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#0 什么是数据分析? – 译学馆
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#0 什么是数据分析?

Data Analysis 0: Introduction to Data Analysis - Computerphile

Okay, so artificial intelligence, machine learning, data mining, data analysis,
人工智能 机器学习 数据挖掘 数据分析
clustering classification, data pre-processing,
聚类分析 数据预处理
big data.
还有大数据
00 – 数据分析入门 电脑狂热
It’s hard to go anywhere now without hearing about AI and machine learning and data,
现在到处都在谈论AI 机器学习和数据
data particularly, it’s everywhere.
尤其是数据 到处都是
Researchers have suggested that every two years we generate more data than ever existed before
研究表明 我们每两年就会制造出比以往更多的数据
So the amount of data is doubling every two years.
因此数据量每两年就会翻一番
The fact is actually, you know astronomical amount of data,
数据量巨大是事实
but the thing is of course that, these data doesn’t necessarily mean anything.
但当然了 这些数据不一定有意义
In fact, you can create tables of data
其实你也可以创建大量数据
but unless you understand what’s in them and what they mean,
但如果你不了解它们的构成和意义
you haven’t got any knowledge, right?
你就没学到什么知识 对吧?
So there’s a distinction between having data and having knowledge.
所以有数据和有知识 二者是存在区别的
So very well saying, yes, as a species, we’re producing a huge amount of data,
没错 我们人类制造出了大量数据
but actually a lot of it doesn’t get used.
但大部分数据其实并未得到使用
a lot of it sits there on a hard disk, waiting for someone to look at it.
大量存储在硬盘上的数据在等着人们来探索它
And that’s kind of what we’re talking about here.
这就是我们要探讨的这类数据
If we want to extract knowledge from data,
如果要从数据中提取知识
we’re going to need some tools and processes to do this in a formal way,
我们就需要一些工具和过程来正式地实施
and that’s that’s what data science is, right?
这就是数据科学
And things like machine learning and AI have a place within it
而机器学习和AI之类的技术在这一领域就有着一席之地
So perhaps if you do this for your job,
如果这是你的工作
then data analysis is going to be useful for you.
那么数据分析将能助你一臂之力
Maybe your company’s generating data and you want to analyze this data?
也许你的公司生成数据而你要对其作出分析
But on the other hand, perhaps you’re just a consumer, and companies are using data on you.
也有可能你只是一名消费者 而公司在对你使用数据
They’re generating data on you, and actually they’re profiting from data on you.
他们拿你去生成数据 而且还拿你的数据去盈利
These are sometimes life-changing decisions that are being made on your data.
他们有时会用你的数据做一些改变人生的决策
And so it’s empowering to know how this process works.
所以你最好能明白这个过程
And I have a very simple example which you might even do yourself.
我举一个非常简单的例子 你甚至可以自己动手试试
Suppose you go online to book some flights for a holiday,
假设你上网去订一个度假的航班
and then you decide that actually two flights via an intermediate airport
你发现 订两个中转航班
is cheaper than a single flight, right?
比订一个直达航班要便宜一些
You’re doing data analysis. Say you’re taking lots of different data sources
你这就是在做数据分析 你收集了大量不同数据源
and working out the optimal route.
然后找出了最佳路线
And this of course happens automatically as well,
当然这个过程也可以是自动的
depending on the flight website that you’re using.
这要取决于你用的哪个航班网站
All right, so this kind of stuff you’re already doing it.
你已经在做数据分析了
It’s just a case of trying to formalize this process.
这就是一个将你订航班的过程形式化的情况
So what do any of the things I listed at the beginning mean?
那么 我在视频开头列出那些名词是什么意思呢?
Well, one problem is that everyone’s definitions differ slightly,
每个人对此都有不同的定义
but also I think that a lot of these terms are used completely interchangeably.
但我认为 大量这种术语是完全可以通用的
AI is the classic example.
AI就是一个典例
So AI is everywhere, right? You can’t buy a product without it having been having AI added to it.
AI到处都是 购买产品都要用到AI
A lot of the time you see AI,
但往往你看到AI这个词的时候
we’re actually talking about machine learning
我们实际探讨的是机器学习
So machine learning is the idea that we’re training a machine to perform a task
机器学习指的是 在没有显式编程的前提下
without explicitly programming it to do so.
