For undergraduates, the Goergen Institute ONLY offers a data science major - it does not offer a minor or a cluster.
Undeclared, undergraduate students interested data science can sign up for the data science undeclared major listserv, follow the Goergen Institute on LinkedIn and Twitter, and sign up for the Goergen Institute newsletter.
Lisa Altman is the main point of contact for questions related to the data science major.
The professional advisors at the College Center for Advising Services (CCAS ) are available to help students navigate a wide variety of academic programs and policies . They assist students with developing comprehensive academic plans, provide referrals to specialized academic resources, and help students problem-solve when they are experiencing academic challenges. CCAS is a good place to start when students have academic questions or concerns and are unsure of who to go to for assistance.
Peer advisors are upper class data science majors who have been selected for their experience and knowledge. They are an excellent resource for questions about study abroad, research, course content, clubs, internships, double majors, and more. For a student's perspective on the data science major, please contact the data science peer advisor. Data science students may also want to consult with peer advisors in other areas, such as computer science, mathematics, or the department of their application area.
Juney Lee (Class of 2022), Data Science Peer Advisor
Majors: data science (BS) and business (BA)
Internship (s): Product Line Management (PLM) Engineer intern at Applied Materials
Research experience: N/A
Activities: Women's Field Hockey Team, Kappa Delta Sorority, Varsity Student Athlete Advisory Committee (VSAAC) member, fitness center monitor, CETL study zone leader
Interested in becoming a data science peer advisor? Email Lisa Altman to indicate your interest.
Undergraduate faculty advisors are data science affiliated faculty from departments across the University. They can advise on research opportunities, graduate school, career paths, and discipline-specific questions in various data science application areas.
|Biomedical signals and imaging||Ajay Anand|
|Brain and cognitive sciences||Ralf Haefner|
|Computer science, statistics, and mathematics||Jiebo Luo, Dan Gildea, Brendan Mort , Gaurav Sharma (CS) |
Joe Ciminelli (STAT)
Alex Iosevich (MATH)
|Earth and environmental science||Lee Murray|
|Economics and business||Bin Chen (ECON) |
Roy Jones (BUS / CIS)
Declared majors receive additional support from the department, including information about career and research opportunities, and invitations to special talks and meetings. Those wishing to declare should meet with a data science academic or peer advisor to address general questions about course planning, major declaration, and transfer credits.
The following resources may be useful for your declaration:
How to Declare
- Complete the data science pre-requisite courses.
- Fill out the Major/Minor Declaration Form.
- If you plan to pair your data science degree with a minor, declare your minor with the appropriate department by filling out a separate Major/Minor Declaration Form for your minor.
- Schedule a 30 minute meeting with Lisa Altman to review your data science declaration form for final approval.
Required degree courses cannot be taken on a satisfactory/fail (S/F) basis. In cases where a course taken under the S/F option is a required part of the major, students must inquire about whether their grade is a C or better. Contact a CCAS advisor for more information on this policy.
Double Majors/Double Degrees
Many data science students choose to complete double majors, most often selecting an additional major related to their application area. Students completing a double degree program must complete all the requirements for each of their degrees. Students must also adhere closely to the college’s course overlap policy. We recommend that students seek advice when planning two majors, especially in regard to course schedules and overlaps. This is particularly important when declaring a major in another natural science. The University’s overlap policy does not permit double majors in computer science and data science. However, it is possible to major in data science and minor in computer science.
If your double degree is a BA and a BS or two BS degrees, you must fill out the Petition For an Exception to a Faculty Rule or Regulation after declaring your second major. This form is not necessary if you are completing two BA degrees.
Double degree students are also expected to earn at least 136, rather than 128 credits, to fulfill the graduation requirements of both degrees.
Revising your Major
If you need to update your declaration with a different course than originally planned, contact Lisa Altman for assistance filling out a Departmental Major/Minor Revision Form. This form can only be completed by a departmental advisor.
Shifting Between a Data Science BA and BS Degrees
If you are shifting between a BA and a BS in data science, you must fill out the Rochester Curriculum Change Form to drop your existing major. We recommend meeting with your departmental advisor to complete a declaration for a new degree.
Shifting Between Data Science and Hajim Degree Programs
If you are planning to switch into/out of computer science or an engineering major, please complete the Hajim Pre-Major Change Form to notify the Hajim school of your intent to change your major, since data science is officially a part of the School of Arts & Sciences. Then, meet with the data science advisor to discuss your plans for a data science major.
The first step toward academic success is taking advantage of office hours and appointments with faculty and teaching assistants. Academic help is also available from departments across the University.
- The River Campus Libraries offer a wide variety of helpful resources in the LibGuide for Data Science.
- The Computer Science Undergraduate Council (CSUG) offers free tutoring for all computer science courses.
- The Department of Mathematics offers math help at their math study hall and through the Center for Excellence in Teaching and Learning (CETL).
Refer to the University’s complete list of academic services to support your academic success.
