Why You Shouldn’t Get a Master’s Degree in Data Science?

…and what to do instead

In recent years, data science master’s programs have emerged, partly because data scientists have become popular as the “sexiest job of the 21st century.” These courses range in cost from $30,000 to $100,000, not including living expenses while studying.

My introduction to data science did not include a formal education. The required skills were self-taught using widely available free or very low-cost materials online. We estimate the total amount spent on paid materials was less than $300.

After recently reviewing resumes and interviewing candidates for data scientist positions, I came to the firm conclusion that a master’s degree in data science is not the best route to enter the field. A master’s degree is a very expensive way to acquire the necessary skills, and it doesn’t really teach you everything you need.

This article will cover the main reasons why I feel that a master’s degree in data science is not a good route to enter the field, and that the self-study route is the better option in most cases. Explain why. I’ll also briefly explain what you should do instead of going to a master’s degree to get a job as a data scientist.

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master’s degree is expensive

As I mentioned earlier, a master’s degree is very expensive. Also, because you need to study for a certain period of time, you may not always have the option of earning a living while working while studying, which can be even more expensive.

If you can afford to pay for your degree and pay for your living at the same time, that’s fine, but the reality is that many people can’t afford that. So either you have to borrow a lot of money to cover your master’s degree, or you simply don’t go for it.

The good news is that a master’s degree isn’t a must to become a data scientist, you can learn everything it takes to become a data scientist without going to a master’s degree, and most importantly, data science. You can become a data scientist without spending a fortune in the process of learning

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A master’s course doesn’t teach you everything you need

Working as a data scientist requires more than just knowledge of statistics, mathematics, programming, and machine learning theory. In fact, being a good performer in a real-world data science team requires many skills that most master’s programs don’t teach.

MScs don’t teach you how to use Github or why it’s so important for collaborating in teams with data engineers and software engineers as well as other data scientists. Nor does it teach Agile, which many data science teams adopt . It also doesn’t teach important soft skills such as communication, creativity, and business acumen that are crucial to a career in data science.

I’ve interviewed many candidates with master’s degrees in data science, but few possessed these skills that many companies value. These skills are almost guaranteed to come from learning data science on your own.

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There are various fields in data science

To be a successful data scientist, you need skills in multiple disciplines, including mathematics, statistics, and computer science. In my opinion, a master’s degree in data science will find it difficult to teach skills in all these areas in depth enough. Taking a year or two of a master’s degree isn’t going to prepare you enough to land a job as a data scientist.

Data science is a conglomeration of disciplines and takes years to master. Self-studying or getting a job in a related field (discussed below) will give you the deep knowledge you really need in fields other than mathematics and statistics.

I’m not against getting a master’s degree at all, in fact I think it’s the best way to get a deeper knowledge in your field. Personally, I recommend getting a master’s degree in one of the data science fields, for example computer science or statistics. In doing so, you will gain valuable knowledge in this area. It will also be a great foundation for learning the rest of data science.

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what should i do instead?

As mentioned above, my personal opinion is that you can learn everything you need to know about data science online for free or very cheaply. Studying outside of the master’s program also has the added benefit of allowing you to study at a pace that suits your situation. If you have a full-time job or other responsibilities outside of your studies, you can adapt your learning and spend the time necessary to develop your skills.

Moreover, self-studying allows you to study in a way that suits you. Personally, I am best at hands-on learning, so I took a project-based approach to learning from day one. This approach would not have been possible had I gone on to a master’s program.

In addition to self-study, I strongly recommend that you gain practical experience using data and software in the actual field. This approach can be achieved through internships, volunteering, hackathons, contributing to open source projects, and taking on closely related jobs such as business insights and analytics. In this way, you will be exposed to the realities of working with data and technology, gain empirical experience, and eventually get a job as a data scientist.

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This article is based on my own experience studying data science and personally interviewing candidates for data science jobs. From these experiences, I learned that demonstrable skills, such as project portfolios and relevant work experience, are much more important than certificates and qualifications in order to actually get a job in this field. (*translation note 3).

That’s why I recommend learning data science on your own and getting hands-on experience. What’s especially great about this method is that anyone can get into the field and financial issues aren’t a barrier to entry into data science.

If you want to learn more about how to learn data science online and for free, or how to find resources, I wrote a comprehensive guide earlier, so check out the articles below.