训练机器执行任务
A good example of AI that isn’t machine learning would be, let’s say a mouse in a maze,
迷宫中的老鼠 是说明AI并非机器学习的一个好例子
where all you’re doing is telling it to turn left or right at random.
你只需要随机告诉它左转还是右转
Not learning anything, it doesn’t understand what the maze is
这只老鼠没有学到任何东西 也也不明白什么是迷宫
but it will eventually get to the end, right?
但最后它还是会走出迷宫的
That’s a kind of rudimentary artificial intelligence that doesn’t involve learning anything.
这就是一种不涉及学习的基本人工智能
Machine learning is about not giving it conditions,
机器学习不是给出条件
not saying “if you’re here, turn left; if you’re here, turn right”.
不是“如果你到这里就左转 到这里就右转”
It’s just giving it examples and hoping it will learn to perform most tasks itself, right?
而只是给出案例 希望机器能学会自己去执行大多数任务
So machine learning is a subset of AI, but they shouldn’t be used interchangeably.
所以 机器学习是AI的一部分 二者不应通用
If we’re using machine learning, often what we’ll do
如果要用机器学习的话 我们往往要
is we train it based on samples of data.
基于数据样本进行训练
So we’ll have some existing data set that we’re trying to train on,
所以要有一些用于训练的数据集
and we’re trying to use machine learning to either
然后试着用机器学习
tease out information or make predictions on these data.
来梳理个中信息或作出预测
The problem is that not all data is sort of made equal.
但问题在于 数据质量不一
Some of its noisy and messy, maybe we don’t know what it is
有的数据非常混乱 存在很多噪声 我们可能看不出它是什么
and don’t know whether we can apply a certain technique to it, right?
也不知道能否用什么技术来处理它
And so we need to clean this data up.
所以我们需要进行数据清洗
We need to take this data, understand what it is and extract some knowledge,
我们要获取数据 了解数据 并且从中提取出一些知识
so that we can then apply these AI or machine learning techniques to it.
然后才能对其应用AI或机器学习技术
So this combination of things that can take data and prepare it
获取数据 以及为使用和理解它们
in a way that we can then use it or understand it, that’s data science.
而做准备的整个过程 就是数据科学
There are quite a few ways we could do this data analysis right throughout this course.
本课用到的数据分析的手段有很多
We could use R, we could use Python, we could use MATLAB. They all have their pros and cons
包括R Python和MATLAB 这些工具各有利弊
We’re gonna use R because it’s free and it’s really good for statistical analysis
我们将选择R 因为它免费 而且非常适合统计分析
It’s got loads of great libraries.
还有大量的包
If you’re really familiar with Python, then maybe that’s what you want to start with for this kind of stuff.
如果你很熟悉Python的话 也可以用它来入门
But we know we’re going to be working with R
但在本课中 我们要用R
We have our script area here where we can write scripts and run scripts.
这里是脚本区域 可以写脚本和运行脚本
You can save them and then come back to them later.
你可以保存脚本 回头再来编辑
Console where we’re going to be putting in, you know, specific commands.
这里是控制台 用于运行一些命令
We have our environment, which is where all our variables and our data is held
环境是储存和查看变量与数据的地方
and we can look at them there.
可以看这里
And then we have plots, any plots, which you can do quite a lot of different plots in R, very versatile.
还有图像 你可以用R画很多种类的图像 非常万能
That’s going to appear down here.
图像会显示在这里
Okay, so you’ve probably got everything you need to get started with data analysis.
有了这些 你就可以开始数据分析了
In my opinion, the best way to get into R is just to kind of have a go.
在我看来 学习R的最佳方法就是上手试试
So it’s going to look at a few of the most obvious things that it does.
我们将看看它的一些常用功能
It has a little bit of a learning curve only because it’s syntax is slightly unusual.
R学起来有点费工夫 只因它的语法有些与众不同
If you can program you’ll be fine, but even if not, you should get there pretty quickly.
如果你有编程基础 就没什么问题 但即使没有 你也能很快上手
Most of the time in R we’ll be using either matrices or vectors
在R中 我们大部分使用的是矩阵 向量
or which are kind of a special case of matrices or maybe data frames.
