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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:

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

We’ve talked a lot about “full stack” data science on the podcast. To many, going full-stack is one of those long-term goals that we never get to. There are just too many algorithms and data structures and programming languages to know, and not enough time to figure out software engineering best practices around deployment and building app front-ends.

我们在播客上谈论了很多“全栈”数据科学。 对许多人来说，全力以赴是我们从未实现的那些长期目标之一。 要了解的算法，数据结构和编程语言太多了，没有足够的时间来了解围绕部署和构建应用程序前端的软件工程最佳实践。

Fortunately, a new wave of data science tooling is now making full-stack data science much more accessible by allowing people with no software engineering background to build data apps quickly and easily. And arguably no company has had such explosive success at building this kind of tooling than Streamlit, which is why I wanted to sit down with Streamlit founder Adrien Treuille and gamification expert Tim Conkling to talk about their journey, and the importance of building flexible, full-stack data science apps.

幸运的是，新一轮的数据科学工具正在使无软件工程背景的人们快速，轻松地构建数据应用程序，从而使全栈数据科学的访问更加方便。 可以说，没有任何一家公司比Streamlit在开发这种工具方面取得如此爆炸性的成功，这就是为什么我想和Streamlit的创始人Adrien Treuille和游戏化专家Tim Conkling坐下来谈论他们的旅程，以及建立灵活，完整的重要性。堆栈数据科学应用程序。

Here were some of my favourite take-homes from the conversation:

以下是我最喜欢的对话内容：

• The problem Streamlit is taking on is fundamentally to reduce the learning curve associated with deployment and app building. Whereas tools like AWS, Flask, Heroku or Django take a while to master before they can be used to make something compelling, Streamlit has its users build real products within minutes by obsessively gamifying the onboarding process.Streamlit所面临的问题从根本上减少了与部署和应用构建相关的学习曲线。 像AWS，Flask，Heroku或Django之类的工具需要花一些时间才能掌握，然后才能使它们引人注目，而Streamlit却通过迷恋游戏流程来让用户在几分钟内构建出真正的产品。

• One of the reasons it’s so important to build full-stack apps is accessibility. Most people don’t know how to work with Jupyter notebooks (and notebooks are also stateful, so it’s harder to ensure that the notebook you pass along will be used the way you intend for it to be!). Having a live app that people can use without spinning up a server themselves or learning how to code makes it much more likely that data scientists can share insights and persuade key decision makers to act on them.

  构建全栈应用程序如此重要的原因之一是可访问性。 大多数人不知道如何使用Jupyter笔记本电脑(而且笔记本电脑也是有状态的，因此很难确保您通过的笔记本电脑会按预期使用！)。 拥有一个人们可以使用的实时应用程序，而无需自己旋转服务器或学习如何编写代码，这使得数据科学家更有可能分享见解并说服关键决策者对其采取行动。

• Reducing the overhead associated with app deployment is really important because it allows you, the data scientist, to experiment more both with algorithms and visualizations, because it doesn’t take a ton of time to redeploy systems and track down server-side bugs. So even if you already have software engineering skills, tools that abstract away the software engineering layer of your data science problem are likely to make you work faster and improve the quality of your work product.减少与应用程序部署相关的开销确实很重要，因为它使您(数据科学家)可以对算法和可视化进行更多试验，因为重新部署系统和查找服务器端错误无需花费大量时间。 因此，即使您已经具有软件工程技能，抽象化数据科学问题的软件工程层的工具也可能使您的工作速度更快并提高工作产品的质量。

You can also follow Streamlit on Twitter here, and you can follow me on Twitter here.

您也可以跟踪Streamlit在Twitter这里，你可以按照我的Twitter这里。