If you’re considering data science as a career, you would probably be wondering what kind of academic background you need to be successful. There’s a lot of discussion surrounding this question, but it really all comes down to the fact of what exactly you’re looking to do within the world of data science.
It is definitely not required to have either a Master’s or a Ph.D. to become a successful data scientist. A Ph.D. is not necessary, but it can come in handy depending on the field you’re looking to go into. The same goes for having a master’s degree; therefore, it really depends on the individual.
In this article, we’ll explore what an aspiring data scientist actually needs, and whether a Master’s or Ph.D. degree is worth it for those who want to start or continue their career in data science. If you’re interested in learning more about these topics, I am sure you will find the following sections of this article very interesting.
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Do Data Scientists Need a Ph.D.?
As we mentioned above, a Ph.D. is not necessary for those looking to pursue a career in data science. When it comes to deciding on whether a Ph.D. is right for you, there are a few factors you should consider before putting the work in to receive this degree.
Questions you should ask yourself should include:
- What kind of work do you want to do in this field?
- How long are you willing to spend at an institution to gain a Ph.D.?
- How passionate are you about data science?
- Does the kind of work you’re interested in require a Ph.D.?
Asking yourself these kinds of questions is the key to understand what the best path for you is. While obtaining a Ph.D. is not necessary, it can help down the line if you feel like that’s the right choice for you.
Below, we’ll look more in-depth into the questions we asked above, and how answering these questions is vital to understanding the best course of action for you as a student and as a data science professional.
Figure Out What Kind of Work You’d Like to Do Within This Field
If you’re considering working towards a Ph.D. in data science, there are a few factors you should look into first. For one, what kind of work are you interested in pursuing after you complete your education?
Are you leaning more towards business or academia in terms of where you want to be after you graduate? If you’re looking into staying within the confines of academics and continuing on with your research, a Ph.D. may be the right move for you.
If you’re considering starting your own business after graduation, something like a Ph.D. may not have too much value to you if those are your plans. The same goes for those who are looking into work within the corporate field. Consider whether spending time on a Ph.D. is necessary for the specific field you’re looking to explore and go into.
This is just one of the many factors you should be mindful of before deciding whether you’ll pursue your education that far.
Figure Out How Long You Want to Stay in School
Another factor you’ll want to be mindful of when considering going after your Ph.D. is how long you’d like to spend pursuing your education.
In both the U.S and Canada, a Ph.D. can take anywhere from 4-8 years to complete. It’s also important to note that the world of data science moves very fast. Even some of the material that your grad school may be teaching you is likely to be outdated.
Once you’ve completed school, it’s also likely that you’ll still have some catching up to do with the latest industry best practices in the world of data science. This can definitely be a setback of remaining in school for so long, thus this is another important factor that you need to consider before making your final decision.
Furthermore, employers will be looking for those who are up to date on all the current data science techniques, so you’ll want to ensure that the knowledge you have is up to date and sharp. While sticking it out for the long haul in terms of receiving your Ph.D. may be worthwhile to you, it’s also vital to remember that it’s a risky bet that may hurt you in the short run.
Figure Out How Passionate You Are About Data Science
If you choose to work towards your Ph.D., you should really consider how passionate you are about data science. This is because if you plan to pursue your Ph.D., you’ll most likely be spending the majority of your career working within this domain.
If you’re excited to engage more within the world of data science, this might be the right path for you. If you can see yourself to stay motivated enough to spend a large amount of time within the world of data science, going after your Ph.D. might be for you.
At the end of the day, it really depends on what feels most appropriate to you. As we’ve mentioned above, a Ph.D. is not a necessity within the fast-moving world of data science, but it really depends on what’s best for you and your academic pursuits.
Figure Out if You Really Need a Ph.D. for the Kind of Work You’re Going Into
As briefly mentioned earlier in the article, you should explore where you’d like to be after graduation and then assess if a Ph.D. is really needed to achieve your envisioned goals.
