Do I need a master's / doctorate degree to become a data scientist?

Updated on : December 3, 2021 by Bryant Prince



Do I need a master's / doctorate degree to become a data scientist?

No, you don't need to, either, but you do need the proper background.

There are tons of free resources for learning data science online, many of which are listed in How can I become a data scientist?

Why Some MS / PhD Graduates May Have Advantages

Candidates exiting certain master's / doctoral programs may have an advantage in data science because at least one of these is true:

Them....

  • conduct research involving programming and large data sets
  • have collected statistical and data insight through their work
  • show resilience in asking / answering difficult questions
  • You can explain the motivations and reasoning behind your work.
  • they are abl
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No, you don't need to, either, but you do need the proper background.

There are tons of free resources for learning data science online, many of which are listed in How can I become a data scientist?

Why Some MS / PhD Graduates May Have Advantages

Candidates exiting certain master's / doctoral programs may have an advantage in data science because at least one of these is true:

Them....

  • conduct research involving programming and large data sets
  • have collected statistical and data insight through their work
  • show resilience in asking / answering difficult questions
  • You can explain the motivations and reasoning behind your work.
  • are able to think critically about difficult problems
  • can learn and adapt quickly

What can you do as a student

As a college student, you can take advantage of your courses, work through the countless resources available for free online, and do your best to land some data science internships / job offers while developing the above skills on your own.

If you can already get data science internships / job offers, then you don't need to get an additional degree to advance your career potential.

Over a fixed period of time, actual data science experience will almost always exceed the time spent in a master's / doctoral program, especially since years of data science experience are still quite rare at the moment.

If you are about to graduate and can't get data science internships / job openings, I would recommend one of the following:

  • get whatever job you can where you can work on developing your skills in data science
  • Join a master's program where you can work on your data science skills.

Then you can move on to data science when you're ready.

Please note that there will be fewer positions available to you if you only have a bachelor's degree

There are quite a few companies that only accept PhD candidates for their data science positions. However, there are still a growing number that will accept candidates with bachelor's degrees for their job openings. Check Which companies have data science internships for college students? o Which tech companies are hiring data science interns for the summer of 2016? for lists.

Why would I recommend getting a master's degree instead of a doctorate

Keep in mind that a PhD program can be a very lengthy undertaking. If your main goal is to jump into data science in 5 years, you would be more prepared to spend 1-2 years in a master's program and 3-4 years in a data-related position than 5 years in a PhD. I would only recommend a PhD program if at least one of the following is true:

  • You've spent hours trying to solve a research problem as you couldn't imagine leaving it unanswered.
  • You are not a US citizen and you need a starting point to immigrate to the US and work here (if so, you can try to enter a doctoral program in the states).

If you are still aiming for a PhD, you need to be very careful that your research aligns very well with data science (for example, it involves heavy data analysis, a lot of programming, not entirely theoretical). Many doctors are ill-prepared for a data science role because they don't have the data analysis / programming / hands-on experience. As a master's candidate, you will have more flexibility to pursue what is most useful for data science.

Why do I expect many more college students to enter data science in the future

As more resources and relevant courses / programs become available to college students in the next 5-10 years, many more entry-level data scientists will come straight out of a bachelor's degree program. Then you will start to hear a lot more about data scientists who started immediately after finishing their bachelor's degree.

I write more about this in Are PhD Data Scientists Better Than Those Who Are Qualified? And Are PhD Data Scientists Better Than Those Who Are Qualified?

It really depends on the role you are looking for. The "data scientist" label is attached to many different job descriptions, some of which doctoral training is more useful than others.

Over the course of a PhD, you are likely to develop a number of skills that are often not emphasized in undergraduate studies (or even at the master's level):

  • Browse Relevant Literature - Finding useful articles and extracting meaning from them is something you'll get a lot of practice at while writing your thesis.
  • Definition of a clear methodological problem to be solved: All theses will have to answer a question, but calculating or
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It really depends on the role you are looking for. The "data scientist" label is attached to many different job descriptions, some of which doctoral training is more useful than others.

Over the course of a PhD, you are likely to develop a number of skills that are often not emphasized in undergraduate studies (or even at the master's level):

  • Browse Relevant Literature - Finding useful articles and extracting meaning from them is something you'll get a lot of practice at while writing your thesis.
  • Define a clear methodological problem to solve: all theses will have to answer a question, but figuring out which question to answer can be one of the most difficult parts of PhD research: you need to find a problem that is interesting, relevant and novel. but it can also be solved in 2 to 5 years by someone with (probably) relatively little research experience.
  • Apply the theory to know when a method is appropriate: There is a great quote that "knowing the difference between strict and lax tolerance is perhaps the most important measure of a craftsman" 1. Theoretical training throughout a PhD helps develop a deeper understanding of why certain methods work and how harmful it is to violate the assumptions of each model. A master's degree also gives you some of this training.

