When I was a kid, I used to love playing with Lego. My brother and I built almost all kinds of stuff with Lego — animals, cars, houses, and even spaceships. As time went on, our creations became more ambitious and realistic. There were also times when we could each have insisted that our Lego was our own, till we realized that pooling resources would eventually help us went further. We were growing up too, and as our playing became more sophisticated, we learned how to make better models.

小时候，我曾经喜欢和乐高玩。 我的兄弟和我用乐高积木建造了几乎所有东西-动物，汽车，房屋，甚至宇宙飞船。 随着时间的流逝，我们的创作变得更加雄心勃勃和更加现实。 有时我们每个人都可以坚持认为乐高是我们自己的，直到我们意识到汇集资源最终将帮助我们走得更远。 我们也在成长，随着我们的演奏变得越来越复杂，我们学会了如何制作更好的模型。

As an aspiring data scientist, I realized that working with data is surprisingly a lot like my childhood Lego memories. In this article, I want to share some of the memories I’ve had that show how playing with Lego and working with data are closer than you think.

作为一个有抱负的数据科学家，我意识到处理数据非常像我童年时期的乐高记忆。 在本文中，我想分享一些我曾经经历过的记忆，这些记忆表明与Lego玩游戏和处理数据的关系比您想象的要近。

探索是该过程中最有趣的部分。 (Exploration is the most fun part of the process.)

When I was a kid, I liked to put all my Lego bricks together in a giant tub because a lot of fun in building something was searching through a sea of bricks and trying out new patterns that I didn’t think about before.

当我还是个孩子的时候，我喜欢将所有乐高积木放在一个巨大的浴缸中，因为建造东西的乐趣来自于在积木中寻找并尝试了以前从未想到的新模式。

Anyone who deals with data knows that as much as 80% of the process is cleaning up the data and doing exploratory analysis. Personally, that’s what I love about working with data — that’s where I let my creativity and imagination run wild. Jumping straight into the dataset and exploring various visualizations and correlations, in search of patterns, brings me back to a childhood spent digging through a pile of Lego.

任何处理数据的人都知道，多达80％的过程正在清理数据并进行探索性分析。 就个人而言，这就是我喜欢使用数据的原因，这是我让自己的创造力和想象力疯狂的地方。 直接进入数据集并探索各种可视化效果和相关性，以寻找模式，这使我回到了童年时花大量时间在挖掘一堆乐高玩具上的经历。

要构建有用的东西，您需要大量资源。 (To build something useful you need lots of resources.)

If you don’t have enough Lego bricks, chances are the things you’re building aren’t realistic. The model is crude, the colors don’t match, and there are gaps. The same goes for machine learning models. If you don’t have enough data, your models are poor, and you will encounter lots of errors.

如果您没有足够的乐高积木，那么您正在建造的东西可能就不现实了。 模型很粗糙，颜色不匹配，并且有空隙。 机器学习模型也是如此。 如果没有足够的数据，则模型会很差，并且会遇到很多错误。

However, sometimes, I might not have the right pieces to build a model exactly the way I wanted it, so I had to search for alternatives or reconsider how to build my Lego model. Hence, I learned a new way of using what I had. Similarly, as long as you are creative about where you look, there are always insights to be gained from even the most limited data.

但是，有时候，我可能没有合适的工具来按照我想要的方式完全构建模型，因此我不得不寻找替代方案或重新考虑如何构建Lego模型。 因此，我学到了一种使用现有物品的新方法。 同样，只要您对自己的外观具有创造力，即使是最有限的数据也总会获得洞察力。

高质量的模型需要多种资源。 (A good quality model needs a diversity of resources.)

To build a good quality Lego model, you also need a diversity of bricks. Models built with only the basic 2x4 bricks are rough and inaccurate. This is where it was so useful to get Lego from friends and family. As our family and friends gave us more Lego bricks, we got more diverse bricks that helped us create more accurate models.

