S******y
发帖数: 1123 | 1 一个家喻户晓的全球大品牌公司。最近比较新开的data science group
我跟他们组内部的人聊了聊。大概接触了五六个人。说起用的工具，几乎每个人都讲在
用Python R Hive 其中组里有个印度小女生，还特別提到她主要工具就是R
被HM要求在白板上当场写code. 同一道题，答案要求分別用SQL/Hive, Python, R各写
一遍。这个不难，我每天都用这些，挺熟的。拿起marker就写。HM很满意 而且用手机
对着答案拍照. 之后又让比较了几个常用算法的 order of complexity
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基本上没有一个人提到SAS. 震撼！ 没想到啊，因为这家并非硅谷科技公司啊。I was
expecting they would at least ask me about SAS "proc logistic" regression
No. nothing about SAS.
欢迎上我的两小时Python/R/Hive实战速成课程
欢迎联络我 m*********[email protected]
http://www.eventbrite.com/e/pythonrsas-training-saturday-pm-cla via @eventbrite | n******7
发帖数: 12463 | 2 外行问一句，SAS比R有什么优势？
我能想到的
1. legacy SAS code
2. user friendly （这个我猜的）
3. more build-in toolboxes (这个我也猜的)
R有了好的IDE(Rstudio)之后觉得好用多了
另外，感觉最近5年R资源爆发增长
以前找个像样的R 书都难
我想教LD学R，SAS我没用过，也买不起，实在不想学 | s*****n
发帖数: 2174 | 3 这个其实跟行业无关, 没有legacy burden的公司都不会主动去选择SAS吧.
SAS现在就是吃老本, 以后SAS的市场肯定是越来越小, 但是像pharma, bank这些行业,
老本也能吃很久罢了.
我记得我上本科的时候, 统计软件课的老师的就讲过, 咱们课上主要讲SAS, 捎带讲R,
但是推荐大家主要用R, 因为以后肯定是R越来越流行, 话说那是2002年. | S******y
发帖数: 1123 | 4 哈哈，谢谢宋昆大侠回复。您的那位老师真有预见能力！
早知如此，SAS公司还不如早些年IPO上市算了。(虚拟时态)估计当时一定很多人会去买
它家股票 :-) 现在不好说了。 | D******n
发帖数: 2836 | 5 其实SAS挺罗嗦的，一些code用R改写后简单多了。
不知道R的sql接口好不好。
,
,
【在 s*****n 的大作中提到】
: 这个其实跟行业无关, 没有legacy burden的公司都不会主动去选择SAS吧.
: SAS现在就是吃老本, 以后SAS的市场肯定是越来越小, 但是像pharma, bank这些行业,
: 老本也能吃很久罢了.
: 我记得我上本科的时候, 统计软件课的老师的就讲过, 咱们课上主要讲SAS, 捎带讲R,
: 但是推荐大家主要用R, 因为以后肯定是R越来越流行, 话说那是2002年.
| s*****n
发帖数: 2174 | 6 接口挺好的吧, 至少和MySQL, Oracle等等接口没问题.
【在 D******n 的大作中提到】
: 其实SAS挺罗嗦的，一些code用R改写后简单多了。
: 不知道R的sql接口好不好。
:
: ,
: ,
| n****n
发帖数: 11 | 7 换个角度设想一下，如果ＳＡＳ不再高昂收费了，干脆免费了，这时在功能上再比较会
用哪个SAS or R？
除此之外，也简单搜了一下两个的比较。For more detailed comparison, please see
link below (a MIT slides 据说):
http://lgdata.s3-website-us-east-1.amazonaws.com/docs/148/98214
/comparison_presentation.pdf
1-If you want free software with a lot macro available, you could use R. It
is good with not so strong errors. If you want to develop some heavy
calculus and routines on large databases, so SAS is the best.
2-Another thing SAS does much better than R is data manipulation. In
situation of majority work of programming time is spent in DATA steps
cleaning data sets and getting them into the necessary form for the
statistical procedure I need to perform. R is much less efficient at this.
3-General use. SAS is a package that many "power users" like because of its
power and programmability. Because SAS is such a powerful package, it is
also one of the most difficult to learn.
4-To use SAS, you write SAS programs that manipulate your data and perform
your data analyses. If you make a mistake in a SAS program, it can be hard
to see where the error occurred or how to correct it.
5-Data Management. SAS is very powerful in the area of data management,
allowing you to manipulate your data in just about any way possible. SAS
includes proc sql that allows you to perform sql queries on your SAS data
files. However, it can take a long time to learn and understand data
management in SAS. However, SAS can work with many data files at once easing
tasks that involve working with multiple files at once.
6-SAS can handle enormous data files up to 32,768 variables and the number
of records is generally limited to the size of your hard disk.
7-Statistical Analysis. SAS performs most general statistical analyses (
regression, logistic regression, survival analysis, analysis of variance,
factor analysis, multivariate analysis). The greatest strengths of SAS are
probably in its ANOVA, mixed model analysis and multivariate analysis, while
it is probably weakest in ordinal and multinomial logistic regression (
because these commands are especially difficult), and robust methods (it is
difficult to perform robust regression, or other kinds of robust methods).
8-Graphics. SAS may have the most powerful graphic tools among all of the
packages via SAS/Graph. However, SAS/Graph is also very technical and
tricky to learn. The graphs are created largely using syntax language;
however, SAS 8/9 does have a point and click interface for creating graphs.
9-Summary. SAS is a package geared towards power users. It has a steep
learning curve and can be frustrating at first. However, power users enjoy
the its powerful data management and ability to work with numerous data
files at once
10-SAS is not a nice language (compared to R) but it is very, very fast on
large data sets. It also is not expensive (compared to other industry tools)
. R has gotten much faster, but remains slow compared to SAS |
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