blocks|key|2339642|text|在你的“分析事业”中，你需要两者，甚至更多。从SAS开始，SAS工作室是直观和简单的，一旦你对“统计数据”感到舒服，并寻找下一个级别，而不是即将到来的R到位。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2339643|SAS也有免费的课程介绍在stat和他们的平台。SAS免费课程|offset|length|2339644|从长远来看，相信我，这与工具/平台无关。您可以查看免费图书列出的下面和最重要的建模和预测技术所列的书。对许多重要方法的理论和实践理解是必不可少的，在此之后，您将能够在任何语言中进行计算。|2339645|统计学习概论|entityMap|0|LINK|mutability|MUTABLE|url|http://support.sas.com/training/tutorial/index.html|1|http://www-bcf.usc.edu/~gareth/ISL/^0|0|O|7|0|0|0|0|6|1^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@$D|U|E|V|1|W]]|A|$]]|$1|F|3|G|5|6|7|X|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|Y|8|@]|9|@$D|Z|E|10|1|11]]|A|$]]]|J|$K|$5|L|M|N|A|$O|P]]|Q|$5|L|M|N|A|$O|R]]]]

You'll need both or even more for your "career in analytics". Start with SAS, SAS studio is intuitive and easy, once you are comfortable with the "stats" and looking for next level than is coming R in place.

SAS also have free courses for intro in stat and their platform.
<a href="http://support.sas.com/training/tutorial/index.html" rel="nofollow">SAS free courses</a>

For long term believe me that is not about the tools/platform. You can check the free book listed bellow and most important modeling and prediction techniques listed in the book. Theoretical and practical understanding of many important methods is essential and after that you will be able to compute in any language.

<a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow">An Introduction to Statistical Learning</a>

blocks|key|1196869|text|去找R.+SAS是个怪物，一点也不好玩。话虽如此，你的市场价值将更多地取决于你能做什么，想做什么，以及普通教育水平，而不仅仅是这个或那个技术。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|entityMap^0^^$0|@$1|2|3|4|5|6|7|C|8|@]|9|@]|A|$]]]|B|$]]

Go for R. SAS is a monster and is not fun. Having said that your market value will be determined more by what you can and want to do and the general education level than just by this or that technology.

blocks|key|2339338|text|这都要看情况了。SAS很棒，得到了SAS研究所的支持，这意味着如果您为一个在SAS中投资过的组织工作，您可以与支持团队联系，了解软件中发生的任何奇怪的事情。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2339339|R是免费的、开源的，也有一些组织正在建立和支持R，就像SAS研究所所做的那样。不同之处在于SAS存在的时间要比R长得多，结构也要好得多。|2339340|他们都有起起落落，这取决于你计划在职业生涯中做什么样的分析。所以，为了回答你的问题，我会决定你想象自己在做什么。据我所知，R有利于大数据应用和机器学习，SAS对统计分析(ARIMA等)非常有用。|2339341|SAS和R比较：|2339342|http://support.sas.com/resources/papers/proceedings13/348-2013.pdf|offset|length|2339343|http://www.learnanalytics.in/blog/?p=9|entityMap|0|LINK|mutability|MUTABLE|url|1^0|0|0|0|0|0|1U|0|0|0|12|1^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|X|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|Y|8|@]|9|@$J|Z|K|10|1|11]]|A|$]]|$1|L|3|M|5|6|7|12|8|@]|9|@$J|13|K|14|1|15]]|A|$]]]|N|$O|$5|P|Q|R|A|$S|I]]|T|$5|P|Q|R|A|$S|M]]]]

This all depends. SAS is great and is backed by the SAS Institute, meaning if you're working for an organization that has invested in SAS, you can contact the support team for anything funky happening with the software.

R is free and open source, and there are also organizations being created that are building on to and supporting R much like SAS Institute has done. The difference being that SAS has been around much longer and is much more structured than R.

They both have their ups and downs, and it depends on what type of analytics you plan on doing in your career. So, to answer your questions, I would decide what you envision yourself doing. To my understanding R is good for Big Data applications and Machine Learning, SAS is great for statistical analysis (ARIMA, etc).

