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by Jose Marcial Portilla
通过何塞·马西尔·波蒂利亚(Jose Marcial Portilla)
Hi! I’m Jose Portilla and I’m an instructor on Udemy on Python for Data Science and Machine Learning, R Programming for Data Science, Python for Big Data, and many more.
嗨！ 我是Jose Portilla，是Udemy的讲师有关数据科学和机器学习的Python，数据科学的R编程，大数据的Python 。
Almost every day a student will ask me some form of this question:
几乎每天都有学生会问我这个问题的某种形式：
“What should I do to become a data scientist?”
“我应该怎么做才能成为数据科学家？”
In this post, I’ll try my best to help answer this question and point to resources that can help guide you to an answer, also hopefully this post serves as something I can quickly link to my students :)
在这篇文章中，我将尽力帮助回答这个问题，并指向可以帮助您指导答案的资源，也希望这篇文章可以作为我可以快速链接到我的学生的东西:)
Before we get started, I’m now teaching Data Science for Python and R on Udemy. You can check out these courses below and get a discount for using these links:
在开始之前，我现在在Udemy上教授Python和R的数据科学。 您可以在下面查看这些课程，并获得使用这些链接的折扣：
For Python:
对于Python：
For R:
对于R：
Now on to the rest of this post. I’ve broken down the steps into some key topics and discussed helpful details for each.
现在继续本帖子的其余部分。 我将步骤分为几个关键主题，并讨论了每个主题的有用细节。
“The secret of getting ahead is getting started.” — Mark Twain
“取得成功的秘诀就是开始。” — 马克·吐温
If you are interested in becoming a data scientist the best advice is to begin preparing for your journey now. Taking the time to understand core concepts will not only be very useful once you are interviewing, but it will also help you decide whether you are truly interested in this field.
如果您有兴趣成为一名数据科学家，那么最好的建议就是立即开始您的旅程。 花时间了解核心概念不仅在面试后非常有用，而且还可以帮助您确定您是否对该领域真正感兴趣。
Before starting on the path to becoming a data scientist, its important that you are honest with yourself about why you want to do this. There are probably some questions you should ask yourself:
在开始成为数据科学家之前，重要的一点是您对自己为什么要这么做很诚实。 您可能应该问自己一些问题：
Ask yourself these questions and be honest with yourself. If you answered yes, then you are on your way to become a data scientist.
问问自己这些问题，并对自己诚实。 如果您回答“是”，那么您将成为一名数据科学家。
The path to becoming a data scientist will most likely take you some time, depending on your previous experience and your network. Leveraging these two can help place you in a data scientist role faster, but be prepared to always be learning. Let’s now jump to discussions on some more tangible topics.
成为数据科学家的道路很可能会花费您一些时间，这取决于您以前的经验和您的网络。 充分利用这两点，可以帮助您更快地成为数据科学家，但要时刻准备学习。 现在让我们跳到一些更具体的主题上进行讨论。
“Do not worry about your difficulties in Mathematics. I can assure you mine are still greater.” — Albert Einstein
“不用担心您在数学上的困难。 我能向你保证，我的依然更好。” — 爱因斯坦
The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core mathematical foundations will serve as a base for you to continue learning from. Let’s briefly describe each and give you a few resources to learn from!
如果您想进入数据科学领域，您应该熟悉的数学主题是概率，统计和线性代数。 当您了解有关统计学习(机器学习)等其他主题的更多信息时，这些核心数学基础将成为您继续学习的基础。 让我们简要地介绍一下每种方法，并为您提供一些学习的资源！
Probability is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.
概率是事件发生可能性的度量。 许多数据科学都基于尝试测量事件的可能性，这些事件的可能性从广告被点击的几率到装配线上零件发生故障的可能性，无所不包。
For this classic topic I recommend going with a book, such as A First Course in Probability by Sheldon Ross or Probability Theory by E.T. Jaynes. Since these are textbooks they can be quite expensive if you buy new directly from amazon, so I suggest looking at used copies online or at pdf versions to save yourself some money!
