Admissions and Aid

The M.S. Program in Data Analysis and Visualization at the CUNY Graduate Center welcomes your application. Here, we provide some tips on how to approach your application to the Graduate Center. To begin, consider your professional goals and how our program might serve them. By choosing to study Data Analysis and Visualization in the context of the M.S. program at the CUNY Graduate Center, you are electing to participate as an active learner in a collaborative academic setting. At the Graduate Center, you will be involved in an interdisciplinary community of scholars with a long-standing history of advancing social justice through intellectual inquiry and research activities.

To apply to the M.S. program, you will want to put together a strong application package that shows that you are serious about academic work in data analysis and visualization and are likely to do well at it. Because the goal of the program is to provide students with research tools in data analysis and visualization, applicants must demonstrate commitment to a research-informed approach to their areas of interest, and some evidence that they understand what is involved in making such a commitment. We additionally consider your prospects for working together with faculty and students in our program, as well as the contributions you can make to diversity and inclusion. We review all applications holistically, considering all elements of the application together.

If you have not been a student for a long time, or your undergraduate work was in a different field, or are simply unsure if data analysis and visualization is right for you, you may want to consider taking one or two courses as a non-matriculated student before you submit your application to the M.S. program. It is possible to enroll in a maximum of two courses as a non-matriculated student at the CUNY Graduate Center. Taking DAAV courses as a non-matriculated student gives you the opportunity to build relationships with faculty who may be willing to write letters of recommendation for you should you decide to apply. You will also have a chance to explore and decide whether you think the program is a good fit for you. Please contact the program at datavis@gc.cuny.edu for more information.

Your evidence of meeting our minimum eligibility requirements can include your transcripts, certificates, and C.V.

All applicants must present a C.V. detailing their previous educational and professional experiences.

All applicants must present transcripts as evidence of having earned a bachelor’s degree from an accredited college or university or their equivalents in the U.S. or abroad.

In addition, applicants must show transcripts, certificates, or a C.V. as evidence of previous experience in one of two areas of eligibility:

1. Programming, quantitative data analysis, statistical analysis, and/or web development
2. Art, design and/or other creative visual media work

 If your previous experience is in programming, quantitative data analysis, statistical analysis, and/or web development, you can provide one or more of the following forms of evidence:

• A transcript showing a minimum of 15 credits of undergraduate or graduate coursework in the above topics with a B (3.0) or better average,
• A graduation certificate from an accredited college or university showing completion of a program or series of courses in programming, data analysis, web development, statistical analysis, or app development (graduation certificates from nationally recognized coding academies may also be considered), or
• A C.V. showing a minimum of one year of professional experience in software development, data analysis, web development, app development, statistical analysis, or related areas, with at least one recommendation letter speaking to this work.​

If your previous experience is in art, design and/or other creative visual media work, you can provide one or more of the following forms of evidence:

• A transcript showing a minimum of 15 credits of undergraduate or graduate coursework in art and design or digital media creation with a B (3.0) or better average,
• A graduation certificate from an accredited college or university showing completion of a program or series of courses in art, design, web development, UIUX, digital art, software art, interactive art, or related digital media (graduation certificates from nationally recognized coding academies may also be considered), or
• A C.V. showing a minimum of one year of professional experience in art, design, web development, UIUX, digital art, software art, interactive art, or related digital media, with at least one recommendation letter speaking to this work.​

Your statement should emphasize your intellectual background, interests, and goals, and should explain why the CUNY Graduate Center’s M.S. Program in Data Analysis and Visualization is the right place for you to develop them. Discuss the particular issues and concepts in data analysis and visualization that most interest you; show how you have thought about them, being as specific as possible, and including both opportunities and challenges. Please avoid generalizations and truisms, such as the observation that data plays an important role in today’s world.

• Indicate why the Graduate Center’s M.S. Program in Data Analysis and Visualization is attractive to you. What excites you about this program, specifically, in comparison with other programs you may be applying to, or with previous experiences you may have had? In what ways will your work grow through this specific program? How will it help you explore your interests? We are looking for students who will thrive in this program; answering this question about this specific Graduate Center program will help us understand whether or not we offer a good fit for the type of work you hope to accomplish. You can find out more about our program by browsing our program website.
• Explain your intellectual interests in data analysis and visualization. Why are you choosing to study data analysis and visualization in an academic setting? In what ways will your work grow through communities and resources at the CUNY Graduate Center, specifically? You should explore the program website and related areas like GC Digital Initiatives to get a sense of the resources offered by the GC.
• Indicate how your background prepares you for advanced study at the graduate level. What prior experiences (technical, creative, intellectual, and/or professional) will you contribute as a member of our incoming cohort and DAAV community?
• Identify by name faculty whom you would be interested in working with, indicate an area of common interest, and, ideally, show some familiarity with their work.
• If relevant, please indicate ways that you might contribute to diversity, equity, and inclusion in the field of data analysis and visualization.
• If there is anything in your background that you think needs explanation, you may provide a brief account in your statement.
• If you are concerned about whether you meet minimum eligibility requirements, make the case for your admission in the Application Statement, explaining how your experiences and skills are a good fit for the program.
• Proofread your statement to eliminate errors of grammar, spelling, or punctuation.

