- School Location (even if remote)
- Duration of Program
- Type of Specializations
As someone who has researched and applied to many Master’s programs in Data Science, I have come to realize a set of characteristics of what determines if a program is great vs if a program is okay, and in general what to consider. While there are countless things to consider when choosing which Master’s degree you will ultimately complete, I have compiled five of the things I considered when choosing my Master’s degree in Data Science. My hope is that you can also think about these characteristics and apply them to your own search that will lead you to become a professional Data Scientist with a Master’s degree.
This subject may be taboo to discuss as it is more often detailed in a way that pertains to money or personal finances, or how much you have, and how much you are willing to spend on a degree. That being said, I wanted to see more people talk about tuition and how it has impacted them not only before their search, but after their graduate program search. I paid quite a bit for my program, nearly $60,000, while I think this amount is astronomical, I can say that it was an amount I was willing to pay because of my return on investment. What I mean by that, is that with a Master’s in Data Science degree, a new Data Science job thereafter completing the program, and a long career in Data Science, I proved to myself that it was worth the cost since it would provide me pay that I would not have otherwise received. While some companies you interview with or work for will not require a Master’s degree, some will or will put it as a nice-to-have on their job descriptions. It gives the company reassurance that you studied and specialized on something for several months, allowing them to have more trust and faith in your abilities beyond the interview. That being said as well, in no way is a lower tuition a worse investment and vice versa, it is simply something to consider as you make your way to enrolling in your final-picked Master’s degree program in Data Science. Ultimately, you will consider many things, and the tuition and the price of your degree are incredibly important overall.
Here are some of the reasons why you should consider tuition:
- return on investment
- amount of debt you are willing to have for a certain amount of years
- what medium/acceptable amount of tuition you are wanting to spend
- if tuition is more important than the other factors (and more) that I will discuss below
- if your employer is willing to pay your tuition
As you can see, tuition is an important factor in deciding which school you ultimately go to. Do not put yourself through debt if you are not actually going to be a Data Scientist after your program is completed, and rather, look for a cheaper, shorter duration certification in Data Science if you are looking to learn more out of curiosity. However, if you decide on a Master’s program, the school itself and its respective location is also important just like tuition.
A lot of Master’s programs are online nowadays, particularly in Data Science. The current state of The World’s events has made this point even more common. It is critical to consider where the school is located, even if the school is completely online or remote. The reason as to why is because the location of your school tends to be the location of where you will work. When you apply for Data Science jobs, it can look better to have completed a degree at a reputable school in your company’s state or city. For example, I completed my Master’s degree at SMU in Dallas. The program was mostly remote (with some in-person experiences). Because I was applying to jobs in Texas, companies, hiring managers, and recruiters will see a school they are more familiar with. While this does not certainly guarantee more recruiters and managers to approve of you more, it might for some, and that can be worth it. Additionally, if you go to a remote school near where you live, you can make connections and network with others who also went to that same school.
If you are eventually attending a Master’s program in person, then location is of course an important factor in deciding as well. Most people studying for a Master’s degree will currently be working at a job at the same time. That being said, it would be beneficial to have a shorter commute so that you are not spreading yourself too thin.
Here are the main reasons for considering school location:
- reputability of school allowing companies, recruiters, and hiring managers to find you/you may stand out more
- networking with people who went to your school
- shorter commute if you are studying in-person
Whether you are studying in-person, remote/online, or a hybrid of both versions, the school location is an important factor to consider when deciding which Master’s program in Data Science to enroll in. The next thing to consider is the length of your program.
Depending on your current state, you may find this factor especially important in your considerations. The length of a Data Science Master’s program varies greatly. Although a Master’s degree has a traditional sound and expectation of two years to complete, some are quite a bit shorter — even around one year total. You might want to address things like if you have children, if you have wanted to study and work at the same time for up to a year, if you want to have trimesters/summer vacations from studying, etc. The program I did took closer to 2 years and had about 33 credits. It was nice because I had some breaks, but not too many where I was wasting time.
The duration of your program may be influenced by the following:
- if you want a decelerated program instead of the average program length
- some programs also offer accelerated programs if you are in a time-crunch
- if your employer will pay a shorter vs longer program length
- if you have a family and want to choose a decelerated program in order to have fewer classes throughout the week so you have a better study-work-life balance
There are several reasons why the duration of a program is important to consider. I have discussed some of the more pronounced reasons that I have experienced and could be applicable to your situation as well. Once you look deeper into the specific Master’s programs, you will want to consider the types of specialization the program offers as well.
