Data science train is moving, at a constantly accelerating speed, and increasing its length by adding up new coaches. Businesses want to be on the data science train to keep up with the ever-evolving technology and improve their operations. Thus, there is a huge pool of jobs in the field of data science. More and more people want to get on the train and start working in this field because:

數據科學培訓正在以不斷加速的速度前進，并通過增加新的教練來增加其長度。 企業希望參加數據科學培訓，以跟上不斷發展的技術并改善其運營。 因此，在數據科學領域有大量的工作機會。 越來越多的人希望上火車并開始在這個領域中工作，因為：

1. The jobs are exiting and fun
   工作正在退出并且很有趣
2. The jobs are well-payed
   這些工作是高薪的
3. The demand will not decrease in the foreseeable future
   在可預見的將來需求不會減少

It is like a chain reaction. Businesses adopting data science create jobs that drive people to work in this field. These people need to be educated which motivates some other people to create learning resources. In this post, we will focus on the “learning resources” part of the story.

這就像一個連鎖React。 采用數據科學的企業創造了推動人們從事該領域工作的工作。 這些人需要接受教育，以激勵其他人創造學習資源。 在本文中，我們將重點介紹故事的“學習資源”部分。

The amount of resources to learn data science is overwhelming. There are two main reasons that cause this situation:

學習數據科學的資源數量巨大。 導致這種情況的主要原因有兩個：

1. Data science is such a broad field that is kind of a mixture of math, statistics, and programming. Thus, there is so much to learn.
   數據科學是一個廣泛的領域，混合了數學，統計和編程。 因此，有很多東西要學習。
2. People tend to prefer more flexible, faster, and cheaper learning paths over traditional education. Thus, there is a variety of MOOC courses, youtube videos, blogs, and bootcamps that teach data science.
   人們傾向于比傳統教育更靈活，更快和更便宜的學習途徑。 因此，有許多MOOC課程，YouTube視頻，博客和訓練營來教授數據科學。

There is so much to learn on so many different platforms. This can be an advantage or disadvantage depending on how we handle it.

在這么多不同的平臺上有很多東西要學習。 根據我們的處理方式，這可能是優點還是缺點。

When I started learning data science, I had some questions that were demotivating me. I would like to list some of those questions here:

當我開始學習數據科學時，我遇到了一些困擾我的問題。 我想在這里列出一些問題：

• Should I learn Python or R? (I did not have any prior programming experience)
  我應該學習Python還是R？ (我之前沒有任何編程經驗)
• Do I need a masters degree or just a few certificates?
  我需要碩士學位還是只需要幾個證書？
• How much statistics do I need to learn?
  我需要學習多少統計數據？
• I have a BS in engineering so I have enough math knowledge but “how much math do I need to learn” would be an important question for people with non-technical backgrounds.
  我擁有工程學學士學位，所以我擁有足夠的數學知識，但是“對于非技術背景的人來說，“我需要學習多少數學”將是一個重要問題。
• TensorFlow or PyTorch?
  TensorFlow還是PyTorch？
• Should I learn natural language processing (NLP) techniques?
  我應該學習自然語言處理(NLP)技術嗎？
• How about time series analysis?
  時間序列分析怎么樣？
• What should I learn for data visualization? Matplotlib, Seaborn, Plotly or some other?
  對于數據可視化我應該學什么？ Matplotlib，Seaborn，Plotly還是其他？
• NumPy and Pandas enough for data analysis?
  NumPy和Pandas是否足以進行數據分析？

And there are some more questions. You might have similar questions and hesitate to start. I don’t have answers to those questions. But my suggestion is to stop wasting your time looking for answers.

還有更多問題。 您可能有類似的問題，開始猶豫。 我沒有這些問題的答案。 但是我的建議是不要再浪費時間尋找答案。

Just start learning!

剛開始學習！

Once you start and take the first steps, you will discover some of the answers. You will also see that there is not a clear answer to some questions. However, this should not stop your learning process.

一旦開始并采取第一步，您將發現一些答案。 您還將看到對某些問題沒有明確的答案。 但是，這不應阻止您的學習過程。

Another very important thing to keep in mind is that you cannot just learn everything. For instance, NLP is an entire field by itself and requires in-depth training and practice. If you want to specialize in NLP, you may focus more on NLP-specific tools and frameworks.