That being said, a PhD generally takes 5 to 7 years after bachelor's (or 3 to 5 years after master's). There are also important skills and attributes that you are more likely to develop during that time in an industrial setting:

  • Take a pragmatic approach to problems - You can't live with the 80/20 rule that long when it comes to academia. A great deal of effort goes into the final polishing of academic work, and as such, doctors tend to lean too far toward perfectionism when they first leave graduate school. In industry, you also often select a method based on the problem, while in academia sometimes (not always) the method comes first.
  • Your Specific Industry Awareness - Deep understanding of the nuances of your chosen industry's issues and demands is (unsurprisingly) something that is best built on the job.
  • Social Skills: Much of the PhD work is solitary or done only with a small number of colleagues. Soft skills like mentoring, meeting effectively, and building consensus are more easily acquired through the most collaborative environments found in the industry.

Unsurprisingly, PhD skills are more important if your goal is a research-oriented data science position that involves developing a novel methodology. It is not to say that PhDs have a monopoly on these skills, but they are difficult to acquire even with the support of an academic department and a thesis advisor, so I would not underestimate the difficulty of acquiring them on your own. It's also worth considering that some companies are also unwilling to hire postgraduates for data science roles, or promote them beyond a certain level on an IQ track.

All that said, there are large swaths of data science that don't require these research-oriented skills. There is a great deal of impact you can have by leveraging skills that are also best developed through industry settings. I recommend looking for job openings for the type of work you want to do and speaking with data scientists with and without graduate degrees to determine which direction is best for you.

Footnotes

1 My Ten Commandments for Makers - Proven

No you don't and let me tell you why.

Just think of the various paths that could lead to a career in data science. You have astronomers who are data scientists (Jessica Kirkpatrick), biologists who are data scientists (Roger Peng), computer scientists who are data scientists (Verena Kaynig-Fittkau), physicists who are data scientists (Shankar Iyer), statisticians who are data scientists. (William Chen) and even mechanical engineers who are data scientists (me: P). See What are the different ways to enter the field of data science?

What do these people possibly have in common besides being data scientists?

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No you don't and let me tell you why.

Just think of the various paths that could lead to a career in data science. You have astronomers who are data scientists (Jessica Kirkpatrick), biologists who are data scientists (Roger Peng), computer scientists who are data scientists (Verena Kaynig-Fittkau), physicists who are data scientists (Shankar Iyer), statisticians who are data scientists. (William Chen) and even mechanical engineers who are data scientists (me: P). See What are the different ways to enter the field of data science?

What do these people have in common besides being data scientists?

They are working in different fields. They know different things. A biologist, astronomer, or physicist would rather have a master's degree in their respective field than a master's degree in data science, and what value could a master's degree in physics add to a data scientist at a company such as Amazon, for example. I say Zilch.

The main part to understand is that these people have acquired the ability to play with data at some point in their career.

Some of them have read about Statistics, Mathematics, Machine Learning not as part of their career, but as something that would be useful in their work. A biologist has gained experience by looking at genetic data, an astronomer by looking at telescope data, and so on.

These skills are what make them important to companies like Amazon, not their master's degrees!

And one could acquire these aforementioned skills by working with industry or working with publicly available data sets.

I can safely bet that all of the people mentioned above would have definitely spent time playing around with the Iris dataset.

And to tell you a secret, you can also do this without MS.

Aside from learning what a database is and why programming languages, earning a computer science degree will do little to prepare you to become a data scientist. The most successful data scientists I know have college degrees in math, physics, or both, and doctorates in math or physics. They don't love data, whatever that meant in another answer. On top of that, the successful data scientists I know have genius-level IQs, 140+ at the very least. Starting salaries far exceed six figures, even with starting salaries, the first year goes up

