一個數(shù)據(jù)包的旅程

With coming fast tech industry changes and robotic innovations, Data Science is one of the great niches to start learning. If you are passionate about data and how to use it, this article will help you to understand how to learn a Data Science specialty to transform your interest into a high-paid profession.

隨著技術行業(yè)的快速變化和機器人創(chuàng)新的到來，數(shù)據(jù)科學是開始學習的最重要的領域之一。 如果您對數(shù)據(jù)及其使用方法充滿熱情，那么本文將幫助您了解如何學習數(shù)據(jù)科學專業(yè)，從而將您的興趣轉變?yōu)楦咝铰殬I(yè)。

數(shù)據(jù)科學新手學位或證書 (Degree or Certificate for Data Science Newbie)

You might think that it is time to get a tech degree or certificate as your first step. In reality, you don`t need this. Why? Your knowledge and what you can do will be a major factor in your interview, not education.

您可能認為現(xiàn)在是時候獲得技術學位或證書了。 實際上，您不需要此。 為什么？ 您的知識和能力將是面試的主要因素，而不是教育。

Yes, for sure it is good to have a tech degree or certificate, but this won`t help you to land your job only because you have a diploma. So think about certificate/degree as an optional item. It is good to have, but definitely not the first thing you need to start your Data Scientist path from.

是的，擁有技術學位或證書肯定是件好事，但這不會僅因為擁有文憑而幫助您找到工作。 因此，將證書/學位視為可選項目。 擁有它是一件好事，但絕對不是您要從中開始Data Scientist路徑的第一件事。

On the other hand, if you decided to dedicate your time to a deep learning path and selected university or course helped you to get the knowledge that you actually will use, it is a totally different thing.

另一方面，如果您決定將時間投入到深度學習道路上，并且所選的大學或課程幫助您獲得了實際使用的知識，那是完全不同的事情。

So my general advice will be to start a theory or practical learning path. If you have a lot of time and money, get courses or even a degree. Remember that company won`t ask you about your educational documents very detailed. Your answer will be generic: “Yes, I do have a degree/certificate” or “No, I don`t have a degree/certificate”.

因此，我的一般建議是開始理論或實踐學習的道路。 如果您有很多時間和金錢，那么可以上課程甚至獲得學位。 請記住，公司不會詢問您非常詳細的教育文件。 您的回答將是通用的：“是的，我確實有學位/證書”或“否，我沒有學位/證書”。

Also, 2020 is a year of doing everything remotely. I believe you can get everything online rather than go learning onsite. It is safer in our time and more effective.

此外，2020年是遠程完成所有工作的一年。 我相信您可以將所有內(nèi)容都在線獲得，而不必去現(xiàn)場學習。 它在我們時代更安全，更有效。

您需要學習的技能 (Skills You Need to Learn)

The very base of your learning path are Python, SQL, Machine Learning, and Statistics. This knowledge is high-level, so let`s review each of them separately.

您學習路徑的基礎是Python，SQL，機器學習和統(tǒng)計。 這些知識是高層次的，因此讓我們分別回顧每個知識。

Python (Python)

This will be your first programming language to learn. And this is very exciting! Why? Because Python is a general-purpose programming language. It supports a lot of frameworks and libraries. It is simple and fast in learning. Also, you will save a lot of your time writing in Python instead of Java.

這將是您學習的第一門編程語言。 這非常令人興奮！ 為什么？ 因為Python是一種通用編程語言。 它支持許多框架和庫。 學習簡單，快速。 同樣，您將節(jié)省大量時間來編寫Python而不是Java。

If you want to learn more why I give so high priority to Python, I have a separate article on Medium with all details:

如果您想了解更多為什么我為什么要如此優(yōu)先考慮Python，那么我有一篇有關Medium的文章，其中包含所有詳細信息：

You need to learn and practice simultaneously. Try to build your learning path with projects to include in your portfolio. It means that once you get enough theory knowledge, include as much practical steps as possible. By this, you will be able to write in pure Python very soon. This covers basic syntax, functions, control flow, loops, modules, and classes.

您需要同時學習和練習。 嘗試通過包含在您的投資組合中的項目來構建學習路徑。 這意味著一旦您掌握了足夠的理論知識，就應包括盡可能多的實際步驟。 這樣，您將能夠很快用純Python編寫代碼。 這涵蓋了基本語法，功能，控制流，循環(huán)，模塊和類。

SQL (SQL)

To work with databases, you need to learn SQL. This skill allows you to extract and interact with data. There are different SQL types, but you need to learn basic analytical SQL as a newbie.

要使用數(shù)據(jù)庫，您需要學習SQL。 此技能使您可以提取數(shù)據(jù)并與之交互。 SQL有不同的類型，但是您需要作為新手學習基本的分析SQL。

You can use W3School to learn from it. It has a basic theory that you need. And again, it is better to learn theory and practice as frequently as you can. Remember, any tech industry requires a lot of practical experience. If you stop your practice, you will lose your knowledge really fast.