Oftentimes, graduate programs offer more specific specializations that follow under Data Science. You can also expect more unique specializations between different programs. For example, I had the choice to focus more on Business Analytics vs Machine Learning as a specialization. Some programs may not offer a specialization at all so it is important to keep that in mind as well. I chose to focus on Machine Learning, as I wanted to learn more about the common Machine Learning algorithms and how to apply them to business situations, as well as programming with Machine Learning. The Business Analytics approach would be for people who want to be more customer-facing, perhaps in upper-leadership at a company leveraging the knowledge of Data Science to drive their decisions, or people who want to focus less on the code and programming, and more on the insights instead.
Here are some common specializations to look for:
- Data Analytics
- Business Analytics
- Data Engineering
- Machine Learning
- Data Analytics
Depending on what you want to do in your career, specializations can be highly valuable. I would compare the different types and see which is the best fit for you — also, to see which courses follow under each specialization. Sometimes, a specialization can only have one to two courses, while some can be considerably more like five to six. It is almost important to note that some of the programs that do not offer specializations do offer electives that could, in a way, serve as a specialization that is more defined by you than the school. For example, if you find that three of the five electives you take are all advanced statistics courses, then it would be beneficial to highlight that on your resume as a Statistics specialization. Either route that you ultimately take, a specialization is a critical thing to consider when applying to grad programs. That being said, other similar things to consider are the main courses a program offers as well as which elective it offers overall.
To expound upon types of specializations, the courses that you take at a graduate program are also very crucial to consider. The best advice that I can give is to make sure you are not going to take too many general courses in Data Science. Data Science covers several different facets like Statistics and Machine Learning; however, if you are wanting to specialize, or at least have an experience that is more unique and detailed, then you will want to make sure there are several comprehensive courses to enroll in. For example, if you want to study language, make sure there is more than one Natural Language Processing (NLP) course to take so that you are really getting your money’s worth.
One of the more unique features of a graduate program is the ability to complete a capstone project. This project usually composes of multiple credits as you will be researching over two semesters possibly. You will work with a group of classmates, have an advisor that is a professor, and a professional advisor who is an expert in the facet you are writing about. The capstone process can entail a presentation as well as a publication of the paper you will write on — your capstone topic. For example, my capstone was on fake news detection. It is an excellent way to show your future employers how you work with people, what you want to focus on within Data Science, as well as your academic aptitude for Data Science. A capstone project may help you land a job in Data Science since your article will be published and peer-reviewed publicly as well.
Here are some key components to consider when working on your capstone project, as well as the benefits:
- how many people you will be working with
- who you will be working with (just classmates, an advisor from your program, and or an advisor from the professional field?)
- how many credits it is/how much of the program does it compose?
- the duration of the capstone project
- if you need to present your capstone to an audience
- put on your resume
- become an expert in a specific Machine Learning topic
- have an experience that is more than just school and more closely related to real-world experience
As you can see, the capstone part of the graduate program is quite unique (when comparing it to an undergraduate program or certification), and can be unique compared to other programs, so it is vital to examine what you will be getting out of your specific program’s capstone.
In addition to a capstone, graduate programs also offer something called an immersion. This is usually an in-person event where you can meet your classmates and professors for the first time, network with Data Science leaders and professionals, as well as hear and present on your capstone project.
Researching, enrolling, and completing a Master’s of Science in Data Science can be quite overwhelming. However, I hope I was able to shed some light on some of the most useful factors to consider when applying for a Master’s degree. While I have discussed five, there are countless others, so keep in mind that there is more to research. After completing my Master’s degree, I can truly say that the factors that I have considered to be important in deciding on a graduate school in the past and in this article, are still the ones that I would say are relevant now.
To summarize, here are the five things to consider in a Data Science Master’s degree:
TuitionSchool Location (even if remote)Duration of ProgramType of Specializations- coursesCapstone- immersion
I hope you found my article both interesting and useful. Please feel free to comment down below if you have researched Master’s programs and what you found to be the most useful things to consider when applying — and if those held true after you completed your program.
These are my opinions, and I am not affiliated.
Please feel free to check out my profile and other articles, as well as reach out to me on LinkedIn.
Here is my article outlining the top five Data Science certifications , if you would like to learn more about some quicker ways to learn Data Science:
 M.Przybyla, The Top 5 Data Science Certifications, (2020)