SAS and R comparisons:

<a href="http://support.sas.com/resources/papers/proceedings13/348-2013.pdf" rel="nofollow">http://support.sas.com/resources/papers/proceedings13/348-2013.pdf</a>

<a href="http://www.learnanalytics.in/blog/?p=9" rel="nofollow">http://www.learnanalytics.in/blog/?p=9</a>

blocks|key|2339473|text|根据我的经验，这里有几件事要考虑：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2339474|你要进入哪个领域|unordered-list-item|2339475|你相信你会用什么样的技术|2339476|你相信你会和什么样的团队一起工作|2339477|字段|header-two|2339478|这是一个巨大的决定因素。据我所知，SAS是金融、银行、一些生物统计和其他行业的标准。另一方面，R是开源的，免费的，在微软收购了革命分析之后，得到了微软的广泛关注。|2339479|技术|2339480|你认为你会在一家大公司还是一家小商店？您认为您需要创建生产准备好的算法还是简单地产生洞察力和分析？|2339481|这很重要的原因是，开放源码技术常常会更容易地转换为生产现成的使用。软件的成本也可能成为小公司的一个障碍，并可能决定你能够使用什么。|2339482|与其他团队的协作|2339483|如果你坚定地站在业务方面，那么你工作的每个人都有可能使用SAS，或者对产出感到满意。在这种情况下，您可能不需要跨任何其他技术进行协作。|2339484|但是，如果您使用技术，则很难将SAS或专有解决方案集成到其他工作流中。一个例子就是创建一个实时用户评分系统。如果您能够将其编程到R或Python中，则可以将代码直接传递给开发人员以供实现。如果涉及到专有解决方案，这将更加困难。|2339485|其他考虑事项|2339486|分析空间正在迅速发展。除此之外，Python作为一种非常流行的技术出现在机器学习和数据挖掘中，并与Spark和其他一些大型数据技术很好地结合在一起。这里的图书馆的一个具体例子是scikit-学习。|2339487|闭幕式思想/我的经验|2339488|我是一个R/Python用户的经验，虽然我有一些经验，SAS在学校和工作。一般来说，我发现对R和Python的支持程度很高--特别是因为它们已经开始流行起来了。SAS有它的用途，而且肯定已经为自己开辟了行业的一部分。|2339489|总之，在统计理论、编程(脚本)以及理解分析可以提供的应用程序和价值方面获得坚实的基础将有很大的帮助。一般来说，这些工具之间的语法是不分开的。与R和Python相比，SAS可能有点奇怪，但所有这些工具通常都具有相当可读的语法，并且很容易适应另一个工具。|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1B|8|@]|9|@]|A|$]]|$1|E|3|F|5|D|7|1C|8|@]|9|@]|A|$]]|$1|G|3|H|5|D|7|1D|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|1E|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|1F|8|@]|9|@]|A|$]]|$1|N|3|O|5|K|7|1G|8|@]|9|@]|A|$]]|$1|P|3|Q|5|6|7|1H|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|1I|8|@]|9|@]|A|$]]|$1|T|3|U|5|K|7|1J|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1K|8|@]|9|@]|A|$]]|$1|X|3|Y|5|6|7|1L|8|@]|9|@]|A|$]]|$1|Z|3|10|5|K|7|1M|8|@]|9|@]|A|$]]|$1|11|3|12|5|6|7|1N|8|@]|9|@]|A|$]]|$1|13|3|14|5|K|7|1O|8|@]|9|@]|A|$]]|$1|15|3|16|5|6|7|1P|8|@]|9|@]|A|$]]|$1|17|3|18|5|6|7|1Q|8|@]|9|@]|A|$]]]|19|$]]

In my experience, there are a few things to consider here:

<ul>
<li>What field you're going into</li>
<li>What kinds of technology you believe you'll be working with</li>
<li>What kinds of teams you believe you'll be working with</li>
</ul>

Field

This is a huge determiner. To my understanding, SAS is standard in finance, banking, some biostats, and other industries. R, on the other hand, is open source, free, and is receiving a lot of attention currently from Microsoft after their acquisition of Revolution Analytics.

Technology

Are you thinking you'll be at a large corporation or a small shop? Do you think you'll need to work with creating production ready algorithms or simply producing insights and analysis?

The reason this matters is that open source technology can often times be a bit easier to convert to production ready use. The cost of software can also be a barrier at smaller companies and may dictate what you are capable of using.

Collaboration with Other Teams

If you sit firmly on the business side, it's possible that everyone you work with may use SAS or may be comfortable with the outputs. In this case, you may not need to collaborate across any additional technology.

If you work with technology, however, it may be difficult to integrate SAS or proprietary solutions into other workflows. An example would be creating a real-time user scoring system. If you were able to program this into R or Python, you may be able to pass the code directly to a developer for implementation. This would be more difficult if a proprietary solution was involved.

