为什么要书籍？ (Why Books?)

The internet is a treasure-trove of information on a variety of topics. Whether you want to learn guitar through Youtube videos or how to change a tire when you are stuck on the side of the road, the internet allows us to learn skills faster and easier than ever before.

互联网是有关各种主题的信息宝库。 无论您是想通过Youtube视频学习吉他，还是想在路边被困时如何换轮胎，互联网都使我们比以往任何时候都更快，更轻松地学习技能。

I am a big supporter of using the internet to learn and improve your data analytics skills. There are loads of resources on personal blogs, Youtube, and my favorite site: Towards Data Science! However, I find that books are still an extremely useful medium for learning these skills.

我大力支持使用互联网来学习和提高您的数据分析技能。 个人博客，Youtube和我最喜欢的网站上都有大量资源：迈向数据科学！ 但是，我发现书籍仍然是学习这些技能的极其有用的媒介。

Online resources are fragmented — written from different authors, expecting various levels of previous experience, and contain slight differences between them. This can make it difficult to make connections between these resources when you are first trying to learn analytics. That is why I think books are a great additional resource to use in your education.

在线资源是零散的-由不同的作者撰写，期望各个级别的先前经验，并且两者之间存在细微差异。 当您首次尝试学习分析时，这可能使在这些资源之间建立连接变得困难。 这就是为什么我认为书籍是您的教育中可以使用的大量额外资源的原因。

I have compiled a list of three of my favorite books that I think provide a great foundation in data analytics. While this list is by no means exhaustive, I encourage you to take a look!

我整理了一份我最喜欢的三本书的清单，我认为它们为数据分析奠定了良好的基础。 虽然此列表绝非详尽无遗，但我鼓励您看看！

对于那些知道如何编码的人： (For Those Who Know How to Code:)

用于数据分析的Python：使用Pandas，NumPy和IPython处理数据 (Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython)

Python for Data Analysis by Wes McKinney is a great book for those who are interested in using Python as their tool of choice. Python is an extremely powerful and flexible tool for data modeling, analysis, and prediction.

Wes McKinney撰写的Python for Data Analysis是一本很棒的书，适合那些对使用Python作为首选工具感兴趣的人。 Python是用于数据建模，分析和预测的极其强大且灵活的工具。

With the help of packages such as Pandas and Numpy, python is a great environment to learn the tools necessary to work as a data scientist. In addition, many companies use python in their workflow and it can be even used in production environments.

在Pandas和Numpy等软件包的帮助下，python是学习作为数据科学家所需的工具的绝佳环境。 此外，许多公司在其工作流程中都使用python，甚至可以在生产环境中使用它。

This book is very dense but packed with lots of good information and can be used as a reference for years to come.

这本书非常密集，但是包含很多有用的信息，并且可以在以后的几年中用作参考。

对于那些了解入门统计信息的人： (For Those Who Know Introductory Statistics:)

应用预测建模 (Applied Predictive Modeling)

The cover of Applied Predictive Modeling may not look exciting — but you know what they say: “Don’t judge a book by its cover.” This book assumes you have a small statistics foundation and sits comfortably above the level of an introductory statistics course.

“应用预测建模”的封面可能看起来并不令人兴奋-但您知道他们在说什么：“不要凭封面判断一本书。” 本书假定您的统计基础很小，并且处于统计学入门级水平之上。

Don’t be afraid by this book's statistic nature, however. Applied Predictive Modeling contains treasure troves of heuristics and tips for various real-world projects. In addition to learning valuable algorithms and tools, the book explains why specific decisions were made and how to make them yourself. The authors also provide various real-world examples using messy and real data and explain what decisions were made and why.

但是，不要担心本书的统计性质。 应用预测建模包含启发式的宝库和各种实际项目的技巧。 除了学习有价值的算法和工具外，这本书还解释了为什么要做出特定的决策以及如何自己做出决策。 作者还提供了使用凌乱和真实数据的各种实际示例，并解释了做出了哪些决策以及为什么做出了决策。

If you wish to dig into predictive analytics in real-world scenarios, this is the book to get.

如果您想深入研究实际场景中的预测分析，这本书是您可以获取的。

对于那些在家中使用电子表格的人： (For Those That Feel At Home In Spreadsheets:)

数据智能：使用数据科学将信息转化为洞察力 (Data Smart: Using Data Science to Transform Information into Insight)

Starting your data science journey can be scary and overwhelming. Not only are data scientists analysts, but they oftentimes also programmers, presenters, and database administrators among other things. However, you don’t need to dive headfirst into Python or R if you don’t want to.

开始数据科学之旅可能会让人感到恐惧和压倒性。 数据科学家分析师不仅如此，而且他们通常还包括程序员，演示者和数据库管理员。 但是，如果您不想这么做，则无需先深入研究Python或R。

Data-Smart provides a great foundation for those that are new to programming and data science but want to provide value. If you are semi-comfortable in a spreadsheet application such as Excel (and want to stay that way for now) this book is great for you.

Data-Smart为那些刚接触编程和数据科学但希望提供价值的人提供了良好的基础。 如果您对电子表格应用程序(例如Excel)不满意(并希望暂时保持这种状态)，则这本书非常适合您。

You may not be able to create complex models ready for production in a spreadsheet, but lots of valuable insights can be gained from these programs and you can learn to provide serious value to your organization.

您可能无法在电子表格中创建可用于生产的复杂模型，但是可以从这些程序中获得许多有价值的见解，并且可以学习为组织提供重要价值。

不要在这里停下来 (Don’t Stop Here)

Books are an amazing resource for learning new skills. No matter your background or goals, there is a book out there for you. However, while I tout the greatness of books, don’t let them be your only resource.

书籍是学习新技能的绝佳资源。 无论您的背景或目标如何，都有适合您的书。 但是，尽管我吹嘘书籍的伟大之处，但不要让它们成为您唯一的资源。

Watch youtube videos, connect with other data scientists, take training or classes, and of course read blogs and publications such as Towards Data Science. And most importantly, never stop learning!

观看youtube视频，与其他数据科学家联系，参加培训或课程，当然还要阅读博客和出版物，例如“迈向数据科学”。 最重要的是，永不停止学习！