对于这个经典主题，我建议您阅读一本书，例如Sheldon Ross撰写的《 概率论 第一课》或《 概率论》 由ET Jaynes。 由于这些都是教科书，如果您直接从亚马逊购买新书，它们可能会非常昂贵，因此，我建议您在线上查看二手副本或使用pdf版本，以节省金钱！
If you prefer learning through a video format, you can also check out Khan Academy’s video series on probability. You can also check out MIT’s OpenCourseWare lectures on Probability and Statistics. Both can be found easily for free on Youtube with a simple search.
如果您喜欢通过视频格式学习，还可以查看可汗学院关于概率的视频系列。 您也可以查看MIT的关于概率和统计的OpenCourseWare讲座。 只需简单搜索，即可在Youtube上免费轻松轻松找到这两者。
Once you have a firm grasp on probability theory you can move on to learning about statistics, which is the general branch of mathematics that deals with analyzing and interpreting data. Having a full understanding of the techniques used in statistics requires you to understand probability and probability notation!
一旦掌握了概率论，您就可以继续学习统计学，这是处理数据分析和解释的数学的一般分支。 全面了解统计中使用的技术要求您了解概率和概率符号！
Again, I’m more of a textbook person, and fortunately there are two great online textbooks that are completely free for you to reference:
同样，我更像是一个教科书的人，幸运的是，有两本很棒的在线教科书完全免费供您参考：
If you prefer more old-school textbooks, I like Statistics by David Freedman. I would suggest using this book as your main base and then checking out the other resources listed here for deeper dives into other topics (like ANOVA).
如果您更喜欢老式的教科书，我喜欢David Freedman的Statistics 。 我建议您以这本书为主要基础，然后查看此处列出的其他资源，以更深入地研究其他主题(例如ANOVA)。
For practice problems I really enjoyed using Shaum’s Outlines Series (you can find books in this series for both Probability and Statistics).
对于实践问题，我真的很喜欢使用Shaum的提纲系列(您可以在本系列中找到有关概率和统计学的书籍)。
If you prefer video, check out Brandon Holtz’s great series on statistics on Youtube!
如果您喜欢视频，请查看Brandon Holtz在YouTube上有关统计数据的精彩系列！
This is the branch of math that covers the study of vector spacing and linear mapping between these spaces. Its used heavily in machine learning, and if you really want to understand how these algorithms work, you will need to build a basic understanding of Linear Algebra.
这是数学的分支，涵盖了这些空间之间的向量间距和线性映射的研究。 它在机器学习中大量使用，如果您真的想了解这些算法的工作原理，则需要对线性代数有基本的了解。
I recommend checking out Linear Algebra and Its Applications by Strang, its a great textbook that is also used in the MIT Linear Algebra course you can access via OpenCourseWare! With these two resources you should be able to build a solid foundation in linear algebra.
我建议您阅读Strang撰写的《 线性代数及其应用》 ，它是一本很棒的教科书，您也可以通过OpenCourseWare访问MIT线性代数课程！ 使用这两种资源，您应该能够在线性代数中建立坚实的基础。
Depending on your position and workflow, you may not need to dive very deep into some of the more complex details of linear algebra, once you get more familiar with programming, you’ll see that some libraries tend to handle a lot of the linear algebra tasks for you. But it is still important to understand how these algorithms work!
根据您的职位和工作流程，您可能不需要深入研究线性代数的一些更复杂的细节，一旦您对编程更加熟悉，就会发现一些库倾向于处理很多线性代数任务给你。 但是了解这些算法的工作原理仍然很重要！
“Measuring programming progress by lines of code is like measuring aircraft building progress by weight.” — Bill Gates
“通过代码行来衡量编程进度就像通过重量来衡量飞机制造进度。” — 比尔·盖茨
The data science community has mainly adopted R and Python as its main languages for programming. Other languages such as Julia and Matlab are used as well, but R and Python are by far the most popular in this space.
