Applying to Graduate School

Common reasons: specialization & expertise; advanced skills & opportunities; broader career opportunities; research experience
Ultimately, there is no one-size-fits-all answer, and the decision should align with your career aspirations, personal preferences, and financial considerations.

Factors to consider:

Money: Masters degrees are not always funded by the department. Most Ph.D. programs provide a stipend and pay for tuition for candidates, contingent upon graduate assistantships.
Time: Master’s programs are much shorter than Ph.D. ones, usually taking no longer than 2 years, vs. an average of 5-7 years for a Ph.D.
Research vs. Industry Focus: Most master’s programs do not have as much of a focus on research as Ph.D. programs do. Master’s
Programs tend to focus more on industry preparation. Some MS programs are thesis-based, however.
Career goals: If being a professor or leading a research group is the long-term goal, then obtaining a Ph.D. is the better path to take. Getting a master’s typically prepares you for industry positions.

Letters of recommendation:
- Most graduate schools require at least 3 letters
- Reach out to individuals who know you fairly well: professors (whom you interacted with often), former supervisors, mentors
- Ask for recommendations at least two months before your applications are due
Mission statement/letter of intent:
- Masters Applications: The focus is more on why you want to pursue the degree, how your coursework/experience translates into the program, and how a master’s would benefit you
- Ph.D. Applications: The focus should be more on research than anything, and how this particular program aligns with your own interests.
- Mention specific faculty and their work, etc.
- Sample here
Additional Material(s):
- Academic Transcript(s)
- Resume/CV
- Application fees & financial aid documents (where applicable)
- GRE Test Scores

Coursework
- Take “core” CS classes such as: Data Structures, Algorithms, Operating Systems, etc.
- Take electives based on your area of interest (ML/AI, Data Science)
- Check with each individual school to ensure that you have the right requirements
- GPA: schools typically prefer a 3.0 or higher, but sometimes 3.5
Graduate Record Examinations (GRE) Preparation
- Most programs require that you take the GRE
- Utilize resources such as The Princeton Review, Barron’s, practice tests, etc.
- International students: TOEFL scores are needed in addition to the GRE ones
Research
- Get involved in research at your institution as an undergraduate, in your area of interest
- Start preparing sooner than you think that you might need to. Most applications close by December!

Do your research into particular programs that interest you; do not just apply based on the name of the school
Use some type of platform for staying organized (ClickUp, Asana, etc.)
Network with professors at your top universities
Resources:

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