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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:

編者按：邁向數據科學播客的“攀登數據科學階梯”系列由杰里米·哈里斯(Jeremie Harris)主持。 杰里米(Jeremie)幫助運營一家名為 SharpestMinds 的數據科學指導創業公司 。 您可以收聽以下播客：

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.

如果您有興趣升級您的編碼游戲或總體上的數據科學游戲，那么值得花一些時間來了解學習本身的過程。

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.

如果有一家公司對學習過程的研究比幾乎其他任何公司都多，那就是Codecademy。 Codecademy擁有超過6500萬用戶，對如何使人們學習編碼有深入的了解，這就是為什么我想與他們的數據科學負責人Cat Zhou談談本播客的這一集。

Here were some of my favourite take-homes:

以下是一些我最喜歡的地方：

• 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.
  培養具有不同教育背景的團隊具有很多價值。 CS專業，經濟學家，商務人士和頑強的貝葉斯主義者都注意到數據方面的各種機會，而學習如何使這些團隊一起工作對于管理數據科學工作至關重要。
• 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.
  根據Codecademy的數據，對內容進行暴飲暴食并在短時間內進行編碼的人們從長遠來看往往不會保持其編碼習慣。 因此，編碼活動的沖刺可能并不是最好的方法，因為它們的作用與填塞測試的作用相同。 關鍵是找到可持續參與的“最佳位置”，您需要確保編碼成為一種長期習慣。
• 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.
  隨著數據科學越來越受到重視，數據團隊與產品團隊的集成越來越緊密，而產品團隊則依靠它們來幫助指導新功能的開發。 因此，數據科學家需要發展良好的產品直覺，以便能夠與依賴它們的產品經理，設計人員和開發人員進行溝通，以全面了解用戶行為。

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

您也可以按照貓在Twitter這里跟上她的工作，而我在這里 。