Jupyter Notebook documents can also be published on RStudio Connect. And more: Even within the languages a multilingual development is possible, so in Python in the module rpy2 the necessary interface to the R-code is found and in R in the above-mentioned reticulate package the other way round. RStudio server and the Jupyter Notebook have integrated the necessary support for both languages. The popularity of Jupyter Notebook extends to the most popular cloud computing services like Amazon’s SageMaker, Google’s Cloud-ML-Engine and Microsoft Azure’s Machine Learning Studio.Īs already discussed in our article about the R-package reticulate, the data scientist of today, even with an existing infrastructure, rarely has to choose one of the two languages. The developers of Project Jupyter also provide a multi-user environment like RStudio Server for Jupyter Notebook in the form of JupyterHub. Especially in the field of data analysis, the development environment „Jupyter Notebook“ is often used, since the documents created here can be used interactively and easily exported and distributed as static reports. Especially in the field of machine learning, which covers processes like image recognition and language analysis, Python is the language of choice. As time passed, Python only became important in the field of data science, when extensive tools for data processing were implemented by additional modules such as “numpy” and “pandas”. The programming language Python, published in 1991, impresses above all with its comparatively simple and easy-to-read syntax as well as its usefulness in a wide variety of applications, from backend development to artificial intelligence and desktop applications. The in-house RStudio Connect, a platform on which published results in the form of scripts, reports or applications created with R’s WebApp framework “Shiny” can be viewed and, if necessary, used interactively. The results can then be conveniently published with a click of a button and thus made accessible to users of all kinds. With the free software RStudio-Server, or the commercial equivalent RStudio-Server-Pro, the developers create an intuitive user interface in which several users can work in parallel on a project basis. As a free software and with over 14000 additional packages listed on R’s largest open source package archive CRAN, you will find the right tool for almost every application. In the meantime, R has enjoyed great popularity among statisticians and analysts from a wide range of disciplines. R & RStudioĪs maintainer of the leading R development environment, package developer and provider of solutions for the professional use of R, RStudio is one of the pioneers for the distribution of R in the enterprise environment. The statistical language R was published in 1993 and was originally developed for statisticians. In order to simplify the answer to the question posed in advance, this article briefly introduces and evaluates the current and most common languages.įirstly, it should be noted that the evaluation of a programming language is usually dependent on the respective requirements of the application and we therefore make a very general assessment. Based on the assessment, it will be identified which programming language is best suited for the requirements in your individual analysis scenario. Data science languages also play a decisive role in implementing the right IT infrastructure. For this reason, the individual languages are also suitable for different areas. „Which programming language should be used for development?“ĭata scientists now have a selection of programming languages at their disposal. But first and foremost, there is usually a central question: Especially for companies that are just beginning to gain a foothold in the data science and analytics world, it is often difficult to select the appropriate tools and processes for their analysis workflow. cloud computing (the outsourced evaluation of analysis scripts). Despite the „modernity“ of the industry, there is now a wealth of software for every need: From the design of the analysis infrastructure to the complete, decentralized evaluation through e.g. As digitalization progresses and data science interfaces continue to grow, new opportunities are constantly emerging to reach the personal analysis goals.
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