或矩阵的一种特别形式 或数据框
Data frames a really nice aspect of R,
数据框是R中非常好用的一个对象
which you can kind of think of like a table that you might have in in Excel,
你可以把它想成是Excel中的表格
except you’ve also got headings for your columns.
但数据框是有列标题的
So let’s have a look at some of these things, and just a few of the things we can do with them
我们先学习一下其中几个对象的使用
before we perhaps go into a little bit more detail in other videos.
然后再在其他视频中详细学习
So for example, we might look at our variable X which I’ve created
举个例子 看我创建的这个变量X
and X is a sequence going from 0 all the way up to a few multiples of Pi,
X是一个从0到Pi的几倍数的序列
which I used to create this plot.
我用它画了这个图
That was only one line of code that produced that
画这个图只用了一行代码
and I’ve used that to create my plot by essentially saying y equals sin(X),
说明y=sin(x)之后
and then just simply plotting that.
就能画出这个图了
If you wanna get a little bit more complicated, we can start looking at matrix data.
如果你想更复杂点 我们可以考虑一下矩阵数据
So I created a CSV file with a Gaussian function in it.
我创建了一个包含高斯函数的CSV文件
So essentially a two-dimensional array of values
高斯函数就是一个二维数组
that get bigger in the center. Very straightforward.
越靠近中心的值越大 很好懂吧
The CSV file is essentially a text file with commas separating those values,
而CSV文件是一个用逗号隔开这些值的文本文件
very easy to read and write these out of Excel and other packages
它用Excel和其他包来读写都很方便
and so you’ll often find data is passed around in this way,
所以数据经常会以这种形式传输
at least moderately sized data, if it isn’t too, you know to it too huge.
至少不太大的数据如此
I can load this in using my “read.csv” function.
用“read.csv”函数可以导入这份数据
So I can say “namedata”.
并将数据存在“namedata”名下
Now the arrow operator is essentially equivalent in R
箭头运算符在R中的作用
for the assignment operators or equals.
和赋值运算符等号基本相同
Equals will often work, but I tend to try and use this one. So “namedata”…
等号通常也能行 但我喜欢用箭头 输入“namedata”
I’m going to assign “read.csv” and the file is going to be “norm.csv”
我要把”read.csv”这个函数赋给它 文件是”norm.csv”
And I’ve got no header for this file,
由于这个文件没有数据头
so I don’t want it to use the top row for the labels
所以不需要把第一行作为列标签
So I’m going to say “header” equals “false”.
所以“header”参数等于”false”
And that’s loaded in “namedata”. And we can have a look,
然后数据就存到“namedata”里了 我们可以看看
so I’m gonna click on “namedata” here.
我点一下这里的“namedata”
And if we click on it, you can see we’ve got
点击它 你就能看到
the rows and the columns of our data in here.
数据有多少行 多少列
We can look at individual elements in this array.
我们可以看看这个数组中的单个元素
So we can say data at position three four,
比如想看坐标为[3, 4]的数据
and that’s going to be the third row down and the fourth value across.
这指的是第三行第四列的数据
We can also leave one empty and just have an entire row,
我们也可以空出一个参数
or conversely, an entire column, like this.
查看一整行或一整列
And so it’s very easy to take ranges of values.
所以查看指定范围的值是很容易的
You’ve got a huge table of data selecting certain columns,
这个大的数据表可以用来查询指定列
looking at certain columns, plotting certain columns.
查看指定列和给指定列画图
This is one of the reasons why R is very popular.
这便是R如此常用的原因之一
Quite often when you’re looking at data,
往往我们在查看数据的时候
we’ll actually be looking at something called a data frame.
查看的是一种叫数据框的东西
Now a data frame – I’ve got a load one up –
我已经载入了一个数据框
is simply a… In essence, a table of values, but it won’t have to be the same type.
它其实就是一个数据表 但数据类型无需一致
So in an array, normally they’ll all be floats or they’ll all be integers.
所以数组中的值一般都是浮点数或整数
In a data frame, there can be different things,
而数据框中的值类型可以有所不同
so you could have first and last name next to age, for example.
比如你可以在年龄旁边写上姓和名
So I’ve just created a tiny little CSV file
我刚创建了一个小的CSV文件
with some random people in it. So let’s load this up.