数据科学界主要采用R和Python作为其主要的编程语言。 也使用了其他语言，例如Julia和Matlab，但是R和Python是迄今为止在该领域最受欢迎的语言。
In this section I’m going to describe some of the main basic topics of programming and data science, and then point out the main libraries used for both R and Python!
在本节中，我将描述编程和数据科学的一些主要基本主题，然后指出用于R和Python的主要库！
This is a topic that is extremely dependent on your personal preference, I’m just going to briefly describe some of the more popular options for development environments (IDEs) for data science with R and Python.
这是一个非常取决于您的个人喜好的主题，我仅简要介绍一些使用R和Python进行数据科学开发环境(IDE)的较流行选项。
Python — Since Python is a general programming language lots of options are available! You could just use a plain text editor such as or and then customize to your own liking, I personally use this approach for larger projects. Another popular IDE for python is from JetBrains, which provides a free community edition that has plenty of features for most users. My favorite environment for Python has to be the , previously known as iPython Notebooks, this notebook environment uses cells to break up your code and provides instant output, so you can interact with the code and visualizations easily! Jupyter Notebook supports many kernels, including Scala, R, Julia, and more. Python is by far the best supported out of all of these, although the other languages improve all the time! Jupyter notebooks are extremely popular in the field of data science and machine learning. I use this for all my Python courses and most students have really enjoyed it. While probably not the best solution for larger projects that need to be deployed, its fantastic for a learning environment.
Python-由于Python是一种通用的编程语言，因此可以使用许多选项！ 您可以只使用纯文本编辑器(例如或 ，然后根据自己的喜好进行自定义，我个人将这种方法用于大型项目。 另一个流行的python IDE是JetBrains的 ，它提供了一个免费的社区版本，该版本为大多数用户提供了很多功能。 我最喜欢的Python环境是 (以前称为iPython Notebooks)，此笔记本环境使用单元来分解代码并提供即时输出，因此您可以轻松地与代码和可视化交互！ Jupyter Notebook支持许多内核，包括Scala，R，Julia等。 尽管其他语言一直在不断进步，但到目前为止，Python是所有这些语言中受支持最好的一种！ Jupyter笔记本在数据科学和机器学习领域非常受欢迎。 我在所有Python课程中都使用了它，大多数学生都非常喜欢它。 对于可能需要部署的大型项目来说，虽然它可能不是最佳的解决方案，但对于学习环境而言却是绝佳的选择。
As far as getting Python installed on your computer, you can always use the official source — python.org , but I usually suggest using the distribution, which comes with many of the packages I’ll discuss in this section!
至于要在计算机上安装Python，您始终可以使用官方资源python.org，但是我通常建议使用发行版，该发行版将与本节中将讨论的许多软件包一起提供！
R — is probably the most popular development environment for R. It has a great community behind it, its basic full version is completely free. It displays visualizations well, gives you lots of options for customizing experience and a lot more. It is pretty much my go to for anything with R! Jupyter Notebooks also support R kernels, and while I have used them, I have found the experience lacking compared to Jupyter Notebook’s capabilities with Python.
R — 可能是R最受欢迎的开发环境。它背后有一个强大的社区，其基本完整版完全免费。 它很好地显示了可视化效果，为您提供了许多定制体验的选项以及更多其他功能。 我几乎可以选择R！ Jupyter Notebook还支持R内核，尽管我使用过R内核，但与Jupyter Notebook的Python功能相比，我发现缺乏这种体验。
Python — For data analysis, two libraries are the main workhorses of Python: and . NumPy is a numerical scientific computing package that serves as the base for almost all the other Python packages in the Python Data Science ecosystem. Pandas is a data analysis library that is built directly off of NumPy that is designed to mimic many of the built-in features or R, such as DataFrames! You can think of it as a super version of Excel that allows you to quickly clean and analyze data. If you become a data scientist that uses Python, pandas will quickly become one of your main tools! It is personally my favorite Python library! I would also recommend checking out for details and links for the libraries in the PyData system.
