August 12th, 2019
It’s no secret that data science is a popular career right now. Harvard Business review declared it the Sexiest Job of the 21st Century and Glassdoor.com ranked it the top job in America 4 years in a row. Basically, its a great time to become a data scientist.
But how does someone become a data scientist? Truth be told, there are a lot of different ways to become a data scientist. With a job on everyone’s list, the competition is tough. One way to set yourself apart from other candidates is to get a graduate degree. According to a Burtch Works survey, 91% of data scientists have at least a masters degree. Although the percent of data scientists with graduate degrees appears to be decreasing slightly, it is still a valuable asset to have when trying to break into the field.
If you decide graduate school is the route for you, there are some things you should consider before choosing a program. These steps will help you choose a program that gets you the best value for your time and money.
1. Price
The first thing to consider is price. How much will the program cost when all is said and done? Tuition and fees are typically the largest expenses in a graduate school. Tuition for data science programs can range anywhere from 15-95k. However, most programs are in the 35-45k range.
Does the school offer graduate scholarships that you are a candidate for? If so, apply early so that you can take that into consideration before making a final decision.
Make sure to also include the cost of books, materials, and software as these things can quickly add up. If you are not able to find this information on the program website, reach out to the program chair or an alumni of the program via social media.
Another thing to think about is the cost of applying to the school. Do they require you to take the GRE, send official transcripts, or pay an application fee? Since these expenses are not covered by student loans, they are important to keep in mind. A $100 application fee should not keep you from applying to your top school, but it may be smart to apply to schools with lower application fees all else being equal.
Make sure to ask your current employer about tuition reimbursement options. Most companies have such programs, so it is a good idea to understand their policies. Watch out for clauses that require you to stay at the company a certain number of years to avoid payback. This is really important because you will likely be looking for a new job as soon as you finish your degree.
2. Curriculum
When it comes to a masters degree, the curriculum is vital. In my opinion it is the most important factor when choosing a program. The goal of getting the degree is to help you aquire skills to make you valuable to a company. So, what skills do you want/need to learn? This step will probably be the most time consuming because you will need to do a lot of research.
Look at job postings that you would like to have and see what skills are required. Then look for programs that focus on those skills in the curriculum. This is one step that many people overlook. If every other box is checked, but the curriculum is not what you want, it should be a deal-breaker.
3. Degree Name
The third thing to think about is the name of the degree. While most degree titles will not make a difference, it is important to know what employers are looking for. Most data science job postings have wording along the lines of “M.S. in engineering, statistics, mathematics, computer science, or related scientific field”.
With that in mind, your undergraduate degree may impact what you want to focus on in your graduate program. If you have a B.S. in statistics, you may want to study computer science in your masters program or vice versa. This will show future employers that you have both math and programming skills.
Other degree names include Business Analytics, Data Analytics, Analytics, Information Systems, Data Science, etc.
I think you get the idea.
4. Delivery
Another important thing to consider is course delivery. Is the program available online or in-class only? One benefit of online programs, is the convenience. You can attend class from the comfort of your home without traveling to a university. This is especially important if you are working while attending school. Even if there is a university 20 minutes from your home, that is time that you could spend studying if you are in an online program.
Although online programs are more convenient, you miss out on several benefits that you get from in-class programs. Some of these benefits include networking and relationship building. Graduate school is a great time to build connections and collaborate on projects. While not impossible online, you cannot beat face-to-face interaction.
5. Time
The time it takes to complete the degree is an important factor in your decision. How long does it take to complete the program from beginning to end? Some programs are 2 years with summers off, other programs are full-time for 12 months straight. Other programs are 18 months online, but allow you to keep working. Depending on your situation, one option may be better than another, but know that there are options.
Another thing related to time is when you can start the program. Some programs only start in the Fall and others start every 6 weeks. The flexibility of start times may have an impact, especially if you are looking to make a career change as soon as possible. For example, if your top program does not enroll new students until 10 months in the future, you may be better off choosing your next option and starting sooner.
Conclusion
There is a lot to consider when choosing a masters degree in data science. Price, deilivery, degree name, curriculum, and time are all important factors when making your decision. If you are in the market for a shiny new degree, make sure you take these things into consideration.