為什么要書籍？ (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.

互聯網是有關各種主題的信息寶庫。 無論您是想通過Youtube視頻學習吉他，還是想在路邊被困時如何換輪胎，互聯網都使我們比以往任何時候都更快，更輕松地學習技能。

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.

我大力支持使用互聯網來學習和提高您的數據分析技能。 個人博客，Youtube和我最喜歡的網站上都有大量資源：邁向數據科學！ 但是，我發現書籍仍然是學習這些技能的極其有用的媒介。

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.

在線資源是零散的-由不同的作者撰寫，期望各個級別的先前經驗，并且兩者之間存在細微差異。 當您首次嘗試學習分析時，這可能使在這些資源之間建立連接變得困難。 這就是為什么我認為書籍是您的教育中可以使用的大量額外資源的原因。

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歡的三本書的清單，我認為它們為數據分析奠定了良好的基礎。 雖然此列表絕非詳盡無遺，但我鼓勵您看看！

對于那些知道如何編碼的人： (For Those Who Know How to Code:)

用于數據分析的Python：使用Pandas，NumPy和IPython處理數據 (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撰寫的Python for Data Analysis是一本很棒的書，適合那些對使用Python作為首選工具感興趣的人。 Python是用于數據建模，分析和預測的極其強大且靈活的工具。

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等軟件包的幫助下，python是學習作為數據科學家所需的工具的絕佳環境。 此外，許多公司在其工作流程中都使用python，甚至可以在生產環境中使用它。

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

這本書非常密集，但是包含很多有用的信息，并且可以在以后的幾年中用作參考。

對于那些了解入門統計信息的人： (For Those Who Know Introductory Statistics:)

應用預測建模 (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.

“應用預測建模”的封面可能看起來并不令人興奮-但您知道他們在說什么：“不要憑封面判斷一本書。” 本書假定您的統計基礎很小，并且處于統計學入門級水平之上。

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，不要擔心本書的統計性質。 應用預測建模包含啟發式的寶庫和各種實際項目的技巧。 除了學習有價值的算法和工具外，這本書還解釋了為什么要做出特定的決策以及如何自己做出決策。 作者還提供了使用凌亂和真實數據的各種實際示例，并解釋了做出了哪些決策以及為什么做出了決策。

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

如果您想深入研究實際場景中的預測分析，這本書是您可以獲取的。

對于那些在家中使用電子表格的人： (For Those That Feel At Home In Spreadsheets:)

數據智能：使用數據科學將信息轉化為洞察力 (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.

開始數據科學之旅可能會讓人感到恐懼和壓倒性。 數據科學家分析師不僅如此，而且他們通常還包括程序員，演示者和數據庫管理員。 但是，如果您不想這么做，則無需先深入研究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為那些剛接觸編程和數據科學但希望提供價值的人提供了良好的基礎。 如果您對電子表格應用程序(例如Excel)不滿意(并希望暫時保持這種狀態)，則這本書非常適合您。

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.

您可能無法在電子表格中創建可用于生產的復雜模型，但是可以從這些程序中獲得許多有價值的見解，并且可以學習為組織提供重要價值。

不要在這里停下來 (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.

書籍是學習新技能的絕佳資源。 無論您的背景或目標如何，都有適合您的書。 但是，盡管我吹噓書籍的偉大之處，但不要讓它們成為您唯一的資源。

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!

觀看youtube視頻，與其他數據科學家聯系，參加培訓或課程，當然還要閱讀博客和出版物，例如“邁向數據科學”。 最重要的是，永不停止學習！