I’m sure those that have asked the same question are already aware of the current situation in the job market: companies are generating and collecting more data than ever, and they need people that are equipped with the necessary skills to derive actionable insights from the data to generate more profit.

我敢肯定，那些问过同样问题的人已经了解了就业市场的现状：公司正在产生和收集比以往任何时候都更多的数据，并且他们需要具备必要技能的人员，才能从中获得可行的见解。数据以产生更多利润。

This trend has spurred a series of new master’s programs from universities everywhere to satisfy this demand, and a lot of prospective students are asking the same questions that I was asking few years ago:

这种趋势促使各地的大学推出了一系列新的硕士课程来满足这一需求，许多准学生也在问我几年前提出的相同问题：

Do I need a master’s to be a Data Scientist? Is it worth the time and money?

我需要 成为数据科学家的主人？ 这值得吗 时间和金钱？

I’ve received a number of messages from prospective students asking similar questions, and my short, biased answer would be that yes, it’s worth the time and money, and it definitely helped me with my career. In reality though, it really depends on the individual.

我收到了一些来自准学生的类似问题的信息，而我的简短而有偏见的答案是，是的，这是值得的，这是值得的，而且值得，这对我的职业生涯绝对有帮助。 但是实际上，它实际上取决于个人。

我自己的经验 (My own experience)

I graduated with a maths degree in 2016 without any knowledge of data science, and I applied for analyst jobs through a graduate recruitment agency.

我于2016年获得数学学位，但对数据科学一无所知，并通过一家毕业生招聘公司申请了分析师职位。

Once I got a job as an analyst, I realised there’s a lot of things a maths degree doesn’t teach you, both technically and socially.

当我获得分析师职位后，我意识到数学学位在技术上和社交上都不会教您很多事情。

Basic things like querying a SQL database, or creating simple data visualisations and then communicating the results to stakeholders were things that I struggled a lot with at the start.

我一开始就很努力地进行诸如查询SQL数据库或创建简单的数据可视化然后将结果传达给涉众的基本工作。

As I became more comfortable being an analyst, I began learning about data science and machine learning. It was during that time when I realised I lacked the technical skill and experience when it came to programming in general, and especially in Python.

随着我逐渐成为一名分析师，我开始学习数据科学和机器学习。 正是在那段时间里，我意识到我通常在编程方面(尤其是在Python中)缺乏技术技能和经验。

I wanted to learn more about the computer science aspect of data science, but it was really hard for me to stay disciplined whilst working a full-time job. I decided that I had three options:

我想了解有关数据科学的计算机科学方面的更多信息，但是对我来说，全职工作确实很难保持纪律。 我决定有三种选择：

• quit my job, take a break, and then start a data science master’s;辞掉工作，休息一下，然后开始攻读数据科学硕士学位。
• continue studying whilst working as an analyst, and then try to apply for a Data Scientist role at another company; or在担任分析师的同时继续学习，然后尝试在另一家公司申请数据科学家职位； 要么
• continue studying whilst working as an analyst, and hope that I eventually get promoted to a Data Scientist role.在担任分析师的同时继续学习，并希望我最终被提升为数据科学家一职。

The last two options felt unrealistic for me. I lacked both the experience and technical skill, and finding the discipline to stay on track with self-studying was hard. Plus, there’s no guarantee the company could offer such a position in the future.

最后两个选项对我来说并不现实。 我既缺乏经验，又缺乏技术技能，因此很难找到能够自学的学科。 另外，不能保证公司将来会提供这样的职位。

Working as an analyst probably could’ve still provided me the relevant experience required, and I could also develop my technical skills by doing projects on the side. The main problem with this approach was the lack of rigour and structure that I needed to achieve anything significant in a short amount of time.

担任分析师的工作可能仍然可以为我提供所需的相关经验，并且我还可以通过在旁边进行项目来发展自己的技术技能。 这种方法的主要问题是我缺乏在短时间内实现任何重要目标所需要的严格性和结构性。

So rather than work as an analyst for a few years with the hopes of transitioning to a Data Scientist role later, it made the most sense for me to save some money and go with the first choice: devote a year of my time to study something that I was passionate about.

因此，与其作为分析师工作几年而不希望以后再担任数据科学家一职，对我而言，节省一些钱并选择第一选择是最有意义的：花我一年的时间研究某些东西我充满激情的

毕业并担任数据科学家(Graduating and working as a Data Scientist)

I graduated from my master’s a few months before the lockdown happened in the UK. During those first few months, I was having little success with my job search and the lockdown made the situation worse.

我在英国锁定之前几个月就从我的硕士毕业。 在最初的几个月中，我的求职工作几乎没有成功，而封锁使情况变得更糟。

During that time, I decided to take a step back and re-evaluate the skills and projects that I had to offer which would make me stand out. Despite lacking in work experience, I was always glad I had the data science master’s as a starting point for my CV.