您可以使用W3School來學習。 它具有您需要的基本理論。 同樣，最好盡可能多地學習理論和實踐。 請記住，任何技術行業(yè)都需要大量的實踐經(jīng)驗。 如果停止練習，您將很快失去知識。

SQL related questions are popular for Data Scientist in the interview. That`s why it is another reason to learn SQL and answer interview questions confidently and fast.

與SQL相關的問題在數(shù)據(jù)科學家中很受歡迎。 這就是為什么學習SQL并自信而快速地回答面試問題的另一個原因。

統(tǒng)計 (Statistics)

And you should learn Statistics for sure. The main things to concentrate attention on probability are distributions, statistical significance, hypothesis testing, and regression.

而且您應該確定學習統(tǒng)計學。 關注概率的主要方面是分布，統(tǒng)計顯著性，假設檢驗和回歸。

You can divide your Stats learning into a few steps:

您可以將統(tǒng)計學習分為以下幾個步驟：

Core Statistics Concepts

核心統(tǒng)計概念

• Experimental design
  實驗設計
• Regression modeling
  回歸建模
• Data transformation
  數(shù)據(jù)轉換

2. Bayesian Thinking

2. 貝葉斯思維

3. Machine Learning

3. 機器學習

Check UCI Machine Learning Repository. You can use their data for your personal projects. It is possible to deploy a model as well. Build your projects and store them on Github. This is the best way to learn and build a personal portfolio.

檢查UCI機器學習存儲庫 。 您可以將其數(shù)據(jù)用于個人項目。 也可以部署模型。 構建您的項目并將其存儲在Github上。 這是學習和建立個人檔案袋的最佳方法。

UCI repositoryUCI資料庫

機器學習 (Machine Learning)

I assume that the most popular methodologies for Data Scientists come from Machine Learning. It is different from other computer decisions because it includes prediction. The computer is able to use algorithms to predict results with its own data.

我認為數(shù)據(jù)科學家最流行的方法學來自機器學習。 它與其他計算機決策不同，因為它包括預測。 該計算機能夠使用算法通過其自己的數(shù)據(jù)預測結果。

If you want to build and deploy products in the future, ML is definitely something to learn in the beginning. Among all Data Scientist functions, there is a software engineer, which requires ML knowledge.

如果您想將來構建和部署產(chǎn)品，則ML絕對是一開始要學習的東西。 在所有數(shù)據(jù)科學家功能中，有一個軟件工程師，需要ML知識。

As you can see Data Scientist is not only the person who building models but also a person who run and support them. It means that she/he is similar to a software engineer.

如您所見，數(shù)據(jù)科學家不僅是構建模型的人，而且還是運行和支持模型的人。 這意味著她/他類似于軟件工程師。

一些有用的鏈接 (A few Helpful Links)

Here is a list that will help you:

以下列表將為您提供幫助：

Khan academy — to learn technical materials

汗學院 -學習技術資料

Codeacedmy Python Course — this is a great course for Python learning

Codeacedmy Python課程 —這是一門很棒的Python學習課程

An Introduction to Statistical Learning — Learn Stats from here.

統(tǒng)計學習簡介 -從此處了解統(tǒng)計信息。

Data Elixir: Data Science news and resources

數(shù)據(jù)藥劑 ：數(shù)據(jù)科學新聞和資源

重要的提示 (Important Note)

While you are learning Data Science, contribute to open source projects. You can find a lot of Python libraries, that need community help. It is a good way to:

在學習數(shù)據(jù)科學時，請為開源項目做出貢獻。 您可以找到很多需要社區(qū)幫助的Python庫。 這是執(zhí)行以下操作的好方法：

• practice your skills;
  練習你的技能；
• get instant feedback and help from other people;
  即時獲得他人的反饋和幫助；
• be involved in real projects;
  參與實際項目；
• open-source projects can organize a hackathon and you can participate there;
  開源項目可以組織黑客馬拉松，您可以在那里參加。
• you learn from others;
  你向別人學習；
• your portfolio has a real project.
  您的投資組合有一個真實的項目。

結論 (Conclusion)

Data Scientist is a very innovative and highly paid specialty. If you want to learn it fast, I have shared helpful steps that will make you successful in your learning journey. I hope I have inspired you to become a Data Scientist and you will use my tips to start your learning journey to become a Data Scientist.

數(shù)據(jù)科學家是一個非常創(chuàng)新且高薪的專業(yè)。 如果您想快速學習它，我共享了一些有用的步驟，這些步驟將使您在學習過程中取得成功。 希望我啟發(fā)了您成為一名數(shù)據(jù)科學家，您將使用我的技巧開始學習之旅，成為一名數(shù)據(jù)科學家。

翻譯自: https://towardsdatascience.com/how-to-learn-data-science-and-start-your-amazing-journey-7de3f7757157

一個數(shù)據(jù)包的旅程

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