Do you plan to continue your research within academia, or do you plan to start your own business? Considering factors like these will allow you to save yourself some time and frustration.
Even though you may not need a Ph.D., taking the time to get involved within certain academic programs that many universities or online academies are now offering is a great way to educate yourself on data science. For some additional perspective, while many Ph.D. researchers who specialize in data science acknowledge that they didn’t need a Ph.D. to become knowledgeable about data science, they are thankful they pursued the degree.
At the end of the day, it really comes down to how much knowledge you already have about data science. It also depends on where you’d like to end up after graduation, so these are all components to consider when contemplating pursuing a Ph.D.
Do Data Scientists Need a Master’s Degree?
Now that we’ve discussed whether data scientists need a Ph.D. to be successful or not. Let’s dive more into whether a master’s degree is needed.
This may surprise some, but a master’s degree is not necessarily a must-have for data scientists either. That being said, 88% of data scientists have received a master’s degree within their field, compared to 46% that have a Ph.D.
While it may not be necessary to earn a master’s to become a knowledgeable data scientist, it can be very beneficial to those looking to grow more knowledgeable in the field of data science.
Below, we’ll explore some factors that you may want to consider before pursuing a master’s. If any of the following resonates with you, you may want to consider going after a master’s degree.
You Have a Non-STEM Background
If you have little to no experience working with STEM, you may want to consider going on to pursue your master’s degree. If your background in STEM is strong, and you feel confident in the amount of knowledge you have, a master’s degree might not be the best route for you.
At this same time, it really boils down to preference, and whether choosing to pursue a higher form of education or not feels right to you. If you specialized in any kind of math, physics, or computer science while working as an undergrad student, you likely have all the tools you need to become successful on your own without going on to receive your master’s.
Still, even if you have a stronger STEM background, it can still be beneficial to continue your educational journey and receive your master’s. If nothing else, it will at least make your profile more appealing to recruiters and open numerous career gateways for you.
You Don’t Have a Background in Coding
If your undergraduate experience didn’t involve coding, or perhaps you have very little experience within the coding realm, you may want to consider pursuing a Master’s.
It’s also helpful if you’ve never worked within a profession that requires a large amount of coding. Working towards your master’s will give you the tools you need to be successful, as well as access to programs that will allow you to code successfully.
While you could definitely educate yourself on your own time in terms of coding and how to use different programs and tools successfully, going after a master’s degree and furthering your education will definitely help. Pursuing formal education in data science will definitely make grasping coding concepts relatively easier for you.
You’re Not a Strong Solo Learner
As we stated above, there are plenty of online courses and programs you can involve yourself in if you’re looking to strengthen your knowledge surrounding data science.
While this may be the case, and you can easily educate yourself within the realm of data science on your own, some may not feel comfortable learning certain complex concepts and systems on their own.
Deciding to target a master’s degree may be the right move for those who don’t consider them strong solo learners. If this resonates with you, you may want to further your education within a formal classroom setting.
These are just some of the factors you may want to consider if you’re looking to obtain a master’s degree. If you feel as if you lack within certain fields such as STEM or coding, you should take the extra steps to educate yourself further on these topics.
Do Data Scientists Need an Undergrad Degree?
Unlike the first two degrees discussed in this article, an undergrad degree is necessary to become a successful data scientist.
While it’s not just the knowledge that you’ll consume as an undergrad that will be beneficial to you, but the degree itself will also help you land jobs more easily. Unfortunately, there’s a lot of stigma surrounding the concept of those who are self-taught within many scientific fields, so it’s important to have an undergrad degree to back up your claims.
While there are plenty of online courses that can give you all the information you need to be successful, it’s helpful to have an official degree that future employers can see for you to be seen as a respected data scientist.
While it’s worth obtaining an undergrad degree, you should also brush up on your hard and soft skills while furthering your education through formal teachings.