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Aside from learning what a database is and why programming languages, earning a computer science degree will do little to prepare you to become a data scientist. The most successful data scientists I know have college degrees in math, physics, or both, and doctorates in math or physics. They don't love data, whatever that meant in another answer. On top of that, the successful data scientists I know have genius-level IQs, 140+ at the very least. Starting salaries far exceed six figures, even with starting salaries, the first year raises were almost 20% and received a bonus of 11% at the end of the first year. Yes, you read it correctly, From day one on their first job as a newly hired data scientist, they got all that money and the best of the best was revealed to senior management in that first year. A data scientist learns the data stored by his employer and quickly grasps whether the applications currently in use actually do what management had been told and, if not, correct it or, if so, do it more cash. Everything from learning how to set up your employer's data, quickly grasp the circumstances, analyze the business need, and can create an environment that drives real results for your business partners by giving them a business advantage. As more and more data accumulates, brilliant people are needed who understand mathematics, business problems, They can observe the design and coding used by a predecessor and can devise the approach to get the correct answer. The work of data scientists can make a product or even a company successful. Not everyone who claims to be a data scientist is a data scientist. They may have a data scientist on their business cards, but they usually have other positions and work directly in middle, senior, and senior management. They don't love data. They love the math challenge the business partner presents and the critical thinking to get the answer, whether you support the business partner or not. R is not a programming language, but rather a set of packages that are used for statistical analysis. Consider classes and certification programs that involve big data. R was fine for that, because big data emphasized regression analysis. You can perform regression analysis with Excel. Python is a true programming language used for large batch jobs designed to get the best possible result - information and to get it done quickly. The data scientist then reviews it to determine if the expected or expected result is the one desired by the business partner. Those who only know R often cannot debug the problems they encounter and do not always know if the result is correct. Companies hired statisticians who were comfortable with the well-known R, But they generally lacked the necessary mathematical foundation and the ability to identify how to apply mathematics to the problem, and therefore were unable to explain their results to trading partners. Ever since Harvard and others named it the sexiest career, those now recognized as the true definition of data scientists have the background and technical skills, education and experience and, just as important, the ability to work with patience. and explain to others that they do not and will never understand what they do and how they do it. Having a certificate from Cornell on what was called Big Data in the last 10 years is not what a Data Scientist does. Statistical packages are fine, but they don't have the level of sophistication that those who knew Python and have strong math skills had. Today's best data scientists have PhDs. They know who to think critically and can instinctively explain and defend their position. If you are interested in a great scholarship to learn data science with the ultimate goal of landing a job, you should consider the one offered by Insight. The program only takes Ph.D. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence. Today's best data scientists have PhDs. They know who to think critically and can instinctively explain and defend their position. If you are interested in a great scholarship to learn data science with the ultimate goal of landing a job, you should consider the one offered by Insight. The program only takes Ph.D. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence. Today's best data scientists have PhDs. They know who to think critically and can instinctively explain and defend their position. If you are interested in a great scholarship to learn data science with the ultimate goal of landing a job, you should consider the one offered by Insight. The program only takes Ph.D. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence. They know who to think critically and can instinctively explain and defend their position. If you are interested in a great scholarship to learn data science with the ultimate goal of landing a job, you should consider the one offered by Insight. The program only takes Ph.D. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence. They know who to think critically and can instinctively explain and defend their position. If you are interested in a great scholarship to learn data science with the ultimate goal of landing a job, you should consider the one offered by Insight. The program only takes Ph.D. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence. and trains them on how to make business presentations by developing the skills they gained while working on their thesis and defending them for the degree. You can make a living as a computer scientist, but you can make a lifetime career as a data scientist. Good luck and a warm greeting to all who strive for excellence.

I am a data scientist with a doctorate in clinical epidemiology. During my degree, I took several statistics courses (using STATA software) and a very short data-linking course. I also took a system analysis and design course during my MBA.

My knowledge of STATA was helpful, as was my knowledge of statistics, however I never do complicated statistics. Most of what I do is descriptive (at least at this point). The systems analysis and design course was valuable in understanding how to collect user requirements and the life cycle of a data project.

That being said, almost everything I use now,

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I am a data scientist with a doctorate in clinical epidemiology. During my degree, I took several statistics courses (using STATA software) and a very short data-linking course. I also took a system analysis and design course during my MBA.

My knowledge of STATA was helpful, as was my knowledge of statistics, however I never do complicated statistics. Most of what I do is descriptive (at least at this point). The systems analysis and design course was valuable in understanding how to collect user requirements and the life cycle of a data project.

That said, almost everything I use now I learned on the job. And I made it a point to bring this up to my public health school that I trained in: I believe the courses in data management (including how to document properly), data validation / testing, database design, basic coding (including some SQL), visual analysis and training not only with classic statistical packages, but also with R or Python are necessary to bring practical skills to the job and be competitive. All these things I had to learn on the job since I had no experience with them before.

