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Now you have a gap in your data and yet you still have some magnetic particles there that could be useful.
现在的数据中可能会有一些空白,但你仍然有一些,有用的磁性粒子。
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He introduced the concept of electronegativity which was a measure, therefore, it is quantitative, of the atom's ability to attract electrons within a covalent bond and developed a scale of electronegativity.
他引入了电负性这一概念,一个反映着原子在成共价键时吸引电子的能力的数据标度，因此这是数量上的，在共价电子中,电子原子能吸引电子,并发展为一定规模的电负性。
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In fact to write a run those same experience-- those same experiments now with the even greater volume of data computers are now producing I'd probably have terabytes worth of data and at that point things just would have broken.
事实上-,目前使用大量数据的相同实验中,计算机会产生,兆兆字节的数据,那时，一切都有可能遭到破坏。
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So the Priestly materials are found as a block in Leviticus, a large part of Numbers, and then they're scattered throughout Genesis and Exodus.
在《利未记》中，与祭司相关的内容占了很大一块，有很多的数据，它们都将贯穿在《创世纪》和《出埃及记》中。
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So in fact, it is the case, if you look at data, and by the way, that's the way I ended up setting a lot of these parameters and playing with it, was comparing what my simulation said to historical stock data.
所以实际上，在这个例子中，如果你看到数据，顺便说一下，这就是我设置很多变量,然后编程的方式，也就是把我的仿真程序得到的结果,和历史股票数据进行比较。
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In our--in this particular study we had some people estimate things low and some people estimate things high, but overall, this--these would represent overestimates.
在这个实验中,有些人低估了,而有些人高估了数据,但总体来说,结果显示人们高估了数据
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It's the various programs you run on your computer along with the various data files that you have saved on your hard drive,and so forth.
是你自身这个电脑中运行的各种程序,硬盘存有的各种数据和文件，类似种种。
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The only history that you would have had fifteen years ago would have been those funds that produced great returns, so it's all identified after the fact.
你只会看到在这15年的历史数据中,所有对冲基金都有着极佳的回报,这都是事后调整确认的
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So I wrote a little program called "chart" that, based on some data I happened to hardcode into the program, shows us their numbers for the past several years.
我写了一个叫做“图表“的小程序，基于我在程序中硬编码的一些数据，向我们显示在过去几年中我们课程的人数。
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It imports it into our database and does some fancy indexing as we'll call it later in the term to make searches more efficient.
将它导入到我们的数据库中，并进行一些变址,这将在后期会提到,它可以使查找更高效。
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The good news is the elements in any one spot in a hash table are likely to be a small number, three, four, five.
好的一点是在哈希表中,任何一个存贮点上的元素数据是很小的,三个，四个，五个。
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You're searching three or four things, but nonetheless, you have to do that trade off.
你只是在三个或四个数据中进行查找，但是即使如此，你也必须做一些权衡。
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How do you get data off the hard disk?
那怎样从磁盘中得到数据？
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In the case of Burt Malkiel's data, more than 11% per year and in the case of Roger Ibbotson's data between 7% and 8% per year of those returns can be explained either by backfill bias or survivorship bias.
在伯特·麦基尔的数据中,超过11%的年平均收益,在罗杰·伊博森的数据中,7%到8%的年平均收益,可以用生存偏差或回填偏差来解释
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Your data is actually stored on these platters.
你的数据实际上存储在这些磁盘中。
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And then you can look at food disappearance data and that's sort of a connection with the food production data: not so much how much is produced but how much of it is disappearing from the--the food supply.
然后看看食物消耗的记录,会与食物生产的数据,有某种联系,与其看在食品供应中生产了多少,倒不如关注被消费了多少
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x >> Okay. I'll dereference the pointer x 42 to store the number 42 into its pointee.
>，好的，我废除这个指针,在指针数据中存储数字。
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This is--I'm showing here U.S. data, but Siegel also argues in the latest edition that the equity premium is also high for advanced countries over the whole world.
