数据科学，意见(Data Science, Opinion)

Dear Aspiring Data Scientist,

亲爱的有抱负的数据科学家，

Before you start using ‘low code’ or ‘drag & drop’ data science tools, please learn the fundamentals.

在开始使用“低码”或“拖放”数据科学工具之前，请学习基础知识。

Why aspire to be ‘Citizen Data Scientist’ when you can truly become a ‘Data Scientist.’

当您真正成为“数据科学家”时，为什么要成为“公民数据科学家”。

Don’t get swayed by the fancy titles like ‘Citizen Data Scientist.’ It is funny that so much hard selling is happening in data science.

不要被“公民数据科学家”这类奇特的标题所影响。 有趣的是，数据科学领域发生了如此多的艰苦努力。

I mean, just because we know how to use a thermometer or operate BP machine, should we start calling ourselves ‘Citizen Doctor’?

我的意思是，仅仅因为我们知道如何使用温度计或操作BP机，我们才应该开始称自己为“公民医生”吗？

策略-消除了进行数据科学的难度！ (Strategy — undermine the difficulty of doing data science!)

The undermining of difficulty in doing data science is not healthy. Many ‘become a data scientist in a 1-month course’ sellers and ‘low code data science solution’ sellers use this strategy.

破坏数据科学的困难是不健康的。 许多“为期1个月的课程成为数据科学家”的卖家和“低码数据科学解决方案”的卖家都使用这种策略。

The ‘low code/no-code solution’ sellers will often argue that one could gain intuition by *doing* things. The counter-argument to that is, using a low code/no-code solution is like using a calculator. Before one can operate a calculator, one needs to have numeracy skills. Learning the fundamentals in data science is like acquiring numeracy skills.

“低代码/无代码解决方案”的卖家经常会争辩说，做某事可以使人获得直觉。 相反，使用低代码/无代码解决方案就像使用计算器一样。 在您可以操作计算器之前，您需要具备计算能力。 学习数据科学的基础知识就像获得算术技能一样。

为什么85％的数据科学项目失败了？ (提示：游戏中没有皮肤) (Why 85 % of Data Science projects fail? (hint: No skin in the game))

85 % of Data Science projects fail in the enterprise because people think it is easy to do data science but only do it wrongly. The realization often comes late.

85％的数据科学项目在企业中失败了，因为人们认为做数据科学很容易，但是做错了。 认识常常迟到。

Many fall victim to the ‘become a data scientist in 1 month/ 6 months type courses’ and often wonder why they are not being hired.

许多人成为“在1个月/ 6个月类型课程中成为数据科学家”的受害者，并且常常想知道为什么不雇用他们。

The market is the ultimate truth-teller.

市场是最终的讲真话者。

It somehow knows who the good players are and operates an excellent filtering mechanism. The reason being, the market is comprised of companies that have ‘skin in the game.’

它以某种方式知道谁是优秀的参与者，并具有出色的过滤机制。 原因是，市场由拥有“游戏皮肤”的公司组成。

Companies having ‘skin in the game’ don’t gamble. They hire genuine talent. The simple ‘skin in the game’ test one can do by themselves is ask one simple question. Would I use the machine learning classifier myself?

拥有“游戏皮”的公司不会赌博。 他们雇用真正的人才。 一个人可以自己做的简单的“游戏中的皮肤”测试就是问一个简单的问题。 我会自己使用机器学习分类器吗？

I came across a Linkedin post where a person built a heart disease prediction model using one of the low code libraries. The real question is whether that person would use that model on his/her kith and kin?

我在Linkedin上看到一个帖子，一个人使用一个低代码库建立了心脏病预测模型。 真正的问题是那个人是否会在他的厨房和亲戚身上使用那个模型？

Also, the real utility of heart disease prediction or earthquake prediction is not the prediction that it will happen with x% certainty, but WHEN will it happen.

而且，心脏病预测或地震预测的真正用途不是确定会以x％的确定性发生的预测，而是何时发生。

This ‘temporal’ part no model can predict accurately.

没有模型可以准确预测的“时间”部分。

进行数据科学很容易。 还是？ (Doing Data Science is easy. Or is it?)

One of the reasons data science seems *easy to do* is because many algorithms can be fit in 2–3 lines of code. There is simply no intellectual pain.

数据科学“易于实现”的原因之一是因为许多算法可以适合2-3行代码。 根本没有知识上的痛苦。

Compare this to programming. A person has to think about the syntax, design pattern, and logic. When things go astray in programming, there are multiple checkpoints in the form of error alerts like Runtime, Syntax error, and compiler error. One gets an immediate reality check on how good or bad a programmer he/she is. As a result, one does not go up and about calling themselves ‘citizen software engineer.’

将此与编程进行比较。 一个人必须考虑语法，设计模式和逻辑。 当事情在编程中误入歧途时，会以错误警报的形式存在多个检查点，例如运行时，语法错误和编译器错误。 立即获得关于他/她是一名程序员的好坏的现实检查。 结果，一个人不愿意说自己是“公民软件工程师”。

On the flip side, When it comes to data science, there is no runtime or syntax error equivalent. There are no warning signs that says one can’t apply a particular algorithm on the data. There is no immediate reality of check-in data science.

另一方面，在数据科学方面，没有等效的运行时或语法错误。 没有警告迹象表明不能对数据应用特定算法。 签到数据科学没有即时现实。

This is one reason why people who advocate ‘learning the fundamentals is not important’ go scot-free. This is why fancy but harmful titles like ‘citizen Data Scientist’ arise.

这就是倡导“学习基础知识并不重要”的人放弃苏格兰语的原因之一。 这就是为什么出现像“公民数据科学家”这样奇特但有害的标题的原因。

The above criticism might sound rude/bitter, but it is all in the hope that one day we can all say 85% of Data Science projects succeed rather than fail.

上面的批评听起来有些粗鲁/痛苦，但这完全是希望有一天我们可以说85％的数据科学项目成功而不是失败。

I would also encourage the readers to read the articles below:

我也鼓励读者阅读以下文章：

Your comments and feedback are welcome.

欢迎您提出意见和反馈。

You can reach out to me on

您可以继续与我联系

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