里面有一些随机的人员信息 我们来载入看看
So I’m going to say “namedata”
输入“namedata”
assign “read.csv(names.csv)”
赋值以“read.csv(names.csv)”
And if I look at “namedata”, you can see that it’s got three columns,
查看“namedata”数据集 你可以看到它有三列
it’s got firstname, surname and age,
分别是“名” “姓”和“年龄”
and five rows, and there’s five people in this dataset.
有五行 表示数据集中有五个人
And then you can do just like I did before,
然后你可以像我之前那样做
but now we can also index by the names of these columns.
但现在我们也可以根据列名来查找
So I could say I want all of the first names for example,
举个例子 如果我想知道所有的名
so I can say “namedata$firstname”
就可以输入“namedata$firstname”
and I can see all the different first names.
然后我就能看到所有的名了
So you can start to look at this data set and more in more detail.
你可以浏览数据然后逐渐深挖细节
Obviously, this isn’t absolute tiny data set, but you get the idea.
显然这不是一个绝对小的数据集 但你应该明白我的意思
You could also look at individual instances, so we could say “namedata”.
你也可以查看单个实例 先输入“namedata”
And I want just the second row, for example, “namedata[2,]”.
如果我只想看第二行 就输入“namedata[2,]”
There we go, Bill Jones and he’s 18 years old.
结果出来了 这个人叫Bill Jones 18岁
As we move through these videos, it’s going to be very common for us
随着这些视频的学习 我们将学会
to load in datasets like this in this format.
熟练地载入这种格式的数据集
and then start to process them based on these data frames.
并且开始学会处理这些数据框
So perhaps an example, right? So let’s imagine you’re an online retailer,
我举个例子吧 假设你是一名网络零售商
and someone comes into your shop and buy some thing.
有人到你的店里买东西
And maybe they… you’re trying to understand what it is what they do, so that you can,
你试着去了解他们的购买行为 这样才能
let’s say, send them emails to try and get them to buy more products,
举个例子 才能给他们发邮件 吸引他们购买更多商品
or show them recommended products and things like this.
或者给他们推荐商品 等等
So you want to try and build up a pattern of their behavior, right?
你想给他们的行为建立模式
And all you’ve got is what they click on, what they add to their basket,
而你掌握的信息是 他们点击了什么 添加了什么到购物车
and what they buy, right?
以及购买了什么
So you’ve learned that they’re looking at these kinds of items and they look at these ones regularly.
你知道他们浏览了这几种商品 以及经常浏览这些商品
And then sometimes they just buy something completely random seemingly,
但有时他们会看似非常随机地购买商品
and that goes in their basket and gets bought straight away.
把商品加入购物车然后直接购买
Maybe it’s a present right? So maybe it’s not tied to them as a person.
可能他是在买礼物?所以这次购买行为可能与他本人并不挂钩
So you’re taking all of this data all of these purchases, all of these… products that they’re looking at,
你把这些购买记录 和他们浏览过的所有商品记录了下来
and you’re turning this into a kind of picture of this person,
把这些数据绘制成了此人的图像
and you’re clustering that person in with other consumers that bought similar things,
把此人和其他购买相似商品的消费者归为一类
and trying to predict what they want to buy next, right?
并试着预测他们接下来要买的东西
And that’s when you send them an email say “you should look at this one
这时候你就可以给他们发邮件说 “你应该看看这个商品
because this one’s really good and you didn’t buy it last time, but you’ll definitely want to buy it this time”.
上次你没有买它 但是它真的很好 这次你一定会想买的”
So we’ve got some data we want to extract some knowledge.
我们掌握了一些数据 想从中提取一些知识
What’s the first thing we do?
第一步该做什么呢?
We have to start to look at it
我们必须开始浏览数据
and try and tease out some kind of information or analyze this data.
尝试从中梳理出一些信息或作出分析
The data analysis is the idea of using statistical measures to try and work out what’s going on.
数据分析是用统计手段把事情弄明白的过程
This is kind of a cycle. We’re going to analyze the data so we’re going to do a data analysis,
这是一种循环 我们要分析数据 所以要进行数据分析
and perhaps sometimes just using statistics to analyze the data isn’t enough.
但或许有时只用统计资料分析数据并不够
You can’t really learn everything about it.