Other Considerations

The analytics space is evolving rapidly. On top of the above, Python is coming out as a very popular technology for use in machine learning and data mining and pairs well with Spark as well as some other large data technology. A specific example of a library here would be scikit-learn.

Closing Thoughts/My Experience

I'm an R/Python user by experience, though I have some experience with SAS in school as well as in work. Generally, I've found the level of support with R and Python to be great - especially since they've started to rise in popularity. SAS has its uses and has definitely carved out piece of the industry for itself.

All in all, however - getting a solid base in statistical theory, programming (scripting), and understanding the application and value that analysis can provide will go a long way. The syntax between these tools is, generally, not worlds apart. SAS can be a bit strange compared to R and Python, but all of these tools generally have syntax which is fairly readable and is not difficult to adapt to another tool.

blocks|key|1196967|text|@ImperativelyAblative有许多优点。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1196968|此外，许多企业，尤其是银行，已经开始尝试R.，这是由于节省成本和成熟的企业支持，想想RStudio服务器和Cloudera。SAS是伟大的，但它附带一个溢价标签(一些人可能会认为它甚至过高的价格)。|1196969|我是一位管理顾问，在一家顶级公司工作，专业是数据科学。我们为大多数全球公司提供服务，并与他们的高管和运营团队合作。转向开源工具，如R，得到了许多高层管理人员的支持，并在核心分析团队(市场营销、建模等)之间建立了跟踪。关键是-市场正在向开源工具发展。|1196970|在我看来，另一个考虑是你渴望在一个组织中扮演的角色。以下是一些基于我与分析团队合作经验的原型：|1196971|一般业务分析师:+R，交互式可视化工具(Tableau+/+QlikView)，一些SQL和Excel+/+Powerpoint+(当然)|unordered-list-item|1196972|建模者:+SAS是必须的，R(我的客户都在尝试了解这一点)，深入SQL+/+HQL+(查询Hadoop堆栈)，以及强大的领域知识(风险模型、定价、操作优化等)。|1196973|应用程序开发人员(将产品投入生产的人员)：SAS必须、Python和自动化语言|1196974|当然，这不是一个详尽的清单，但希望它提供一些行业趋势供您参考。在任何组织中，总会有一个超级明星知道所有这些东西，可能就是你:)|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|V|8|@]|9|@]|A|$]]|$1|K|3|L|5|J|7|W|8|@]|9|@]|A|$]]|$1|M|3|N|5|J|7|X|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|Y|8|@]|9|@]|A|$]]]|Q|$]]

@ImperativelyAblative has many good points. 

Adding to that, many companies, especially banks, that rooted in SAS have started to experiment with R. This is because of the cost saving and maturing enterprise support, think of RStudio Server and Cloudera. SAS is great, but it comes with a premium price tag (some may argue it's even overpriced). 

I am a management consultant working at a top tier firm with specialty in data science. We serve most of the global companies and work with their executives and operating teams. Moving to open source tools, such as R, has gained the blessing of many top executives and building tractions among core analytics teams (Marketing, Modelling, etc.). The point being - the market is moving towards open source tools

The other consideration, in my opinion, is the role you aspire to play in an organization. Here are some archetypes based on my experience in working with the analytics teams: 

<ul>
<li>General Business Analyst: R, Interactive visualization tools (Tableau / QlikView), some SQL, and Excel / Powerpoint (of course) </li>
<li>Modeller: SAS is must, R (my clients are all trying to pick this up), deep in SQL / HQL (querying on Hadoop stack), and strong domain knowledge (risk model, pricing, operation optimization, etc.)</li>
<li>Application Developer (people who put things into production): SAS is must, Python, and automation language </li>
</ul>

Of course, this is not an exhaustive list, but hope it provides some industry trend for your reference. There is always the superstar in any organization who knows all these stuff, could be you :)

As I'm starting my career in analytics, I have to choose between SAS in-memory analytics and Openware and largely adopted R, which one should I choose now so that I will have good market value in short-term as well as long-term?

which technology should I invest into SAS In-memory analytics or R?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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当我开始我在分析领域的职业生涯时，我必须在SAS内存分析和Openware之间做出选择，并在很大程度上采用R，我现在应该选择哪一个，这样我就能在短期和长期内都有良好的市场价值。

问我应该投资哪种技术在SAS内存分析或R？
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