MS DACSS Frequently Asked Questions
- While DACSS is a data science program, we emphasize substantive understandings of political, social, and economic behavior grounded in social scientific theories and methods. We offer courses using the same computational methods taught in a computer science program, but from a more applied perspective with an emphasis on the use of data science approaches to tackle specific real-world problems. The program provides students with a broad introduction to a range of methods used for data collection and interpretation with a focus on designing problem-driven research to support evidence-based decision-making.
- The DACSS program also emphasizes the importance of visualization and communication in the evidence-driven decision-making process. Successful students will graduate from the program knowing how to collect, analyze, and make sense of data for government, business, and NGOs. Students who graduate from the program also have the skills needed to continue their education through a PhD program.
- Formal MS program requirements include successful completion of 12 courses (34 total credits): 8 required core courses (including courses on professional development and data ethics and AI), at least 2 technical electives, and up to 2 substantive electives.
- The eight core courses that ensure that students are able to meet industry standards in data analysis and communication and effectively use data to support decision-making:
- DACSS 600 : Math for Applied Data Science
- DACSS 601: Data Science Fundamentals
- DACSS 602: Research Design for Social Scientists
- DACSS 603: Introduction to Quantitative Analysis
- DACSS 621 : Ethics in Data Science & AI
- DACSS 691P : Polishing Your Professional Presence
- DACSS 713 or DACSS 756: Advanced Statistical Methods or Machine Learning for Social Scientists
- DACSS 684/698R: Advanced Data-Driven Storytelling
- Technical electives provide advanced technical training in specialized methods of data collection and analysis: surveys, text as data, social networks, GIS, spatial statistics, lab and field experiments, time series, machine learning, and Bayesian statistics.
- Substantive electives offer a substantive background in a range of social science topics. Courses include Public Opinion, Digital Labor, Social Life of Algorithms, Industrial Organization, and Media and Politics.
- Note: These courses illustrate the types of courses that might be available at any given time; course titles and contents are subject to change. Please check updated listings available on SPIRE, through University+, or on the DACSS website for courses available during specific terms.
- The MS in DACSS can be completed either full-time or part-time. Graduate students who take at least 3 courses per term are considered full-time students and generally finish the degree in 18–24 months. Part-time students complete the program at their own pace and consult with our academic advisors to create a course pathway that works for their schedule. Please note that international students enrolled in the in-person MS are not eligible for part-time status.
- Students must take 1–2 courses during both the Fall and Spring terms each year. Students are not required to take courses during the Winter or Summer terms in order to remain active in the program.
- Students are not required to write an MS thesis, but may use up to 2 of their substantive electives (up to 6 credits total—one during each of the Fall and Spring terms) to work on an independent research project. Students may find faculty who are interested in working on a publishable project during their time at UMass, and this independent research track allows time to work on a conference paper, publication, or research-based report in lieu of a traditional thesis track.
- We are looking for students with a strong problem-solving mindset, a substantive background or interest in social science, strong communication skills, and a willingness to engage with mathematical and logical concepts. Prior experience with programming is not required for admission.
- There are no specific prerequisites to enter the MS in DACSS program. We are looking for students with a strong problem-solving mindset, a substantive background or interest in social science, strong communication skills, and a willingness to engage with mathematical and logical concepts. Prior experience with programming is not required for admission.
- Applicants to the MS in DACSS program do not necessarily need to come from a computer science background, nor do they need to have a background in highly technical mathematical skills.
- The MS DACSS program is STEM designated. Students accepted into the in-person MS program will be eligible for a 3-year STEM OPT (optional practical training) placement after 8 months of residency in the United States. Students who pursue an online MS are not eligible for STEM OPT.
- Yes, students may accept hourly campus jobs or part-time jobs off-campus (if eligible, international students should confirm whether visa restrictions exist).
- Any enrolled student who is working a full or part-time job involving research and data analysis who finds a suitable faculty sponsor may enroll in up to 2 practicum courses (no more than 1 each term) and apply up to 6 practicum credits as substantive electives counting toward degree completion requirements.
- Students enrolled in the MS DACSS program will not be eligible for TA or RA positions that fall under the Graduate Employee Organization contract ("tuition-waiver" positions). However, there may be limited opportunities to work on research with faculty (primarily in unpaid positions), as well as the chance to earn course credit (via a faculty sponsored practicum or independent study) for work on guided individual research or a research-intensive internship (paid or unpaid).
- The MS DACSS program prepares you for careers in data-driven fields including applied data science, policy analytics, and research. Our graduates have titles such as "Data Scientist", "Data Analyst", "Research Analyst", "Research Consultant", etc. The U.S. Bureau of Labor Statistics projects strong job growth for data scientists with median wages over $112,000. BLS predicts a 34% increase in data science roles by 2034, and our graduates are well-positioned for these opportunities.
- Check out some of our alumni stories here.
Accelerated 4+1 Frequently Asked Questions
- 4+1 programs make it possible for undergraduate students to start completing graduate-level courses while working toward their bachelor’s degree. Once students have finished undergraduate, they are able to complete the master’s program in an accelerated timeline due to having completed credits in undergraduate.
- Any graduate courses taken in undergraduate will be counted toward the master’s program. Some students may be able to “double-count” credits, meaning they are used to fulfill undergraduate requirements while also contributing to the master’s degree.
- Following admission, students meet with our graduate advisors to discuss their course timeline. Undergraduate students should check in with their undergraduate advisors to determine if their master’s courses can also be used toward their major requirements or the required 120 credits for undergraduates.
- Please note, 4+1 students are only able to take a total of 12 graduate credits while still in undergraduate. We recommend 4+1 students take a minimum of 6 graduate credits during their senior year.
- We ask that students interested in a 4+1 program apply during their junior year of undergraduate. This ensures that 4+1 students can take graduate courses in combination with undergraduate courses for both semesters of their senior year.
- While we welcome first-semester seniors to apply to the program, having only one semester of graduate coursework while in undergraduate may alter the timeline for those students' graduate years.
Admissions FAQs
For more information on admissions, check out our Admissions FAQs page.