I also think the question is tricky because the job description for a "data scientist" is probably highly variable. My role includes writing articles and conducting research, for which I believe my graduate experience prepared me.

Of the other three data scientists I work with, one has a Ph.D. in geography. He is our medical cartographer and he is the expert when it comes to working with spatial data, but he also had to learn a lot about data management and data validation / testing on the job. The other two I work with have master's degrees. One in public health and the other in informatics. We all use some of our graduate background in our work, but a large part of the knowledge comes from learning what we needed on the job.

I also think that coding should be mandatory for learning in school these days. Coding is a requirement of many jobs now and I wish I had started learning it long before age 35. You would have acquired the necessary skills much faster if you had had a basic understanding of coding and coding logic.

Short answer: No. I have "only" a BA in economics and am having a very successful career as a data scientist. Three years after graduating I got my first position in data science. It can be done, it just takes a lot of determination and preparation. For example, in college, there was an easy route to bachelor's degree (easy A classes and easier classes in general), but whenever I had the opportunity, I would push myself to take more difficult classes: more math, more statistics, economics of Upper level. , programming. When I graduated, my GPA was solid, but not as high as it would have been otherwise, but it was very

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Short answer: No. I have "only" a BA in economics and am having a very successful career as a data scientist. Three years after graduating, I got my first position in data science. It can be done, it just takes a lot of determination and preparation. For example, in college, there was an easy route to bachelor's degree (easy A classes and easier classes in general), but whenever I had the opportunity, I would push myself to take more difficult classes: more math, more statistics, economics of Upper level. , programming. When I graduated my GPA was solid, but not as high as it would have been otherwise, but I was much better prepared to become a data scientist after a bit of professional experience. Regardless of the title you have, Keep in mind that the reason data scientists are well paid is because the job can be exceptionally difficult. Rewarding, yes, but difficult. It's part of the appeal. So dedicating yourself to whatever education you get is good preparation, as is continuing to apply after graduation.

That said, I do think about getting a master's degree sometimes, but frankly, the opportunity cost is just too high. I am doing as well, or better, than my friends and former colleagues who are also scientists or data analysts and who have master's or doctoral degrees.

You'll come across companies that value the advanced degree too much, but that's to be expected - people are comfortable with having a Ph.D. on staff. After all, even an average Ph.D. will likely be the equivalent of a smart, motivated graduate. It's a reasonable filter, as annoying as it is for those of us who don't have advanced degrees. However, this hurdle is largely overcome once you have a data scientist role or two under your belt. Hands-on experience and success in other data science roles outweigh the advanced degree.

No, you don't need a Ph.D. But it is necessary to have sufficient knowledge of mathematics (specifically probability and statistics) and computer science. These often come with obtaining a graduate degree in the correct field.

Having a graduate degree also gives you the benefit of the doubt when potential recruiters and employers look at your resume. However, as Wenwen points out, the real-world experience is more important.

So why not work for a few years and then decide to pursue a master's or doctorate?

There's also the question of * where * you want to be a data scientist. Some companies / groups will insist on a Master '

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No, you don't need a Ph.D. But it is necessary to have sufficient knowledge of mathematics (specifically probability and statistics) and computer science. These often come with obtaining a graduate degree in the correct field.

Having a graduate degree also gives you the benefit of the doubt when potential recruiters and employers look at your resume. However, as Wenwen points out, the real-world experience is more important.

So why not work for a few years and then decide to pursue a master's or doctorate?

There's also the question of * where * you want to be a data scientist. Some companies / groups will insist on a master's or PhD, but most startups probably won't.

The way one of my managers explained it to me (before I went to get a PhD) is that, for statistics at least - a person with a bachelor's degree can be expected to know what a lot of statistical methods are. A person with a master's degree can be expected to know how to apply them. A person with a Ph.D. can be expected to know how to refer them. While I think it's a pretty crude (and flawed) generalization, many doctors probably don't know much beyond a master's degree in their broader fields, and many people with a bachelor's degree are just as capable as people with more education, that's the opinion of some people and influences the way they hire.

Last night, I was part of a panel discussion aimed at beginning data scientists. It was me and two former Google data scientists. We all agreed that no, you don't need a Ph.D. to become a data scientist. However, the answer to "would that be enough MS + projects / internships?" It's probably "no" too.

What's missing in what you said are job search skills. I'm talking about things like writing an effective resume, optimizing your LinkedIn profile, researching companies, interviewing skills, wowing employers with take-home problem sets, etc. I've spent years looking for work myself and

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Last night, I was part of a panel discussion aimed at beginning data scientists. It was me and two former Google data scientists. We all agreed that no, you don't need a Ph.D. to become a data scientist. However, the answer to "would that be enough MS + projects / internships?" It's probably "no" too.