Below, we’ll explore some of the skills you may want to strengthen and brush up on while you’re working towards receiving an undergrad degree.
Strengthen Your Communication Skills
Whether you’re working with other scientists in the classroom or in a business setting, how well you’re able to communicate with others is crucial to working with a team successfully.
When you know how to communicate effectively, you’re more likely to get the best results while working with others. Effective communication also comes into play when you’re trying to convey your results with a group of people properly.
Having proper communication skills shouldn’t be overlooked, seeing how it’s a useful trait that will help you no matter where you are in life. Correctly communicating your findings will enable you to work well with others, solve problems, and get things done quicker.
Become a Strong Critical Thinker
Being able to think critically as a scientist is just as important as being able to communicate proficiently. Brushing up your critical thinking skills is key to forming hypotheses, asking the right questions, and coming to the correct conclusions.
Having strong critical thinking skills is key to being a successful data scientist. Not only are you able to seek out problems that you want answers to, but you’re also able to see a problem from many different angles.
Looking at different science components from multiple different viewpoints and perspectives allows you to grow as a scientist while also strengthening your critical thinking skills.
Now that we’ve discussed some non-technical skills that undergrads should brush up on, below, we’ll explore some crucial technical skills to have as a data scientist.
Be Able to Effectively Present Data
Being able to gather effectively and model data is crucial to becoming a successful data scientist. There are plenty of online courses and programs you can be involved in if you’re looking to strengthen your understanding of presenting research to an audience correctly.
While your research may be excellent, if you’re unaware of how to present your findings effectively, you won’t be able to share your results with others successfully.
Taking the time to learn how to present large amounts of data to your audience for them to digest your research effectively is key to working as a data scientist.
Strengthen Your Knowledge of Math and Statistics
Both math and statistics play a large role within data science, so it’s vital that you keep your knowledge of these skills sharp to remain successful.
Both of these subjects will allow you to form credible hypotheses while also allowing you to form detailed and thorough conclusions within your research. While factors such as coding and data presentation are quite important, you should keep your math and statistical knowledge sharp to solve problems and ask the right questions.
Is a Ph.D. in Data Science Worth It?
In this article, we’ve discussed numerous factors within the world of higher education, data science, and what’s required of data scientists.
Previously, we mentioned that while data scientists don’t necessarily need a Ph.D., there are plenty of pros and cons to furthering your education up to this point. At the end of the day, it depends on what career path you’re looking to go into after graduation, as well as any other personal preference you have.
But if you choose to obtain a Ph.D. in data science, is it really worth it? Harvard graduate and Ph.D. research Matthew Stewart definitely has an opinion on the subject.
While Stewart does believe that he could have obtained most of the knowledge he has about data science on his own through self-teaching, he’s glad he went on to receive his Ph.D. in data science.
Pursuing his education in a formal classroom setting gave him experiences that he would not have had access to on his own. He also claims a lot of the more complex projects and research he was a part of, allowing him to grow as a scientist, and he can’t imagine working with some of those components on his own as a beginner.
Stewart also insists that if you’re interested in data science as a beginner, dive into the many online classes and programs offered by many different universities.
While there are many differing opinions out there regarding whether or not receiving a Ph.D. in data science is worth it, you may take it from a Ph.D. researcher who doesn’t regret his time working towards his Ph.D. at Harvard.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
In this article, we discussed what exactly data scientists need to be successful. While it may come as a surprise, you don’t need a Ph.D. or a master’s to become a data scientist.
We went over the pros and cons of receiving both of these degrees, and how it ultimately comes down to the amount of knowledge you already have, as well as the career path you see yourself gravitating towards after graduation.
Furthermore, in this article, we also discussed if a Ph.D. in data science is worth it. While the answer to this question would vary based on your individual needs and career goals, at a high level we concluded the abundance of tools and diversified experiences that a Ph.D. can offer definitely makes it worthwhile in the long run.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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