MS in Data Analytics in Social Sciences

Students are first introduced to basic/intermediate statistics courses and computation programming/data manipulation. They will then take research courses both within and outside of their preferred discipline, culminating in a research project or thesis on a topic of their choice (in conjunction with an advisor).

Additional academic and pre-professional opportunities include:

• Professionalization Opportunies. In addition to formal coursework and in conjunction with their faculty advisor, students in the program are encouraged to seek professionalization opportunities that will prepare them to enter the workforce. Professionalization opportunities are offered through our Career Design Center and The Graduate School.

Prospective DASS students should select a faculty member in their specialty department for thesis or project guidance. A faculty advisor must be selected at the time of admission. 

Students in the program will meet with their faculty advisor each semester. At this meeting, in consultation with the program coordinator, the advisor should provide academic advice to students on every relevant matter, including the students’ course schedule for the upcoming semester. 

The 36 credit program requires:

• 12 credits of statistics
• 6 credits of computation programming/data manipulation
• 6 credits of research methods
• 6-9 credits of coursework in advanced data analysis within one social science discipline
• 3-6 credits of project or thesis guidance

Each student will be required to take at least 9 credit hours in a department other than their specialty discipline to increase familiarity with the methods of adjacent fields.

The MS project or thesis will be submitted to two faculty members from two distinct departments (including the student’s project advisor), who have the sole responsibility for its review, revision and acceptance. Each committee member must be in one of the participating social sciences departments and hold membership in UB’s graduate faculty. In exceptional cases (and if the project warrants), one member of the MS committee might be from a non-social science department.

What is a Master's Project?

A master's project takes an applied approach to developing a practical solution or solving a specific problem in your chosen field in the social sciences. The structure of a project can be more flexible and may include a project report, design documentation or a prototype, with the goal of demonstrating your ability to apply theoretical knowledge in a practical setting. Examples of master's project topics include: 

• Develop a predictive model to assess the impact of lifestyle factors on health outcomes. 
• Develop a predictive model to identify high-risk areas for crime in a city. This project could involve analyzing historical crime data, identifying patterns, and creating a model to predict future crime hotspots. The findings could help law enforcement agencies allocate resources more effectively.
• Study the factors that influence consumer behavior and economic decision-making.
• Develop a policy framework to enhance community resilience to climate change. This project could involve analyzing climate data, assessing community vulnerabilities, and proposing policy measures to mitigate risks and improve adaptive capacity.
• Develop and evaluate a mental health intervention program for college students.
• Conduct an epidemiological study on the spread of an infectious disease in a specific population.
• Design and implement a health promotion campaign to address a public health issue, such as smoking cessation or obesity prevention.
• Document and analyze an endangered language and develop strategies for language preservation and revitalization.
• Create a detailed map of urban heat islands in a city using remote sensing and GIS, and suggest mitigation measures. 
• Study the process of gentrification in a particular neighborhood and analyze demographic and economic data to understand the effects on local communities.
• Investigate how climate change is affecting coastal erosion in a specific region and model future erosion scenarios. 
• Study the impact of urban development projects on social equity in a specific city and propose strategies to ensure equitable development.
• Develop predictive models to forecast economic trends using big data. This project might include collecting and analyzing economic data, building machine learning models, and evaluating their accuracy in predicting economic indicators.
• Analyze migration patterns and their impact on urbanization. This project could involve collecting demographic data, mapping migration flows, and identifying trends and factors driving migration. 

Fall Application Deadline: May 1 
Spring Application Deadline: November 1 

Admissions guidelines for the program are as follows:

• BA/BS in a social science discipline, though special provision may be made for students with degrees in the humanities (particularly those interested in digital humanities)
• 3.0 undergraduate GPA in social science area courses
• Two (2) letters of recommendation
• Resume or curriculum vitae (CV)
• One (1) writing sample
• Statement of educational and career goals
• International students will need to provide proof of English proficiency via standardized test scores.