Data Analytics MS FAQ

This page contains information for students in the Master of Science in Data Analytics program at George Washington University.

Important: for student who started BEFORE Fall 2022: This page reflects the new DA MS course requirements for students starting in Fall 2022 or later. If you are on the old program, then you should check this Pre-2022 DA MS FAQ.

Frequently Asked Questions

Can you help me understand the overall degree requirements?

The full requirements in the Bulletin are:

  • Complete the five Required Courses
  • Complete two Required Track courses for your focus area (CSCI 6212 and CSCI 6364 or EMSE 6575 and EMSE 6577)
  • Complete one more Elective course in your track’s department at the 6000+ level
  • Complete two additional electives. These can be 6000+ courses from any department, but if they are outside of CS or EMSE, then you need to get approval from your advisor first.

Do I need to take CSCI 6362 Probability for Computer Science as a Prerequisite for CSCI 6364 Machine Learning?

No. This is not required as long as you have a statistics/probability course at the undergraduate level. Taking EMSE 6765 Data Analysis for Engineers and Scientists will also meet this need.

Can you help me choose electives?

In general, any 6000 level course should generally be able to be counted for one of your two unrestricted electives. To find courses you may be interested in, check these resources:

  • View the schedule of classes to see what will actually be offered in the semester you are interested in. Not all courses are offered every semester, and this is the best way to know what will actually be offered.
  • View this Google sheet of common electives. While this list is not a comprehensive list of all electives, it does represent courses that DA students have taken in the past. It is a good list to start with when searching for electives, and it is continuously updated.

How many courses do I need in my focus area (CS or EMSE)?

You must take at least 3 courses in the track you have selected – two are required and the third is any of your choice from that department.

Can I take course X which is not listed in the degree requirements?

If it is a 6000 level course, then generally yes it can be one of your two unrestricted electives. You should contact your advisor for approval first.

Yes, but you will need to fill out the CS Department’s Registration help form. Contact your advisor or cs@gwu.edu for more information.

Can the SEAS 6402 DA Capstone course only be taken in Spring semesters?

The course is typically only offered in the spring, though some years we also offer a smaller fall section. You should not take 6402 until you have completed the other four required courses. If you start the program in Spring, then we recommend taking it in your 3rd semester (i.e., your second spring, a semester before graduation). If you are not able to take the course at the normal time, you should consult with your advisor before your last semester about completing the capstone as an independent project.

I want to register for a 6000 level course, but I’m restricted, how do I register?

Some courses have restrictions on who can register for a variety of reasons. Often times the restriction is due to missing prerequisite courses or the course is only offered to majors in that field (e.g. most courses in DATS are restricted to DATS majors). The best course of action depends on the specific situation. Here is some guidance:

In general, if there are prerequisite issues preventing you from registering, also explain in your email why you should be exempt from the prerequisite (e.g. you have prior experience or have taken a similar course, etc.). There are no guarantees that you will be allowed to enroll in the course, but following these steps is the best approach.

I’m interested in becoming a Teaching Assistant or Research Assistant, how can I apply for one of these positions?

GTA (teaching assistantship) positions are limited and typically offered to students later in the program (e.g. year 2) to GTA for a course they’ve already taken in their first year. There are not many of these available, and many are prioritized for PhD students over MS students since PhDs have longer tenures in SEAS (4-5 years) and because many will be in academic settings in their careers and therefore need more teaching experience. The best advice for these positions is to do an excellent job in the classroom and discuss with each faculty member you take a course with that has a GTA to see if you could take on a GTA position in the future. Directly expressing your interest to the faculty is the best thing you can do.

GRA (research assistantship) positions are typically arranged on a faculty-by-faculty basis and usually supported by external research funding. The best way to find opportunities is to reach out to individual faculty and ask if there are potential GRA positions in their lab. It is also best to read up on the faculty member you are reaching out to to make sure their lab focuses on a topic that you are interested in and have something to offer. This isn’t limited to SEAS - you could be a GRA for a professor in a different school, and it is not strange for a DA student to use their skills in other domains, such as public health, the Elliott School, etc. Again, directly reaching out to faculty members is the best advise for obtaining one of these positions.

I am looking for an internship or job in Data Analytics / Data Science, what should I do?

First, reaching out to SEAS career services is a good idea. And it is also a good idea to get on Linkedin and get your CV / Resume setup so employers can find you.

Beyond these channels, you can also significantly improve your chances of getting a position by networking with people in the DC area. For example, attend some of the Data Science DC meetups. Many of the participants are data professionals in the greater DC area and may know of a company hiring. You can also network online. Linkedin sometimes works, but joining Slack communities can also be a helpful way to connect with people. The R for Data Science community, for example, has a very active Slack that you can join that is open to the public. While it is more focused on R, they often post general jobs that are not language-specific.

Finally, I would also recommend reading the book Build a Career in Data Science. The authors have many great suggestions and answer a lot of important questions about searching for and successfully finding a job in this field.

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