? ? ? 今天看到一個討論貼：如何成為一個牛逼的數(shù)據(jù)分析師？其中有一貼如此總結。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，總結一下作者的意思，無非兩層：一是要有干貨，二是要有思維。干貨包括對機器學習的理解，通一門查詢語言（SQL，Hive或者Pig），通一門腳本語言（python或者Matlab），通一門統(tǒng)計語言（R，SAS或者SPSS），通一款軟件(Excel)。思維包括，?明確問題的核心，理解客戶心聲，最后一個沒讀懂！英文有限，翻譯有誤，請找有道。

轉載于:https://www.cnblogs.com/ming5536/archive/2012/11/21/2781062.html

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