要构建高质量的Lego模型，您还需要各种各样的积木。 仅使用基本2x4砖块构建的模型是粗糙且不准确的。 在这里，从朋友和家人那里获得乐高玩具非常有用。 随着我们的家人和朋友给我们提供了更多的乐高积木，我们获得了更多种类的积木，这有助于我们创建更准确的模型。

This may also be a harsh childhood truth, that the children with the most Lego, the best pieces, and the most time to play create the best models. The same harsh truth applies to any machine learning projects. Projects with the biggest data volumes, the most diverse data, and the best teams to use the data would create the most accurate models.

这也可能是一个残酷的童年真理，那就是乐高，最好的作品和最长时间玩耍的孩子会创造出最好的模特。 同样的苛刻真理适用于任何机器学习项目。 数据量最大，数据种类最多，使用数据的团队最好的项目将创建最准确的模型。

两者都需要反复思考。 (Both require iterative thinking.)

The beauty of Lego is that you’re not limited to what’s on the box. Rebuilding something and refining it each time requires iterative thinking. When it comes to working with data, there are also plenty of opportunities to iterate.

乐高的魅力在于，您不仅限于盒子上的东西。 每次重建和完善它们都需要反复思考。 在处理数据时，还存在很多迭代的机会。

When I get a “decent enough” solution, whether it’s a dashboard or a Python script, I still find time to break it, repair it, and keep improving. It may seem to get the job done at first, but I’m likely to be able to redesign it into something more effective and scalable.

当我得到一个“足够体面”的解决方案时，无论是仪表板还是Python脚本，我仍然有时间打破它，对其进行修复并不断改进。 它似乎一开始就可以完成工作，但我很可能能够将其重新设计为更有效和可扩展的功能。

随着您构建更多产品，您会变得更好。 (You get better as you build more.)

Young children make rough Lego models, the colors don’t match and the shapes are wrong. On the other hand, older children build models with careful color and shape planning.

年幼的孩子会制作粗糙的Lego模型，颜色不匹配，形状错误。 另一方面，年龄较大的孩子在构建模型时要仔细计划颜色和形状。

The same also happens with data and algorithms. As you get to know your data and algorithms, you get to understand their limitations and strive to build something better. And as the amount of data is growing, you may need to fix and adjust your models to get better and better. In other words, the same learning curve applies to Lego building and machine learning modeling.

数据和算法也是如此。 当您了解数据和算法时，您将了解它们的局限性并努力构建更好的东西。 并且，随着数据量的增长，您可能需要修复和调整模型以变得越来越好。 换句话说，相同的学习曲线适用于乐高积木和机器学习建模。

设计很重要。 (Design is important.)

The name Lego is derived from the Danish phrase ‘leg godt’, which means “play well.” Before I start building something with Lego, I will first decide if it’s something I want to display, or something I want to play with. For display-only models, I could get away with a simpler architecture, but if it was something I wanted to play with, I knew I had to make it extra robust. After all, it would be very disappointing if the wings of my spaceship fell off while I was swooshing it around the room.

乐高这个名字源自丹麦语“ leg godt” ，意思是“打得好”。 在开始使用Lego构建东西之前，我将首先决定是要显示还是要玩的东西。 对于仅用于显示的模型，我可以采用更简单的体系结构，但是如果要使用它，我知道必须使其更加坚固。 毕竟，当我在房间周围晃动时，如果我的飞船的机翼掉下来，那将是非常令人失望的。

When it comes to making a dashboard, Python script, or even a report, I often start by asking myself if this is something people will actually use (i.e. play with), or if it’s something they want to see once and never again. From there, I plan and build accordingly.

在制作仪表板，Python脚本甚至报告时，我通常会先问自己这是否是人们真正会使用(即玩弄)的东西，还是他们想一次又一次地看到的东西。 从那里，我计划并进行相应的构建。

Lego has taught me a lot about data and building models. Just like Lego:

乐高教给我很多有关数据和构建模型的知识。 就像乐高：

“To build something useful you need lots of resources, diversity, and the knowledge to build the right models in the right way.”

“要构建有用的东西，您需要大量资源，多样性和知识，以正确的方式构建正确的模型。”

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