Python-对于数据分析，两个库是Python的主要力量： 和 。 NumPy是一个数值科学计算软件包，可作为Python数据科学生态系统中几乎所有其他Python软件包的基础。 Pandas是直接基于NumPy构建的数据分析库，旨在模仿许多内置功能或R，例如DataFrames！ 您可以将其视为Excel的超级版本，该版本使您可以快速清理和分析数据。 如果您成为使用Python的数据科学家，熊猫将很快成为您的主要工具之一！ 个人而言，这是我最喜欢的Python库！ 我还建议您查看 ，以获取PyData系统中库的详细信息和链接。
R — For the most part R already comes with a lot of data analysis features built-in, such as Dataframes! But the R community has also created a lot of useful packages for helping deal with data in an even more efficient manner! These packages are known as the “”, and its a collection of useful packages for data science, all designed with a similar philosophy of working with data, meaning that they all work very well together. These packages include for data manipulation, for cleaning your data, for reading in data, and packages like purr and which improve some built-in functionalities of R. Learning the tidyverse of packages is a must for a data scientist using R! ggplot2 is also part of the tidyverse, but is for data visualization, so let’s jump to that topic next!
R —在大多数情况下，R已经内置了许多数据分析功能，例如数据框！ 但是R社区还创建了许多有用的软件包，以帮助您更有效地处理数据！ 这些软件包被称为“ ”，它是数据科学的有用软件包的集合，所有软件包均以类似的数据处理哲学进行设计，这意味着它们都可以很好地协同工作。 这些软件包包括进行数据操作， 清洗您的数据， 在数据读取，并且像呼噜声和包这提高了一些内置的R.学习功能包的tidyverse是数据科学家，使用R一绝！ ggplot2也是tidyverse的一部分，但用于数据可视化，因此让我们跳到下一个主题！
Python — The “grandfather” of visualization with Python is . Matplotlib was created to provide a visualization API for Python reminiscent of the style used in MatLab. If you have used MatLab for visualization before, the transition will feel very natural. However, due to its huge library of capabilities, a lot of other visualization libraries have been created off of matplotlib in an attempt to simplify things or provide more specific functionality!
Python-使用Python进行可视化的“祖父”是 。 Matplotlib的创建旨在为Python提供可视化API，让人联想到MatLab中使用的样式。 如果您以前使用过MatLab进行可视化，过渡将非常自然。 但是，由于其功能强大的库，因此在matplotlib的基础上创建了许多其他可视化库，以简化操作或提供更特定的功能！
is a great statistical plotting library that works very well with pandas and is written with the use matplotlib. It creates beautiful plots with just a few lines of code.
是一个出色的统计绘图库，可与熊猫配合使用，并使用matplotlib编写。 它仅用几行代码即可创建漂亮的图。
Pandas also comes with built off of matplotlib!
熊猫还具有基于matplotlib构建的内置 ！
and can be used to create interactive plots with Python. I recommend playing around with both and seeing which one you prefer!
和可用于使用Python创建交互式图。 我建议您一起玩，看看您喜欢哪一个！
R — By far the most popular plotting library for R is . It philosophy on designed and its layer based API makes it easy to use and allows you to make basically any major plot you can think of! What is also great is that is works easily with Plotly, allowing you to quickly convert ggplot2 graphs into interactive visualizations through the use of!
R —到目前为止，R最受欢迎的绘图库是 。 它基于设计的理念及其基于层的API使其易于使用，并允许您制作出您可以想到的任何主要图形！ 很棒的是，它可以轻松地与Plotly一起使用，从而允许您通过使用将ggplot2图形快速转换为交互式可视化！
Python — is the most popular machine learning library for Python, with built-in algorithms and models for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. If you are more interested in building statistical inference models (such as analyzing p-values after a linear regression), you should check out , it also is a great choice for working with time series data! For Deep Learning, check out , , or . I recommend Keras for beginners due to its simplified API. For Deep Learning topics you should always reference the official documentation, as this is a field that changes very fast!
