How do I become an expert in data science? How can I join the apprenticeship? Rather than any other, I get this question. This is a long , complicated path. I’m not going to cheat about this. This path is different from where you happen to be with your profession. From a few typical job milestones, I will outline roadmap. I need to start with those preconditions before I do.
如何成为数据科学专家？ 如何加入学徒期？ 我得到了这个问题，而不是其他任何问题。 这是一条漫长而复杂的道路。 我不会为此作弊。 这条路不同于您碰巧从事的职业。 从几个典型的工作里程碑中，我将概述路线图。 在开始之前，我需要先考虑这些先决条件。
In data science, there is a lot of versatility. Psychology, engineering, arithmetic, finance, Chemistry, genetics, etc. are all subjects. The language coding is in the diagram. Focus areas and markets differ greatly. There are also a few challenging and swift demands. You‘re going to have to learn algebra. You took advanced math courses, I don’t think. At an applicable level, I think they made sense. I’m mentally focused, but economics has similar benefits; they‘re showing a student how to execute advanced mathematics, not just how to solve simple problems.
在数据科学中，有很多用途。 心理学，工程学，算术，金融，化学，遗传学等都是学科。 语言编码在图中。 重点领域和市场差异很大。 还有一些挑战性和Swift的需求。 您将必须学习代数。 我认为您上过高级数学课程。 在适用的水平上，我认为它们是有道理的。 我专注于精神，但是经济学有类似的好处； 他们向学生展示了如何执行高级数学，而不仅仅是如何解决简单的问题。
Naturally, there are scripting and data structures. Simple to gather channels. Throughout my education, I taught a dozen, maybe more, language programming. They were just objects, attributes, interfaces, data structures, and loops that I have had no trouble with, in various formats. (I know that the model splits Quipper and other quantum programming languages … so we’re not going there now.)
自然地，存在脚本和数据结构。 收集渠道简单。 在整个学习过程中，我教授了十几种甚至更多的语言编程。 它们只是各种格式的对象，属性，接口，数据结构和循环，我没有遇到任何麻烦。 (我知道该模型将Quipper和其他量子编程语言分开了……所以我们现在不去那里了。)
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This is the degree of ease with which you need to excel as a data scientist in programming languages. Over a year of tasks, I will go from C to R to Java, Python to C #. Mongo to Cassandra, Cassandra, Dynamo, and MySQL. Excel and SAS can not do the job with real-world ventures.
这是您作为编程语言中的数据科学家需要表现出的轻松程度。 超过一年的任务，我将从C到R到Java，从Python到C＃。 Mongo到Cassandra，Cassandra，Dynamo和MySQL。 Excel和SAS无法在现实世界中发挥作用。
You‘re running into trouble, not out. As a data scientist, an analysis-based approach is necessary to succeed. You‘re the best person for that area exactly if you get yourself excited any time something goes wrong and get hurried when solving a problem. Models look like marionettes. You must still be patient and able to clean up several messes.
您遇到了麻烦，而不是失败。 作为数据科学家，成功使用基于分析的方法是必要的。 如果您在遇到任何问题并在解决问题时急忙时感到兴奋，那么您就是该领域的最佳人选。 模特看起来像木偶。 您必须仍然耐心并且能够清理一些混乱情况。
In the last couple of years, you have two jobs. A strong master’s degree candidate. Your hands on it, see the real world. Here are my Master‘s suggestions.
在最近几年中，您有两项工作。 强大的硕士学位候选人。 您动手操作，看看真实世界。 这是我师父的建议。
The Standards:
标准：
· UC Berkley
·加州大学伯克利分校
· USC
·南加州大学
· Stanford
·斯坦福
· MIT
·麻省理工学院
· University of Washington
·华盛顿大学
Excellent Emerging Programs:
优秀的新兴程序：
· University of Illinois
·伊利诺伊大学
· Northwestern
·西北
· Arizona State
·亚利桑那州
· University of San Francisco
·旧金山大学
· NYU
·纽约大学
These guidelines are based on my familiarity with students and alumni and a mix of credibility in the region. It’s not thorough at all. How important is your undergraduate course, I have been asked a lot. It will help to get your Master’s in the program you like, but it isn’t really necessary otherwise. Without a master’s, will you get a career in data science? Yeah. Yeah, yes. It may restrict your choices and may impose a limit on your future, but there are many businesses with a bachelor’s degree.