这就是。。。我在这展示的是美国的数据，但是西格尔在他最新出版的书中讨论道,在世界范围内许多发达国家的,股票溢价同样很高。
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You try to design actually I'm going to come back to that in a second. It's like you're trying to use a hash function that spread things out pretty evenly. But the places you store into in those lists may have to themselves have a small list in there, and when you go to check something, you may have to do a linear search through the elements in that list.
你尝试着去设计,实际上过会儿我会回头讲解这个问题,类似于你需要用一个哈希函数,非常平均的将物体分发出去,但是在列表中你数据,映射到的地方可能会有自己的一小段列表，当你回头查找数据的时候,你可能需要在那一小段列表中做线性查找。
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And it's a topic I want to pick up on today, we're going to do for the next few lectures, and it's a topic I want to spend some time on because this idea of capturing data and methods, the term we're going to use for it, but data and functions that belong to that data, things that can be used to manipulate them, is a really powerful one.
我们会在今天讲这个话题,在后面几节课还会讲这个,这是个我愿意花点时间，在上面的话题因为这是个,捕获数据和方法的思想,我们用这个术语来形容它，数据中包括了数据和函数等，能用来操作它们的东西,真的是很强大的工具，我们真正要讲的东西是，我们要学会如何把信息。
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In the case of energy expenditure, how many calories people were burning, there wasn't quite such a difference, but again, it was in the favor of the individuals, that the people were estimating they burned this many calories from physical activity, but this is actually the number that they burned.
就能量消耗来说,人们消耗了多少卡路里,两者之间的差异并不是如此巨大,但又一次,由于人们的一厢情愿,人们估算从体育活动中,他们消耗了这么多的卡路里,但数据是他们实际消耗的
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I don't find that my analysis is profound in the final answer, I just took some estimates using my data and, again, we could-- if someone wanted to argue with us they could argue with my estimates of the expected returns of the standard deviations and the covariances, but not with this theory.
我在计算过程中并没有做太深入的分析，我只是用我的数据做了一下大概的估计,我再说一次，我们可以-,如果有人想就这个问题与我们争辩,他们可以争论我对期望收益的估计,或是争论标准差和协方差的估计值，但并不会针对理论本身。
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Well, a cross-sectional study would mean that you'd get a random group of the population and measure them in 1970 and then take another random group five years later and measure them, but it wouldn't be the same people necessarily, it would be different groups of people.
那么,横断面研究就是,1970年你在人群中随意选择一组人,并记录下相关数据,然后,五年后再随意选择一组人记录数据,但这不需要是同一组人,通常是不同的人
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When you're working on a file, it's saving constantly to RAM but also hopefully to disc, the hard disk so you don't actually lose it if the power goes off.
当你打开一个文件，它不断地保存在内存中,但对于光盘，硬盘,当你断开电源的时候，数据不会丢失。
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Okay, so when you are working with source material that looks like this, there is in fact no such thing as infinite zoom in computer science.
当我们处理,这样的原数据时，事实上计算机科学中,并不存在无限放大。
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You can take a problem that might be relatively intuitive to solve but when you scale this thing up as is increasingly the case in the web, in large data systems, and so forth, you actually have to now think smart, you actually have to think efficiently and you have to solve this problem effectively.
你可以把一个问题用比较直观的方法解决,但如果你把此类问题的数量增大,正如越来越多的互联网，和大规模数据系统中出现的问题等等,你应该考虑怎样才能更简便，怎样才能更高效,你应该用行之有效的方法处理问题。
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It's going to be stuck in a data structure.
或者其他数据结果中。
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You're not searching a million things.
你不是在一百万个数据中进行查找。
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It's in a file called swap.C. Everything should be alphabetical so it should be toward the end of this week's packet and notice that it's almost identical except for a couple of syntax changes.
在一个叫做swap。c的文件中，对于这周末的数据包来说,所有的东西都是字母,请注意,除了几个语法改变,它几乎是一样的。
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Associated with every data type in Python is a type, which identifies the kind of thing it is.
在Python中每种类型都对应着一种数据类型，类型确定了对象的种类。
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