Python — 是最流行的Python机器学习库，具有用于分类，回归，聚类， 维，模型选择和预处理的内置算法和模型。 如果您对建立统计推断模型(例如在线性回归后分析p值)更感兴趣，则应查看 ，这也是处理时间序列数据的绝佳选择！ 深学习，看看 ， ，或 。 由于其简化的API，我向初学者推荐Keras。 对于深度学习主题，您应该始终参考官方文档，因为这是一个变化非常快的领域！
R — One of the issues with R for beginner data scientists is that it has a huge variety of options for packages when it comes to machine learning. Each major algorithm can have its own separate packages, each with different focuses. When you are starting out I recommend first checking out the package, which provides an nice interface for classification and regression tasks. Once you’ve moved on to unsupervised learning techniques such as clustering, your best bet is to do a quick google search to see which packages are the most popular for whatever technique you plan to use, you’ll even discover that R already had some of the basic algorithms built-in, such as kmeans clustering.
R – R对于初学者数据科学家而言，其中一个问题是，在机器学习方面，它为软件包提供了多种选择。 每个主要算法可以有自己的单独程序包，每个程序包都有不同的重点。 开始时，我建议先检查包，它为分类和回归任务提供了一个不错的界面。 一旦您学习了无监督学习技术(例如集群)，最好的选择就是进行快速的google搜索，以了解对于打算使用的任何技术最受欢迎的软件包，您甚至会发现R已经有一些内置的基本算法，例如kmeans聚类。
I teach these topics in full, you can check out the courses for 95% off by using the links below.
我已全面教授了这些主题，您可以使用下面的链接查看95％的课程。
My Python for Data Science and Machine Learning Bootcamp:
我的Python for Data Science and Machine Learning Bootcamp：
My course on R for Data Science, Visualization, and Machine Learning:
我关于R的数据科学，可视化和机器学习课程：
Now that we’ve gone over the general background of programming topics, let’s discuss the path to actually landing a data science job!
现在，我们已经了解了编程主题的一般背景，让我们讨论实际完成数据科学工作的途径！
“Good company in a journey makes the way seem shorter.” — Izaak Walton
“旅途中的好公司使道路看起来更短。” — 艾萨克·沃尔顿 ( Izaak Walton)
The job search for data scientist positions can take a while, its best to begin building out your network!
寻找数据科学家职位的工作可能需要一段时间，最好开始建立您的网络！
One of the best ways to begin to build out your network is to attend But you don’t need to be limited strictly to data science, you should attend meetups with any topics that are related to data science, things like Python meetups, Visualization meetups, etc.
开始建立网络的最佳方法之一就是参加 但是您不必严格限于数据科学，您应该参加与数据科学相关的任何主题的聚会，例如Python聚会，可视化聚会等。
Conferences are another great way to connect to data scientists, while many conferences can be prohibitively expensive, conferences will often have a career fair as part of the event. If you only intend to visit for the career fair you can often get discounted or even free passes to the conference. Conferences also often host workshops for you to learn new skills!
会议是与数据科学家建立联系的另一种很好的方式，尽管许多会议的费用过高，但会议通常会将职业博览会作为活动的一部分。 如果您只打算参加职业展览会，则通常可以享受折扣，甚至免费获得会议入场券。 会议还经常举办研讨会供您学习新技能！
You should also begin to check out online communities and resources, things like O’Reilly data newsletter, Kaggle, and KDNuggets are great resources to plug yourself into what is happening in the data science community. Podcasts are another great way to get started learning about the data science community. I recommend checking out Talking Machines, Partially Derivatives, and the O’Reilly Data Show.
您还应该开始查看在线社区和资源，O'Reilly数据通讯，Kaggle和KDNuggets之类的资源非常有用，可以让您了解数据科学社区中正在发生的事情。 播客是开始学习数据科学社区的另一种好方法。 我建议您查看Talking Machines，部分衍生物和O'Reilly数据显示。
It is also worth exploring general technology communities, such as Quora and HackerNews!
