20+ Resources I Use to Stay Up-to-Date with Data Science
在数据科学领域，情况非常迅速。仅在我的职业生涯（过去〜4年）中，我不得不学习几个新的，行业领先先知，DBT和DASH等工具。
成为数据科学家意味着您必须不断学习新技能和工具，以扩大您的工具武器库，并跟上行业领先的平台。
在本文中，我想与您分享我用来与数据科学保持最新的所有资源和隐藏的宝石！话虽如此，让我们深入研究。
Be sure to在这里订阅to never miss another article on data science guides, tricks and tips, life lessons, and more!
There are six main ways that I keep up with data science, and all of them help me keep up with data science in different ways:
- 数据科学平台
- 学习平台
- 教科书
- LinkedIn影响者
- YouTube
- Conferences
数据科学平台
There are a few data science-specific platforms that I find really valuable, and were actually a huge contribution to my development.
当我想要有关如何执行与数据相关的任务的分步指南，无论是建立时间序列模型还是使用DBSCAN解决聚类问题时，Analytics Vidhya和Kaggle都特别有用。
What’s nice about both is that they both have their own communities where you can ask questions and learn from other people’s code.
How I use Data Science Platforms to Learn
我有几种方法使用数据科学平台（土星云,分析Vidhya,Kaggle） 学习：
- 当我想查看特定主题的示例代码时，我会查看这些资源。例如，当我想知道如何实现K折叠验证时，我使用这些资源来查看其他人如何实施它。
- I also use these platforms to learn new tricks and methods that other programmers use that I find valuable.
- 最后，这些平台有大型社区，您可以利用这些社区来提出问题并向他人学习！
我最喜欢的资源
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学习平台
I’m not a huge fan of bootcamps, but there are a couple of websites that I find to be very strong, Vexpower and Coursera. These two are great if you want to step-by-step guides to learn technical topics. For example, Vexpower has a course that teaches you how to build a Marketing Mix Model step-by-step using Robyn.
当我不需要以书籍的方式学习整个主题时，我发现这些非常有帮助。
我如何使用学习平台学习
If you think of a spectrum, where textbooks cover broad subjects and Stack Overflow covers very specific questions, learning platforms lie somewhere in the middle.
When I have a foundation in a particular subject but I want to extend my knowledge or dive a little deeper on a topic, that’s when I like to use learning platforms. For example, when I was focused on Marketing Analytics, I used Vexpower to learn how to build a Marketing Mix Model using Robyn.
我最喜欢的资源
教科书
虽然并非总是最简单或最快的消化，但我发现阅读书籍是出于某些原因学习新主题的最佳方法。
- Authors put thought into the chronology of what information should be shared first. This avoids the situation where you’re learning a topic and have to recursively learn prerequisite topics/terms to understand the current topic.
- 书籍通常比博客或文章更深入，这在您试图为您要学习的特定主题建立强大的基础时很有用。
- From a technical perspective, I like textbooks because they go through how equations are derived and provide examples as to how to use different equations.
How I use Textbooks to Learn
When I’m trying to learn a relatively new subject, like experimental design, and I have little foundation on the subject, I tend to look for textbooks. Similar to school courses, I feel that textbooks are a good way to learn the basics of a topic relatively quickly.
Whether it be experimental design, deep learning, network analysis, or machine learning, I personally recommend starting with textbooks if you’re net new to the subject.
这是我喜欢的一些教科书！
LinkedIn影响者
LinkedIn是学习新事物的最被低估的来源之一。这是我学习大量新信息和资源的地方。有大量的数据科学影响者花时间分享非常有见地的编码技巧并策划极为宝贵的资源。
In the article below, you can see my personal top data science influencers.
我如何从LinkedIn影响者那里学习
The beauty of this is that you can learn just from scrolling your LinkedIn home page after following several Data Science Influencers. You’ll see a lot of curated content and knowledge sharing on a weekly, or even daily, basis.
请参阅我的顶级数据科学影响者：
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YouTube
如果您是音频或视觉学习者，YouTube很棒。我喜欢YouTube，因为我可以很快了解小概念，而且我也可以学习整个主题。
我个人对YouTube的热爱是，您可以从一些最好的大学中学到很多免费的讲座（麻省理工学院，斯坦福，哈佛等），这是一个很快学习整个学科的好方法。
一些链接开始：
Conferences
我喜欢会议的是，它使您有机会聆听世界上一些最明显的数据领导者。通常，会议是公司和领导者分享与数据科学和AI相关的新思想和新实践的地方。
Below are some of the most popular annual data science conferences:
查看一些最大的会议：
谢谢阅读！
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Not sure what to read next? I’ve picked another article for you:
还有另一个：
特伦斯·辛（Terence Shin）
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