你无法因此而了解它的全貌
Yes, you can learn, you know, mathematically how it works,
的确 你能了解到它的数学原理
but you might not understand about what it all means
但可能无法理解它的全部含义
So visualizing the data can be really helpful.
而对数据进行可视化可以助我们一臂之力
So what we’ll also do is we’ll visualize the data – visualization.
所以我们要对数据进行可视化——数据可视化
So that’s going to be charting it, plotting it,
数据可视化指的是对数据做表 画图
trying to work out trends and links between different variables and things like this.
找出趋势和不同变量之间的联系 等等
And these are kind of being back and forth, right,
这种事情可以反复做
you could do both of these things numerous times and work out what we’ve got, right?
可以在重复多次之后找出我们想要的东西
So you’re gonna do something like this.
这是你要做的
And then what we’re going to do is we’re going to preprocess the data.
然后我们要进行数据预处理
Often you’ll be finding your recording much more data than you actually need. Right.
有时候你会发现 你记录的数据比你所需要的多得多
This is certainly true of an online shop.
网店就经常出现这种情况
I’m going to be looking at a lot of products,
我浏览了很多商品
but I don’t end up buying and I was never really going to buy.
但最终并未购买 而且其实我本就并无购买的意愿
I know maybe a pipe dream.
可能会在梦中买吧
And they’ve got a sort of weed out this information
店主把这条信息清除掉了
to work out what it is that they might actually better convince me to buy right?
这样才能真正找出那种可以说服我购买的东西
So this is going to you going to preprocess data and remove a nonsense,
这就是预处理数据 删除无意义数据
and drill right down to the stuff that’s really useful.
然后深入挖掘出真正有用的东西
So this is preprocessing.
这就是数据预处理
And this is going to be a kind of cycle of analysis and visualization and preprocessing,
数据分析 数据可视化和数据预处理可以构成一个循环
and we can repeat these things and then we can really drill down and whittle down our data
然后我们就能深挖 尽可能将数据压缩到
into the most usable sort of core of knowledge that we can.
只剩下最有用的核心知识
And get the most out of it.
然后充分利用它
Now it may be that just analysing the data is enough, right?
可能只分析还不够
You’ve now sort of you’ve obtained some knowledge.
你现在已经学会了一些知识
You kind of understand what the trends are.
了解了趋势是什么
and maybe that was all you wanted to do. That’s sometimes the case.
觉得到此为止就行 有时候的确可以这样
Maybe actually what we want to do is take things a little bit further
但我们可能其实想进一步
We’re going to use machine learning or modeling
打算用机器学习或建模
to try and model this system and predict what’s going to happen next.
来模拟该系统 预测下一步
So for example in the case of an online shop,
比如在网店案例中
we might want to start predicting what people are going to buy next
我们想开始预测客户接下来的购买意向
and if we can do that, that’s when we can send out these emails
如果我们能成功 就可以给他们发送邮件
or flag things in their recommended items and get many more sales.
或标出给他们推荐的商品 增加销售量
As an example, let’s imagine that someone has spent a lot of time looking at DIY tools.
举个例子 假设一个人花了很多时间浏览DIY工具
I’ve, you know, recently moved house I spent a lot of time doing DIY,
我最近刚搬了家 也是花了很多时间去DIY
and I’m always trying to buy new tools because it just seems like a good idea.
我向来喜欢买新工具 因为觉得这样很好
So, you know, maybe I buy a certain kind of saw, and then you know a few months later,
可能我会买一款锯子 几个月后
they’re starting to recommend me a slightly different kind of saw that serves a slightly different purpose
店家就开始推荐我另一款略有不同的锯子 它的功能也略有不同
that suddenly I definitely need to be doing and I think, uh yeah, maybe I will buy that
而且我应该会用得到 我想 我可以买
and then the end I have 10 saws and I don’t know how to use any of the saws.
最后我就会有10把锯子了 可是我一把都不会用
But you know, the retailers job is done.
但零售商的工作的确是做完了
It’s if we want to extract this data, we’re going to use machine learning or modeling
如果要提取这些数据 我们就要用机器学习或建模
to put to model this system and make predictions.
来模拟系统 作出预测
Now so for example, we could cluster the data together.
比如可以给数据做聚类
We could link my purchase history with similar people.
可以把我的购物历史和相似的人的购物历史相联系
What are they buying? Can I be tempted to buy those things as well, right?