这些准则是基于我对学生和校友的熟悉程度以及该地区的信誉程度而定的。 这根本不彻底。 你的本科课程有多重要，有人问过我。 这将有助于获得您喜欢的程序的硕士学位，但实际上并没有必要。 没有硕士学位，您将获得数据科学职业吗？ 是的 是的，是的 它可能会限制您的选择，并可能限制您的未来，但很多企业都拥有学士学位。
Your second task is to see the modern world. I began working in technology at 19 works that were strange to deploy networks, develop servers, create websites, and maintain databases. I have completed three years of experience and a portfolio of projects in my profession. It was much easier to recruit my first employee.
您的第二项任务是看现代世界。 我从19项工作开始从事技术工作，这些工作对于部署网络，开发服务器，创建网站和维护数据库来说很奇怪。 我已经完成了三年的工作经验，并从事过一系列项目。 招募第一位员工要容易得多。
It can be hard when you’re in school to get real-life experience. Practices in a waiting list and few businesses of school data scientists want to recruit a part-time. My recommendation starts with hackathons that are funded by esteemed businesses such as Google, Facebook, or IBM. I had a hackathon with them at the IBM Link Conference earlier this year. The decision was made at 4:30 a.m. Everybody in the Watson community had work opportunities by 9:30.
当您在学校里要获得真实的体验时可能会很难。 在等待名单中的实践中，很少有学校数据科学家需要招聘兼职人员。 我的建议首先是由知名企业(例如Google，Facebook或IBM)资助的黑客马拉松。 我在今年早些时候的IBM Link会议上与他们进行了黑客马拉松。 该决定是在上午4:30做出的。Watson社区中的每个人到9:30都有工作机会。
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To participate in data analysis and computing space to open-source initiatives. This is another field from which businesses produce talent. It is also a perfect way to make your name visible in the crowd. Quality achievements are the origin of a solid sector credibility. Videos and podcasts will do the same thing. Share with you what you are studying. Contact your teachers and fellow students. You could see the first work opportunity soon.
参与数据分析和计算空间的开源计划。 这是企业培养人才的另一个领域。 这也是使您的名字在人群中可见的完美方法。 质量成就是坚实的行业信誉的源泉。 视频和播客将做同样的事情。 与您分享您正在学习的内容。 与您的老师和同学联系。 您很快就会看到第一个工作机会。
Find ways to engage in academic programs. It looks fantastic on a list whether you get one or two peer ratings while you are at school. An interesting research paper will become an exciting project with some further effort. Please note to submit your master’s thesis so that you can select a subject in an environment in which you would like to work. Dream about beginning a business as well. The basis for a marketable product can simply be a research project. Find practical uses for what you understand. Running a startup while school is indeed a successful idea and business practice, also one which is not so far from.
寻找参与学术课程的方法。 无论您在学校时获得一个或两个同级评级，在列表上看起来都很棒。 经过进一步的努力，一篇有趣的研究论文将成为令人兴奋的项目。 请注意提交硕士学位论文，以便您可以在想要工作的环境中选择一个主题。 也梦想着创业。 适销产品的基础可以只是一个研究项目。 找到您所了解的实际用途。 在学校里创办一家初创企业确实是一种成功的想法和商业实践，而且相距不远。
You‘re going to your first career if you came out of a data science program with some industry experience. It’s not going to be tough to search. The lack of talent is immense. Here’s a pair of tips. Avoid bosses on the first employment. When the industry, the industry, and what businesses are looking to get a great feel for your profession, employers are an impressive advantage. Don’t hop on the first bid, as appealing as it would be. Explore the choices and then make up your mind and future for the right location. For a good tutor. Get a good mentor. It’s invaluable to have someone to share suggestions on who was there.