What's missing in what you said are job search skills. I'm talking about things like writing an effective resume, optimizing your LinkedIn profile, researching companies, interviewing skills, surprising employers who give you sets of problems to take home, etc. I have spent years looking for work and interviewing candidates. And I have found that the vast majority of job seekers do a terrible job at almost all of these.

So an MS + projects / internships + JOB SEARCH SKILLS, yes, that would suffice. But don't underestimate the importance of the latter. If you have a PhD, this is not so critical. But for MS or BS candidates, they will need to know how to best present themselves to compete with those who have a PhD from a top-tier school.

Even though you hear about the high demand for data scientists, employers are still very cautious about who they hire.

Definitely not!

There are two types of data scientists: analyst and engineer roles. The first must know very well statistics / SQL / SciKit / etc, the second - software development / machine learning / etc. You don't need a Ph.D. or a master's degree for that. I hired a lot of data scientists with a bachelor's degree. They did their homework very well.

But sometimes the Research / Data Science position is needed. It requires a thorough knowledge of mathematical / ML theory to conduct some theoretical research or adopt a theory into practice. For example, RecSys: ACM Recommendation Systems published many research articles on recommendations

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Definitely not!

There are two types of data scientists: analyst and engineer roles. The first must know very well statistics / SQL / SciKit / etc, the second - software development / machine learning / etc. You don't need a Ph.D. or a master's degree for that. I hired a lot of data scientists with a bachelor's degree. They did their homework very well.

But sometimes the Research / Data Science position is needed. It requires a thorough knowledge of mathematical / ML theory to conduct some theoretical research or adopt a theory into practice. For example, RecSys - ACM Recomender Systems published many research articles on recommendation algorithms. If someone decided to implement and test them in production, then PhDs / Masters would be very helpful in understanding those jobs in a short period of time.

The short answer is no, you don't need a Ph.D. to be a data scientist.

However, having an advanced degree is probably helpful, as most jobs with a data scientist degree will involve highly technical work, so you need to show that you can understand this level of material. Now if you have on the Research side of data science in an organization this will look a lot more like academic research and I would say the vast majority of people in these positions have PhDs (and probably from top schools too ). But if you get into the more analytical and business decision-making side or even some kind of m

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The short answer is no, you don't need a Ph.D. to be a data scientist.

However, having an advanced degree is probably helpful, as most jobs with a data scientist degree will involve highly technical work, so you need to show that you can understand this level of material. Now if you have on the Research side of data science in an organization this will look a lot more like academic research and I would say the vast majority of people in these positions have PhDs (and probably from top schools too ). But if you get into the more analytical and business decision-making side or even some kind of machine learning product implementation, then a bachelor's and / or master's degree should be fine. However, you need to demonstrate your added value in these activities (projects can be good for this).

Also, I will point out that for the best PhD programs in technical fields, you generally receive a tuition waiver and stipend while studying. Therefore, you should not incur additional debt to obtain a PhD. The stipend should cover enough for you to live (though not in luxury), get rent and food, etc. And if you don't get the stipend / tuition waiver, it probably means you're not qualified for the research-type Data Science positions.

You don't need a master's / doctoral certificate to become a data scientist.

But you need a master's / doctoral level of skills and knowledge that can be applied to the particular role.

Whether you learn it by your own study, on-the-job tutoring, or by other means, grades are just a data point that indicates you've passed a course or researched at that level. Nothing more and nothing less.

The problem is, these things are not always a good indicator of what it takes to excel in a real data science role.

Work environments are often very different from academic ones; there are different

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You don't need a master's / doctoral certificate to become a data scientist.

But you need a master's / doctoral level of skills and knowledge that can be applied to the particular role.

Whether you learn it by your own study, on-the-job tutoring, or by other means, grades are just a data point that indicates you've passed a course or researched at that level. Nothing more and nothing less.

The problem is, these things are not always a good indicator of what it takes to excel in a real data science role.

Work environments are often very different from academic ones - there are different rules, team and company dynamics, budget constraints, tool constraints, business processes, and different flavors of politics.

If you have the right level of skills and can adequately demonstrate it during the hiring process, and can navigate this reality, then you will be successful regardless of your qualification.


Of course, many jobs are also poorly defined, and once you get it, it is unfortunately quite common to find that you are overqualified. And hiring managers only increased the master's / doctorate requirement because they weren't sure what they were looking for.

But that is a different question.

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