他们买了什么?我也会被这些商品吸引吗?
Maybe I’m very different from someone else,
可能我和其他人非常不同
and so it’s not a good idea to recommend me certain products
所以给我推荐一些商品的做法并不合适
because I’m unlikely to buy those things.
因为我不会去买它们的
Perhaps use a different example. In the medical domain,
举个别的例子 医学界往往会
it’s quite common to classify people into kind of risk categories,
把人们分为不同的风险种类
so that we can maybe use preventative treatments.
以便对他们应用不同的预防治疗方案
So every time I go to a doctor, they’re going to collect data on me, on…
每次我去看病时 大夫都会收集我的数据
What’s currently on with me? And what was wrong with me before? and…
比如我最近经历了什么事?以前得过什么病?等等
Combine that with with you know standard data
将以上数据与标准数据结合
like how much exercise someone does, and you know their family history,
比如锻炼量 家庭病史
and how what their stress levels are and things like this,
压力水平 等等
We can combine all these things to make a prediction as to what they were at risk of in the future,
将这些数据结合 就能预测对方将来是否存在健康风险
so you know, heart disease or something else like this.
比如心脏病等 这能挽救一个人的生命
It could save someone’s life if you spot
如果你及时发现有人存在患某种疾病的风险
that they’re at risk of a certain thing
就能挽救对方的生命
and you can really advise that person to, you know, increase their level of exercise or alter their diet.
也可以建议对方增加锻炼或改变饮食习惯
There are two other terms that we come across, you know a lot, right?
我们还要学习另外两个知识点 你应该很清楚
So there’s data mining and big data.
是数据挖掘和大数据
Now, I’m not really sure what data mining is, because I don’t think anyone is.
我不是很清楚数据挖掘是什么 因为我觉得没人清楚
it’s a bit… it’s a bit of a buzzword
它是…它是一个流行词
Really, what data mining is is a combination of preprocessing your data
数据挖掘其实是数据预处理
and maybe using clustering to extract some knowledge from it.
和用聚类提取知识的组合
So that’s our sort of… it’s a word that’s come to be used in place of those things.
所以其实这个词是用来指代二者的
If someone says they’re doing data mining, that’s what they’re doing.
如果有人说自己在做数据挖掘 那么他做的就是上述的事情
They’re preprocessing and extracting some knowledge from their data
是数据预处理和从数据中提取知识
It’s a cool sounding word. You’re not actually “mining” anything, right?
这个词听起来很酷 但你其实不是在真的“挖”东西
You’re just doing what everyone else does on data.
你只是在做每个人都在对数据做的工作而已
Big data is the idea that maybe we collect a lot of examples of something, you know, a huge number,
大数据指的是我们收集了某事的大量样本 海量样本
or each of our examples is quite complicated and it has a lot of variables.
或每个样本都很复杂 包含大量的变量
In that case, the amount of data we’ve got is sort of unwieldy.
这么说来 我们获取到的数据量就很难处理了
So I would argue, perhaps that big data is not data that you can run on your laptop.
所以我认为 大数据不是你能在笔记本电脑上运行的数据
Like, you might be using cloud compute, infrastructure or certainly parallel processing
而是要用云计算 基础设施或是并行处理
in some way to to preprocess and analyze this data.
来预处理和分析数据
So exactly where the line, how big is “big”.
那么大数据究竟有多“大”呢?
I don’t know, but exactly where we draw the line in some ways is not really important,
我也不知道 但是究竟有多“大” 这个问题并不重要
the idea is just that the amount of data we as a species are now producing
但我们人类如今在产生的
more and more of our data is becoming big data.
越来越多的数据 逐渐构成了“大数据”
But you know exactly where the cutoff is doesn’t really matter.
但你也清楚 这个边界并不重要
What is data? I’m pretty sure that’s data.
什么是数据?我很确定那个是数据
Is this data, this picture? Or that data?
这张手机上的图是数据吗?这份杂志是数据吗?
Is this data? What is data?
这张纸是数据吗?什么是数据呢?

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视频概述

简单讲述几个数据分析相关概念,并用案例和R代码来实操。

听录译者

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翻译译者

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视频来源

https://www.youtube.com/watch?v=8GIbOJtUw8w

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