在这段时间里，我决定退后一步，重新评估我必须提供的技能和项目，这会让我脱颖而出。 尽管缺乏工作经验，但我总是很高兴我以数据科学硕士作为我的简历的起点。

With the master’s as an addition to the little work experience that I already had, I didn’t feel as underqualified for some of the job postings anymore, which was good for getting passed the initial job requirements. It also gave me plenty of projects to talk about, and provided a much stronger foundation for me to work on my own projects that I would’ve struggled significantly with before I started the master’s.

有了硕士，再加上我已有的少量工作经验，我不再觉得某些职位空缺，这对满足最初的工作要求很有帮助。 这也给了我很多可以讨论的项目，并为我从事自己的项目打下了坚实的基础，而在我开始硕士项目之前，我本该要进行大量工作。

For example, I worked on a computer vision project during lockdown to improve my data processing and machine learning knowledge which I definitely wouldn’t have had the confidence to start and finish a few years ago.

例如，我在锁定期间从事计算机视觉项目的工作，以改善我的数据处理和机器学习知识，而我肯定在几年前没有信心开始和完成这项工作。

And I think it’s that confidence which had the biggest impact to me. The structure of a master’s really forced me to learn a lot in a very short amount of time, and it provided me a foundation that I felt was previously lacking for me to quickly progress into being a successful data scientist.

我认为正是这种信心对我影响最大。 硕士的结构确实迫使我在很短的时间内学到很多东西，并且它为我提供了一个我以前缺乏的基础，而我以前缺乏快速发展成为一名成功的数据科学家的基础。

In August, I finally landed a job as a data scientist, which I think would’ve been difficult for me to get without the experience and qualification gained from the master’s degree. The portfolio that I built, the confidence in my own data science skillset, and the understanding of what companies were looking for in a data scientist really helped with my job search during the summer.

8月，我终于找到了一名数据科学家的工作，我认为如果没有从硕士学位获得的经验和资格证书，这对我来说很难。 在暑假期间，我建立的投资组合，对自己的数据科学技能的信心以及对公司在数据科学家中寻找的人的了解确实对我的求职有所帮助。

判决 (Verdict)

With all that being said, I still don’t think someone needs a data science master’s to learn what I’ve learnt. Almost everything is available online, and if you have the motivation and discipline to study and finish meaningful projects on your own, then you can build a portfolio of the same quality (whilst saving money too).

综上所述，我仍然认为没有人需要数据科学硕士来学习我所学到的东西。 几乎所有内容都可以在线获得，如果您有动力和纪律独自学习并完成有意义的项目，那么您可以构建相同质量的投资组合(也可以省钱)。

Additionally, if you’re already working, then returning to school isn’t easy. Going from financial stability to having no income can be a difficult transition. If you already work in a data-oriented role with a senior position, the experience alone is likely more valuable, and the technical skills that you’re lacking can be gained by doing projects outside of work.

此外，如果您已经在工作，那么返回学校并不容易。 从财务稳定到没有收入可能是一个困难的过渡。 如果您已经担任过具有高级职位的面向数据的角色，则仅凭经验可能会更有价值，并且可以通过在工作以外进行项目来获得您所缺乏的技术技能。

But it does have the potential to fast-track your career to working as a data scientist if you’re in a situation like I was, and I still think it’s one of the best ways to get a solid understanding of what data science is about. If you’re willing to sacrifice the time and money and you’re truly passionate about data science, then I’m fairly certain you won’t regret starting the degree.

但是，如果您处在像我这样的处境中，它的确有潜力快速将您的职业发展为数据科学家，而我仍然认为，这是对数据科学的深刻理解的最佳方法之一。 。 如果您愿意牺牲时间和金钱，并且对数据科学充满热情，那么我可以肯定地说，您不会后悔获得该学位。

An important thing to keep in mind though, is that not all data science masters are created equal. Master’s degrees aren’t cheap these days so it’s important to do your research into what each course offers, and then decide whether the selection of modules (and especially projects) will actually be of any use to you in the future.

但是要记住的重要一点是，并不是所有的数据科学大师都是一样的。 如今，硕士学位的价格并不便宜，因此对每门课程提供的内容进行研究，然后确定模块(尤其是项目)的选择在将来是否对您真正有用，这一点很重要。

Those that have a fair amount of work experience with data will probably do better at this, but I’ve seen many students avoiding harder modules and then regret it later when they need to learn it again at work (e.g. machine learning).

那些在数据方面具有丰富工作经验的人可能会在此方面做得更好，但是我看到许多学生避免使用更困难的模块，然后在以后需要在工作中再次学习(例如机器学习)时感到后悔。

Be smart about the time spent during the year, and I’m sure the investment will be worth it.

谨慎对待一年中的​​时间，我相信这笔投资是值得的。