如果您是具有一定行业经验的数据科学计划的学习者，那么您将进入第一职业。 搜索将不会很困难。 人才的缺乏是巨大的。 这里有一些技巧。 避免在第一份工作上的老板。 当行业，行业以及企业希望对您的职业有很好的感觉时，雇主是令人印象深刻的优势。 不要像它那样吸引人的出价。 探索各种选择，然后下定决心并在合适的位置发展未来。 对于一个好的导师。 找一个好导师。 找人分享关于谁的建议是非常宝贵的。
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The last paragraph was for those who just don’t need this post. You’ve got a strong sense of what you want to do, whether you come out of a master’s degree in computer science or software education with experience. The fact is really different for most people. Most have only completed their Degree in computer science. There are those who have no field knowledge. Many new graduates are only beginning to consider the notion of an engineering or computer science profession.
最后一段是给那些不需要这篇文章的人的。 无论您是获得计算机科学硕士学位还是具有经验的软件教育硕士学位，您对自己想做什么都有很强的意识。 对于大多数人来说，事实确实不同。 大多数人只完成了计算机科学学位。 有些人没有现场知识。 许多新毕业生才刚刚开始考虑工程学或计算机科学专业的概念。
Starting by choosing two main branches. Would you want to create ideas or methods for research? Construction, realistic, and hands-on is added. You love the production and consumer hands of your jobs. In real life, you want to look at it and you can point to items that you have made. Theoretical analysis is free of the limitations of making it act as a commodity. Concept data is as far as testing is concerned. I’m on the applicable side most of my time. I have trouble providing the theoreticians, but it is because of my analysis that my ideas sound so amazing to my customers.
首先选择两个主要分支。 您想创建研究思路或方法吗？ 构造，现实和动手添加。 您喜欢工作中的生产和消费者手。 在现实生活中，您需要查看它，并且可以指向自己制作的物品。 理论分析没有使其成为商品的局限性。 概念数据就测试而言。 我大部分时间都在适应方面。 我在提供理论专家方面遇到麻烦，但是由于我的分析，我的想法对我的客户来说真是太神奇了。
You go to school for a PhD if you want to do research. If you want to. On the theoretical side, this is an utter requirement. Typically, this is the direction recent graduates pursue in the world of data science. It’s time to get some true expertise if you want to create solutions. See the last segment for a description of the computer science and machine learning. If you come from a Master of data sciences program and feel attracted to your submission, your journey is a little harder. Skip to the next segment where I speak about moving from another profession into the sector.
如果您想进行研究，则可以去学校攻读博士学位。 如果你想。 从理论上讲，这是完全必要的。 通常，这是应届毕业生在数据科学领域追求的方向。 如果您想创建解决方案，该是时候获得一些真正的专业知识了。 有关计算机科学和机器学习的描述，请参见最后一部分。 如果您是数据科学硕士课程的学生，并且对自己的论文很感兴趣，那么您的旅程会有些困难。 跳到下一个部分，我在这里谈到从另一个专业进入该行业。
Meanwhile, check the area for a position. There are several positions in software creation, consultant, quantum, and analysis helpers that can help you understand data science. If you are eligible, submit your curriculum vitae. As soon as your skills and knowledge improve you will be recruited into the industry. There is a lack of talent, like I said, and companies are innovative to fill their gaps. Don’t be shocked if you ask the employer to pay for more training or certifications to keep you up to date.
同时，检查该区域的位置。 在软件创建，顾问，量子和分析助手中有几个职位可以帮助您了解数据科学。 如果您符合资格，请提交简历。 只要您的技能和知识有所提高，您就会被招募到该行业。 就像我说的那样，缺乏人才，公司正在创新以填补自己的空白。 如果您要求雇主为您提供更多培训或认证以保持最新状态，请不要感到震惊。
This can be the most challenging path to data science or computer education. There is potentially just a slight difference in expertise if you’re in a tangential area like computer engineering. The expertise gap is far greater for a software developer, project manager, scientist, or some other field, and doesn’t overlap much with data science. Step 1 evaluates your competence.
这可能是通往数据科学或计算机教育的最具挑战性的途径。 如果您处在计算机工程等切向领域，则专业知识可能仅会略有不同。 对于软件开发人员，项目经理，科学家或其他领域而言，专业知识差距要大得多，并且与数据科学并没有太多重叠。 步骤1评估您的能力。
· You need a lot of math. Calculus, finite math, logic, linear algebra, probability & statistics, and graph theory at a minimum should be well understood.
