Data Analysis: What are the skills needed to become a data analyst?

今天看到一个讨论贴：如何成为一个牛逼的数据分析师？其中有一贴如此总结。全文如下：

There are two classes of skills that are needed to be a successful data analyst: both soft and technical skills are needed. The core work flow for a data analyst is several fold. Once a problem has been defined, and a hypothesis is to be tested, the data must be drawn out and then analyzed. The resulting analysis is written up and communicated to the interested stake holder. In order to do this there are several hard and soft skills that are required.

Technical Skills:
A basic knowledge of statistics to a rigorous understanding of Machine Learning. Most consumers of analysis will not look at more than descriptive analysis (means, medians, significance).
Computer skills that are useful are a Querying Language (SQL,Hive,Pig), a scripting Language (Python,Matlab), a Statistical Language (R, SAS, SPSS), and a Spreadsheet (Excel).
Soft Skills
Defining the problem and narrowing the analysis down often requires a lot of soft skills. Balancing the demands on your time to reduce infinite what-if scenarios and understanding the requestors needs requires good communication and understanding of the business needs. Avoid agreeing to delivering too much information that will be not useful to solving the core issues.
Knowing the audience. There is a different presentation required for a PM or a CEO. As a Data Analyst, you will be often required to answer to both. A typical PM will want a more collaborative interaction with more scenarios spelled out and a less polished presentation. A CEO will often be looking for a specific recommendation in a small polished presentation.
Delivery. Having a wonderfully accurate predictive model, that has been backtested to deliver a low RMSE, or an AB test that can increase conversion 15% without reducing sales price are all great results. However, without a great presentation key findings may be left out of product road maps and in the backlog for months or years.
看完后，总结一下作者的意思，无非两层：一是要有干货，二是要有思维。干货包括对机器学习的理解，通一门查询语言（SQL，Hive或者Pig），通一门脚本语言（python或者Matlab），通一门统计语言（R，SAS或者SPSS），通一款软件(Excel)。思维包括， 明确问题的核心，理解客户心声，最后一个没读懂！英文有限，翻译有误，请找有道。