Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 31

We begin by discussing user environments rather than focusing on specific statistical programming languages. The subsections below contain descriptions of some selected user development environments and related tools. This introductory material may be omitted if desired, and one can safely proceed to Section 2 for descriptions of the most popular statistical software.

Table 1 Summary of selected statistical software.

Software	Open source	Classification	Style	Notes
Python	Y	Popular	Programming	Versatile, popular
R	Y	Popular	Programming	Academia/Industry, active community
SAS	N	Popular	Programming	Strong historical following
SPSS	N	Popular	GUI: menu, dialogs	Popular in scholarly work
C++	Y	Notable	Programming	Fast, low‐level
Excel	N	Notable	GUI: menu, dialogs	Simple, works well for rectangular data
GNU Octave	Y	Notable	Mixed	Open source counterpart to MATLAB
Java	Y	Notable	Programming	Cross‐platform, portable
JavaScript, Typescript	Y	Notable	Programming	Popular, cross‐platform
Maple	N	Notable	Mixed	Academia, algebraic manipulation
MATLAB	N	Notable	Mixed	Speedy, popular among engineers
Minitab	N	Notable	GUI: menu, dialogs	Suitable for teaching and simple analysis
SQL	Y	Notable	Programming	Necessary tool for databases
Stata	N	Notable	GUI: menu, dialogs	Popular in scholary works
Tableau	N	Notable	GUI: menu, dialogs	Popular for business analytics
Julia	Y	Promising	Programming	Speedy, underdeveloped
Scala	Y	Promising	Programming	Typed version of Java, less boilerplate code

Table 2 Summary of selected user environments/workflows.

Software	Virtual environment	Multiple languages	Remote integration	Notes
Emacs, Vim	N	Y	Y	Extensible, steep learning curve
Jupyter project	Y	Y	Y	Open source, interactive data science
RStudio	Y	Y	Y	Excellent at creating reproducible reports/docs