·您需要大量的数学运算。 微积分，有限数学，逻辑，线性代数，概率和统计以及图论理论至少应被充分理解。
· You need average coding skills across a backend language like Java, C#, or C/C++ as well as a data science/machine learning heavy language like Python or R.
·您需要跨Java，C＃或C / C ++等后端语言以及诸如Python或R的数据科学/机器学习重语言的普通编码技能。
· You need to know at least one common SQL database and one NoSQL database. Understanding framework pieces like Hadoop, Spark, Amazon ML, and Azure ML is also important.
·您需要至少知道一个公共SQL数据库和一个NoSQL数据库。 了解诸如Hadoop，Spark，Amazon ML和Azure ML之类的框架部分也很重要。
· You need to be able to convert a business problem(s) into an algorithmic/model solution. You also need to be able to convert a research paper into a solution to a business problem(s). Math provides the tools and common language. Analytical thinking ties those tools to solutions.
·您需要能够将业务问题转换为算法/模型解决方案。 您还需要能够将研究论文转换为业务问题的解决方案。 数学提供了工具和通用语言。 分析性思维将这些工具与解决方案联系在一起。
· Familiarity with data visualization software/libraries and best practices is an often overlooked must-have. Excellent communication skills are needed. Without the ability to visualize and communicate results, all the other skills go to waste.
·熟悉数据可视化软件/库和最佳实践是经常被忽略的必备条件。 需要出色的沟通技巧。 没有可视化和交流结果的能力，所有其他技能都将浪费掉。
You have a bachelor’s or less, you usually go back to school. you definitely go back to school. There are several strong services for students with easy-to-work options. Choose one you deal on so that you stick with it. You are likely to support the new boss with teaching. Choose courses to fill the void in your abilities. If you have a minor weakness or need to revisit topics you haven’t seen, see a credential or two.
您的学士学位或以下，通常回到学校。 你肯定会回到学校。 易于使用的选择为学生提供了多种强大的服务。 选择一个您要处理的项目，以便坚持下去。 您可能会通过教学来支持新老板。 选择课程以填补您的能力空白。 如果您的弱项较小，或者需要重新访问未曾见过的主题，请查看一两个证书。
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Technology boot camps are perfect refrigerators if for a few years you‘re out of computing. No matter how broad your skills are, move along a career path with your boss that leads you to a job in data science or machine learning. If you don’t give one from your current business, look for something you can. It would be way better to move inside your new boss than learn all the skills to make that huge leap.
如果几年之后您都无法使用计算机，那么技术新手训练营将是完美的冰箱。 无论您的技能有多广泛，都可以与老板一起走上职业道路，从而带领您从事数据科学或机器学习的工作。 如果您不从当前的业务中拿出任何东西，请寻找可以的东西。 比起学习所有技能来实现这一巨大飞跃，在新老板内部进驻将是更好的方法。
Do not let age, skills deficit, or your present work hinder you if you want to make a transition. You will also have a 20 + year machine-learning career after you stop with school whether you are 40 years old and have not had a quantitative improvement over Algebra. You will be good as long as you have criteria and interest from the first part.
如果要过渡，不要让年龄，技能不足或当前的工作阻碍您。 无论您40岁，还没有对代数进行定量改进，在您停学后，您还将拥有20年以上的机器学习生涯。 只要您从第一部分开始就有标准和兴趣就可以了。
What did I miss? What did I miss? Have you any questions that I have not answered? Will you have tips on career paths? In the comment line, add them.
我错过了什么？ 我错过了什么？ 您有我未回答的问题吗？ 您会提供有关职业道路的提示吗？ 在注释行中添加它们。
Career Guide and roadmap for Data Science and Artificial Intelligence &and National & International Internship’s, please refer :
数据科学与人工智能以及国家和国际实习的职业指南和路线图，请参考：
翻译自: https://medium.com/datadriveninvestor/how-to-become-a-data-scientist-no-matter-where-your-career-is-at-now-4d0698abbc50
