Your online masters in data science can seem like a great option, especially if you’re already working full-time and don’t have the time to attend classes in person at your local university.
However, there are certain mistakes that many people make when they start an online data science master’s degree, most of which can be avoided by choosing a program carefully.
This article will look at the top 10 mistakes people make when they start an online data science master’s degree, including some crucial pieces of advice on how to avoid these issues and ensure your success in this new program.
1) Not knowing what you want
When deciding to get a data science master’s, be sure you know what it is you want to get out of it. Is your ultimate goal a job in industry or research? What company or lab are you hoping to work for after graduation?
Your ultimate end-goal will dictate how you approach your online data science master’s program—you may wish to focus on machine learning, for example, if your dream is to work for Google Brain.
Whatever you choose, though, try and pick something that sounds exciting as opposed to just practical.
2) Choosing the wrong school
It’s one of the biggest mistakes you can make as someone looking to go back to school for a data science master’s degree. Be sure to carefully research each school, both in its curriculum and reputation, before applying. Weigh factors like graduation rates and employment data, as well as cost.
And don’t be afraid to ask questions—the last thing you want is a piece of paper without a job at the end of it!
Although not all programs are created equal, there are many reputable options out there that offer excellent opportunities for career growth. Make sure you choose wisely!
3) Choosing the wrong program
Many programs are available, but not all of them will be right for you. Some schools offer part-time or evening classes; others offer weekend classes.
And while working with your school to develop a flexible learning plan might be ideal, you may also have other obligations and responsibilities (such as family) that come into play.
Take some time to research your options so that you can choose a program that’s convenient for you—and provides exactly what you need to get ahead in data science!
4) Getting confused about the curriculum
The curriculum can vary a lot between schools, but it should be easy to find on their websites. Some programs require you to take a few core courses before you can begin your thesis project.
Others leave it up to you to figure out how to balance reading research papers and working on projects. If your program has these requirements, stick with them!
Starting early helps you learn what’s important (and not) in data science as well as help build relationships with instructors and mentors who are willing to review your work for free because of that early start date.
5) Not having a goal in mind
If you’re going to be investing in something as expensive as a data science master’s degree, you should have goals in mind.
By having goals, it will help keep you on track and more focused on what it is that you want. If your goal is just to learn everything possible about data science, then maybe a certificate or a shorter program may be a better choice for you.
A data science master’s program typically takes around two years and if your ultimate goal is to become an employee of Google or Facebook then you probably need to invest in full-time coursework versus self-study.
6) Thinking it will be easy
Starting an online data science master’s program can be tricky. While learning from home is a great way to keep costs down, as well as your work-life balance, it isn’t for everyone.
Make sure you have a good grasp on how much time each assignment or project will take before signing up. The last thing you want is to realize that a couple assignments or projects are going to require more time than you anticipated and then having to rush through them in order to finish.
Rushing through assignments often leads to mistakes and doing poorly on those assignments and getting grades you aren’t happy with.
7) Taking unrelated electives
If you’re looking to land a job as a data scientist, you’ll need to be proficient in math and computer science (preferably Python or R) and have strong quantitative skills.
Taking courses in everything from history to French literature won’t help your cause. Instead, focus on taking math and computer science courses.
If you want to take something that isn’t data-related, make sure it has practical applications for what you want to do after graduation—and include it on your resume if possible.
8) Thinking it’s too expensive to switch schools later
This mistake is probably one of the most common, because it’s entirely human to have fear of change. But in data science, a changing world is your friend.
Many people think that to learn a new field, you have to go back to school for years or get a Ph.D., said Jack Nasar, co-founder and president of Data Incubator. But you can actually learn what you need in about three months if you’re willing to put in real effort and not just rely on lectures or articles from other sources.
What’s more, many schools are offering scholarships and grants specifically for data science students—so look into them before thinking it’s too expensive to switch schools later!
9) Not applying for scholarships / financial aid early enough
It’s important to apply for scholarships as early as possible so you have more time to work on applications. Students who begin applying in June will typically have more success than those who wait until January to apply.
Many schools recommend that students apply for financial aid one year before their expected enrollment date, so even if you’re unsure of your enrollment dates, it can’t hurt to get a head start.
Most students don’t realize that it can take up to two months for schools to process your scholarship application; plan accordingly! Missing due dates will slow down or disqualify your application altogether.
10) Waiting until you have all your credentials before starting applications
You don’t need your degree, or a certain number of credit hours under your belt before you can apply to a school.
In fact, there are some advantages to applying early on in your academic career, before you have all your credits and all of your credentials lined up.
Applying for admissions early has some benefits: You can take courses that may help build up those credentials (like statistics and R programming), or build experience by volunteering for research projects, for example.
And with programs like Thankfulness part-time data science master’s program, you don’t even have to quit your job to do it!