数据科学学习心得_学习数据科学

数据科学学习心得

苹果 | GOOGLE | 现货 | 其他 (APPLE | GOOGLE | SPOTIFY | OTHERS)

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

编者按：迈向数据科学播客的“攀登数据科学阶梯”系列由杰里米·哈里斯(Jeremie Harris)主持。 杰里米(Jeremie)帮助运营一家名为 SharpestMinds 的数据科学指导创业公司 。 您可以收听以下播客：

演示地址

If you’re interested in upping your coding game, or your data science game in general, then it’s worth taking some time to understand the process of learning itself.

如果您有兴趣升级您的编码游戏或总体上的数据科学游戏，那么值得花一些时间来了解学习本身的过程。

And if there’s one company that’s studied the learning process more than almost anyone else, it’s Codecademy. With over 65 million users, Codecademy has developed a deep understanding of what it takes to get people to learn how to code, which is why I wanted to speak to their Head of Data Science, Cat Zhou, for this episode of the podcast.

如果有一家公司对学习过程的研究比几乎其他任何公司都多，那就是Codecademy。 Codecademy拥有超过6500万用户，对如何使人们学习编码有深入的了解，这就是为什么我想与他们的数据科学负责人Cat Zhou谈谈本播客的这一集。

Here were some of my favourite take-homes:

以下是一些我最喜欢的地方：

• There’s a lot of value in cultivating teams with different educational backgrounds. CS majors, economists, business people and die-hard Bayesians all notice different kinds of opportunities in data, and learning how to get these teams to work together is key to managing a data science effort.
  培养具有不同教育背景的团队具有很多价值。 CS专业，经济学家，商务人士和顽强的贝叶斯主义者都注意到数据方面的各种机会，而学习如何使这些团队一起工作对于管理数据科学工作至关重要。
• People who binge content, and code a whole bunch during a short period of time don’t tend to maintain their coding habit in the long run, according to Codecademy’s data. So spurts of coding activity probably aren’t the best way to go, because they have the same effect as cramming for a test. The key is to find the “sweet spot” of sustainable engagement you need to ensure that coding becomes a long-lasting habit.
  根据Codecademy的数据，对内容进行暴饮暴食并在短时间内进行编码的人们从长远来看往往不会保持其编码习惯。 因此，编码活动的冲刺可能并不是最好的方法，因为它们的作用与填塞测试的作用相同。 关键是找到可持续参与的“最佳位置”，您需要确保编码成为一种长期习惯。
• As data science is being taken more and more seriously, data teams are integrating more closely with product teams, which have come to rely on them to help guide the development of new features. As a result, data scientists need to develop good product instincts to be able to communicate with the product managers, designers and developers who depend on them to get a complete picture of user behavior.
  随着数据科学越来越受到重视，数据团队与产品团队的集成越来越紧密，而产品团队则依靠它们来帮助指导新功能的开发。 因此，数据科学家需要发展良好的产品直觉，以便能够与依赖它们的产品经理，设计人员和开发人员进行沟通，以全面了解用户行为。

You can also follow Cat on Twitter here to keep up with her work, and me here.

您也可以按照猫在Twitter这里跟上她的工作，而我在这里 。

翻译自: https://towardsdatascience.com/the-data-science-of-learning-e8a5a960f746

数据科学学习心得