还值得探索一般技术社区，例如Quora和HackerNews！
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” — James L. Barksdale
“如果有数据，我们来看一下数据。 如果我们只有意见，那我们就去吧。” - 詹姆斯·L·巴克斯代尔
So you’ve learned your skills, networked, and are now ready to begin working as a data scientist!
因此，您已经学习了技能，已经联网，现在可以开始作为数据科学家了！
The first step is to begin your search for a new job, a lot of this will vary depending on your personal circumstances and goals, so I’ll try to keep advice as general as possible.
第一步是开始寻找新工作，很多工作会因您的个人情况和目标而有所不同，因此，我将尽量保持建议的通用性。
One of the best ways to begin your search and practice your skills at the same time is to participate in Kaggle challenges and blog about your experience with them. Some Kaggle challenges can even directly lead to interviews as part of the prize! Even if nothing comes of the prize, its still valuable experience on a real data set! Note that Kaggle also has its own job board for data scientists.
同时开始搜索和练习技能的最佳方法之一是参与Kaggle挑战并撰写有关您的体验的博客。 一些Kaggle挑战甚至可以直接导致面试，这是奖品的一部分！ 即使没有任何奖励，它在真实数据集上仍然有价值的经验！ 请注意，Kaggle还为数据科学家提供了自己的工作委员会。
Freelancing through sites like UpWork, contributing to open-source projects, and answering questions on StackOverflow is another great way to make your presence known to recruiters.
通过UpWork等网站进行自由职业，为开源项目做贡献，并在StackOverflow上回答问题，这是使招聘人员知道您的存在的另一种好方法。
You will also want to make sure that your CV, LinkedIn, and Github are all updated to reflect your new skills and projects.
您还需要确保您的简历，LinkedIn和Github均已更新，以反映您的新技能和新项目。
Make use of sites like Indeed or for a general job search, of try out sites like Triplebyte that directly give you a series of technical interviews to quickly go through the initial interview phase for many companies at once. You can also check out startup jobs with the and .
可以使用诸如Indeed或类的网站进行一般工作搜索，而可以尝试诸如Triplebyte之类的网站，这些网站可以直接为您提供一系列技术面试，以快速地一次完成许多公司的初始面试阶段。 您还可以使用和检出启动作业。
For better or for worse, many companies still rely on classic interview questions that involve Data Structures and Algorithms. To prepare for these sort of questions you should review topics such as Arrays,Graphs, Recursion, Linked Lists, Stacks, etc… you should reference a book or course, and go through lots of practice problems! I have courses on these topics, you can get a free viewing of some of the material by checking out my popular github repository containing lots of jupyter notebooks with practice questions and solutions!
无论好坏，许多公司仍然依靠经典的面试问题，这些问题涉及数据结构和算法。 为了准备这些问题，您应该复习数组，图形，递归，链接列表，堆栈等主题，您应该参考一本书或一门课程，并经历很多练习题！ 我有关于这些主题的课程，您可以通过查看我受欢迎的github存储库(其中包含许多带有实践问题和解决方案的jupyter笔记本)来免费浏览某些材料！
You can also check out a list of practice problems on leetcode:
您还可以查看有关leetcode的练习问题的列表：
For more specific data science questions, you’ll need to familiarize yourself with a wide variety of topics, such as questions on probability, programming questions on R or Python, SQL queries, and possibly big data management (topics such as Spark). You should also familiarize yourself with modeling and the reasoning behind choosing parameters, for example the differences between L1 and L2 regularization.
对于更具体的数据科学问题，您需要熟悉各种主题，例如有关概率的问题，有关R或Python的编程问题，SQL查询以及可能的大数据管理(诸如Spark等主题)。 您还应该熟悉建模和选择参数的理由，例如L1和L2正则化之间的差异。
Many companies also do take home tasks, this can be a great opportunity to get some extra practice in, even if the job offer itself doesn’t pan out.
许多公司也承担家务劳动，这是一个很好的机会，可以进行一些额外的练习，即使工作本身并没有成功。
翻译自:
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