­cu­lar­ly useful in sta­tis­ti­cal data ana­ly­sis and visua­li­sa­ti­on.

Team office - focus woman in navy blue
// Well posi­tio­ned and future-pro­of

Development environments and tools

Powerful deve­lo­p­ment envi­ron­ments and tools are available for R and Python. RStu­dio and Jupy­ter Note­book are two popu­lar deve­lo­p­ment envi­ron­ments desi­gned spe­ci­fi­cal­ly for R and Python that can speed up the deve­lo­p­ment pro­cess. PyCh­arm is an excel­lent Python deve­lo­p­ment envi­ron­ment with many fea­tures such as auto­ma­tic code com­ple­ti­on and code debug­ging. Git is a ver­si­on con­trol sys­tem that helps deve­lo­pers mana­ge code chan­ges effi­ci­ent­ly.

In sum­ma­ry, R and Python are an excel­lent choice for data sci­ence pro­jects due to their open source natu­re, lar­ge com­mu­ni­ty sup­port, con­ti­nuous deve­lo­p­ment and wide ran­ge of available packa­ges and powerful tools. Com­pa­nies and indi­vi­du­als who rely on R and Python bene­fit from a rich libra­ry of fea­tures that enable them to effec­tively ana­ly­se data and per­form com­plex data ana­ly­sis.

// R and Python cover a wide ran­ge of appli­ca­ti­on are­as

Use cases

With R & Python it is pos­si­ble, for exam­p­le, to

  • cal­cu­la­te spe­ci­fic sta­tis­tics
  • crea­te gra­phi­cal eva­lua­tions
  • gene­ra­te fore­cast models
  • opti­mi­se data-dri­ven pro­ces­ses and much more.

In addi­ti­on to estab­lished methods, sta­te-of-the-art pro­ce­du­res are often available for imple­men­ta­ti­on that are not yet available in other plat­forms.


A rea­dy-made solu­ti­on in R or Python can be

  • be in the form of a script,
  • con­sist of an appli­ca­ti­on (app) with its own user inter­face
  • be deve­lo­ped for a desk­top or ser­ver envi­ron­ment
  • for sin­gle or many users,
  • on-pre­mi­se or in the cloud
  • inte­gra­ted with ano­ther appli­ca­ti­on such as Alte­ryx Desi­gner, TIBCO Sta­tis­ti­ca or TIBCO Spot­fi­re.

Our team has the right expe­ri­ence to build enter­pri­se-rea­dy solu­ti­ons. We dis­cuss the appro­pria­te form with you and find the best vari­ant for your use case.


We sup­port you in all aspects of the use of R and Python - from the sel­ec­tion of the appro­pria­te envi­ron­ment, through the trai­ning of your employees by means of trai­ning cour­ses, to the deve­lo­p­ment of new solu­ti­ons tail­o­red to your pro­blems.