Master’s degrees are shorter-term and involve fewer credit hours than Bachelors or Doctoral degrees. This means that your time in our program will be at a premium. To help our accepted students hit the ground running and in order to maximize their course of study, we ask applications to describe proposed areas of study during your time in the program.

The purpose of this document is to describe what you would like to research, why it is important to you, and what approach you will take toward achieving your goals. In the course of your graduate study, you will be exposed to new ideas, and these ideas will—of necessity—impact your trajectory in exciting and unpredictable ways. Your proposed area of study is not a contract and you are not bound to it; rather, we expect it to change and evolve as a result of your work in the program. Nevertheless, one way to approach the program at the outset is to think ahead to where you may be when you complete the program. What kind of thesis or capstone project might the “future you” undertake?

A strong proposal of research interests will be specific, give concrete examples, and answer such questions as:

• What is the problem, central concept, or issue that you will research during your time in the program?
• How will you approach that problem, concept, or issue?
• How does your research interact with existing research in the field? How do you foresee your approach making an impact in the field, or making a difference in the world?
• What are the social and cultural impacts of your research? How will your research project impact your community? How will it impact others?
• If you have done previous work that has made a difference, explain how. How will you apply what you learned in that process to your graduate study?

Your work in graduate school will include original research projects, so the more information you can provide us about your interests and experience, the better. If you have performed research in the past, you may choose to include a description of that research and the conclusions that you drew from it. If you have problems describing your research interests, you might choose instead to describe some data visualization and analysis projects that have impacted you, and how you might undertake a similar project that focuses on topics that are important to you.

Please proofread your research interests to eliminate errors of grammar, spelling, or punctuation.

Your application should be supported by 2 letters of recommendation. Strong letters of recommendation will come from faculty who are familiar with your scholarly and research potential and abilities. Solicit letters from faculty who can write in detail about your academic achievements, the likelihood that you can successfully complete an MA or PhD program, your ability to work in a sustained fashion, and your potential to carry out research projects.

Should you be unable to ask former faculty for letters, recommendations from people who have supervised you in a professional capacity are acceptable.

Consider that:

• Letters from faculty who cannot talk about your scholarly or research potential or abilities are usually not helpful.
• Similarly, letters from friends, family, or coworkers are usually not informative, but in some cases a letter from a supervisor or coworker may be relevant if it concerns your professional work pertaining to an area of study in this program (see “Proof of minimum eligibility requirements” above).
• If you have been out of school for a while, consider taking one or more courses as a non-matriculated student and participate actively, so that your instructor will agree to write you a letter and will be able to write you a letter with substance (see “Before You Apply: Tips for Applicants” above).

Give your recommenders all the information about yourself that will be helpful, including:

• Classes you have taken with the recommender;
• Transcripts;
• Resume or CV, including awards or special achievements;
• Research experience and internships;
• Academic goals;
• If you have carried our research with your recommender, a summary of all of your activities and what you learned in the course of the research;
• Reminders about any written work you submitted or any other activities that you think are relevant; and
• As a matter of professional courtesy, approach your recommenders with ample lead time and make them aware of application deadlines.

Your portfolio is an opportunity to share examples of your previous work with us. Pick one of the three following portfolio types to submit. The committee will look at only one portfolio per applicant. If you have experience in more than one area, pick the portfolio type that shows the work of which you are proudest.

Computational work portfolio

You may submit a portfolio that showcases your prior work with computational programming, statistical analysis, data visualization, or website design. Your portfolio should include between four to eight work samples that demonstrate your ability to perform computational or statistical analysis, to write code, to create data visualizations, or to create websites.

A strong portfolio will:

• highlight the code you have written or statistical analysis you have used;
• discuss the context of each project; (150 words maximum)
• provide a short description of the results of your analysis for each project (200 words maximum).

You will be asked to provide a link to your portfolio during the application process, so please host it in a space where application reviewers will be able to access it without login, such as a Google Drive, Dropbox, or OneDrive account.

Design/Creative/Art work portfolio

You may submit an optional portfolio that showcases your prior work with data visualization, design, and/or creative practice. Your portfolio should include between four to eight work samples that demonstrate the visual media work of which you are proudest.

A strong portfolio will:

• highlight the visual and/or interactive impact of each project;
• provide a short description of the context in which each project appeared and your role in its creation (200 words maximum); and
• be beautifully designed and grammatically flawless.

You will be asked to provide a link to your portfolio during the application process, so please host it in a space where application reviewers will be able to access it without login, such as a Google Drive, Dropbox, or OneDrive account.

Writing sample

You may submit a short writing sample (5-10 pages maximum), which can be a paper, a chapter from a longer document, or something informal that showcases your way with words. You might include a paper or excerpt, for instance, upon which an undergraduate faculty member gave you positive feedback.

You might approach your writing sample by, for example, trying to resolve a contradiction in the literature, or analyzing a phenomenon involving data practices, or developing a new approach to solving a problem involving data, or report on a data-driven experiment. Alternatively, you might provide an academic writing sample from another field that gives evidence of your thought process.

A strong writing sample will:

• reveal your knowledge of a topic (ideally related to data) and your approach to analyzing it;
• articulate your own original insights;
• show knowledge of the conventions of academic writing; and
• be proofread for grammatical errors.