要記住的另一個非常重要的事情是， 您不能僅僅學習所有內容 。 例如，自然語言處理本身就是一個完整的領域，需要深入的培訓和實踐。 如果您想專門研究NLP，則可以將重點放在特定于NLP的工具和框架上。

There is always more than one option!

總是有不止一種選擇！

When you start leaning towards a specific subfield of data science, some tools and frameworks become prominent but we usually have more than one option. For instance, R might be a better fit for statistical analysis than Python. However, Python also has powerful third-party statistical packages such as statsmodels. I’m not trying to cause any more contradictions. I just want to point out that there are many options to learn data science.

當您開始著眼于數據科學的特定子領域時，一些工具和框架會變得很突出，但是我們通常有多個選擇。 例如，R可能比Python更適合統計分析。 但是，Python還具有強大的第三方統計軟件包，例如statsmodels 。 我不是要引起更多的矛盾。 我只想指出，學習數據科學有很多選擇。

I also want to mention different types of resources. It is actually good to have an overwhelming amount of resources. We have the freedom to choose from different options. There are videos on youtube about almost any topic related to data science. ArXiv contains a gigantic collection of scholarly articles on data science. Numerous platforms offer data science certificates such as Coursera, Udemy, and edX. And, of course, blogs are extremely efficient to learn specific topics. For instance, we can find an article on almost any topic on Medium.

我還想提到不同類型的資源。 擁有大量資源實際上是一件好事。 我們可以自由選擇不同的選項。 youtube上有幾乎與數據科學相關的所有主題的視頻。 ArXiv包含大量關于數據科學的學術文章。 許多平臺都提供數據科學證書，例如Coursera，Udemy和edX。 而且，當然，博客對于學習特定主題非常有效。 例如，我們可以找到有關Medium幾乎所有主題的文章。

I started by completing a certificate, IBM Data Science Specialization. It was very helpful in the sense that the topics were organized and structured. It also provides a general overview of the field of data science. I suggest starting with a basic and comprehensive resource like that one. Then you will easily build your own learning path. You don’t have to collect lots of certificates on any topic.

我首先完成了IBM Data Science Specialization證書。 就主題的組織和結構而言，這非常有幫助。 它還提供了數據科學領域的一般概述。 我建議從這樣的基礎和綜合資源入手。 然后，您將輕松建立自己的學習路徑。 您無需就任何主題收集大量證書。

Last but not least, maybe the most important one, is doing projects. They are what get you ready for the job. Projects consolidate different skills into one. They also serve as a showcase to display your skills.

最后但并非最不重要的一點是，也許最重要的是做項目。 它們使您為工作做好準備。 項目將不同的技能整合為一個。 它們還充當展示您技能的展示柜。

Do projects after the basics are covered.

在介紹了基礎知識之后再做項目。

Data science has lots of applications in different industries. The goal of businesses is to create value out of data. Thus, learning the algorithms or tools to analyze data is not enough to land a job. You should start doing projects related to the area you want to work in. Doing projects will not only help you obtain a more comprehensive knowledge but also bring more optimal tools and frameworks to your plate. Depending on the project, certain tools will outperform others and better fit to your style. Here is a list of my 5 reasons to do projects:

數據科學在不同行業中有許多應用。 企業的目標是從數據中創造價值。 因此，學習算法或工具來分析數據還不足以找到工作。 您應該開始進行與您要從事的領域相關的項目。進行項目不僅可以幫助您獲得更全面的知識，而且可以為您的印版帶來更多最佳的工具和框架。 根據項目的不同，某些工具的性能將優于其他工具，并且更適合您的風格。 這是我做項目的5個理由的清單：

To sum up, it doesn’t really matter how you learn. If you are passionate about learning data science, the path you follow does not make a difference. Whatever fits your learning style will do the job. The most important thing is to start your journey and, of course, do projects after you cover the basics.

綜上所述，學習方式并不重要。 如果您熱衷于學習數據科學，那么您所遵循的道路不會改變。 一切適合您的學習風格都可以勝任。 最重要的是開始您的旅程 ，當然， 在您介紹了基礎知識之后再做項目。

Thank you for reading. Please let me know if you have any feedback.

感謝您的閱讀。 如果您有任何反饋意見，請告訴我。