Data science student major GPA is used for departmental distinction to reflect the quality of performance within a major. In 2021, the cut off was as follows:
- distinction = major GPA equal or above 3.5
- high distinction = major GPA equal or above 3.7
- highest distinction = major GPA equal or above 3.85
Teaching assistants (TAs) play a vital role in helping their peers better understand course material. Each semester, we appoint several students as TAs for data science courses. Current data science TA’s are:
|DSCC 201/401||Tools for Data Science||Brendan Mort||Alex Crystal, Sunishka Misala, Tanqiu Jiang|
|DSCC 202/402||Data Science at Scale||Brendan Mort & Lloyd Pallum||To be offered Spring 2022|
|DSCC 262/462||Introduction to Computational Statistics||Yusuf Bilgic||Minghui Cen, Avery Girsky, Ryan Hilton, Helena Winkler, Isabel Kenney|
|DSCC 265/465||Intermediate Statistical and Computational Methods||Yusuf Bilgic||To be offered Spring 2022|
|DSCC 275/475||Time Series Analysis and Forecasting||Ajay Anand||Chuqin Wu, Sung Beom Park, Ziyu Xiong|
|DSCC 383W/483||DSC Capstone/Practicum||Ajay Anand & Cantay Caliskan||Khoa Hoang|
|DSCC/CSC 240||Data Mining||Cantay Caliskan||Ledion Lico|
Becoming a TA
To become a TA you must have:
- Taken the course before the semester you are seeking to TA for.
- Received (or will receive) an A or A- in that course.
You can apply to be considered for multiple courses, but you will only be assigned to TA for one DSC course. You are allowed to apply to/maintain another TA position or job with other departments.
Use the TA application form to express your interest. You can contact a professor directly but you must apply using the form. The department will review all TA requests and send decisions out before the beginning of each semester.
- For Fall classes, apply between March and April. We will send out decisions by July.
- For Spring classes, apply between October and November. We will send out decisions by December.
Some courses require instructor permission to register on UR Student. To learn about registering for a course that requires instructor permission, see the requesting permission to register (PDF) or view the video (QRV).
We encourage students attend the first day of class even if they are unable to register for a course. Spots may open up in the first or second week of classes as students add/drop. In addition, faculty are more likely to let a student in if they have attended their course since the start of classes.
Transfer credit from AP/IB courses and other placement exams, domestic colleges, and study abroad programs are permitted for the data science major with the appropriate approvals. Consult the CCAS advising handbook to learn more about transfer credit.
Fundamental courses in the data science curriculum are approved by other departments. Students should seek transfer approval from computer science (for CSC171) and mathematics (for calculus and statistics).
Incoming Transfer Students
Incoming transfer students should seek course approvals as soon as they arrive on campus. Since most courses in the data science curriculum are offered by computer science and mathematics, students should seek transfer approval from those departments. Incoming transfer students should meet with the data science academic advisor as soon as possible to create a plan for declaring and completing the major.
Data science students planning to study abroad typically look for programs that offer coursework in computer science, mathematics, or other college requirements, so that they can work towards their degrees while they are abroad. Students planning to study abroad should discuss their course plans with an advisor in the Center for Education Abroad. Refer to the below section on transferring course credit back to the University of Rochester.
Transferring Course Credit
Current students taking courses outside the University of Rochester must obtain approval for transfer credit PRIOR to taking the outside course. This policy applies to education abroad courses as well. Course Approval Forms can be obtained from the College Center for Advising Services (CCAS) in Lattimore 312.
Many courses in the data science curriculum are offered by computer science (artificial intelligence, data mining, database) and mathematics (calculus, linear algebra, statistics, probability); students should seek transfer approval from those respective departments. To receive transfer credit for elective courses from data science application areas, students should consult the authorized approval list for the appropriate department.
To request transfer credit approval you must:
- Fill out the study abroad/transfer request form and the substitution request form
- Provide a syllabus for each course that you wish to transfer, that details:
- Exactly which topics are covered and in what detail
- What the homework and project requirements are
- How the course is graded
- Indicate whether you believe the course to be equivalent to a University of Rochester data science course or not (equivalency means that your course covers the same material, in the same depth).
- A schedule from the course syllabus is also useful.
The departmental advisor will forward your request to the appropriate instructor for consideration and notify you of their decision.
The transfer approval process can take several days or weeks. DO NOT wait until the last minute to get approval. Once your Course Approval Form has been signed by an authorized department approver, you should return it to CCAS in Lattimore 312.
Upon completion of the course(s), ask the registrar at the other school to email an official transcript to CCAS (email@example.com).
Data science is an emerging, cross-disciplinary field. Occasionally, a pre-existing or new course from an ancillary department may be used in place of a pre-approved course in our curriculum.
To obtain a course substitution, fill out the substitution request form for data science. You must provide a course syllabus and reasoning for the substitution exception. Once the course has been approved, your academic advisor will file a Departmental Major/Minor Revision Form to update your declaration.
The Goergen Institute for Data Science supports professional development for our undergraduates through sponsored attendance at conferences and events. With funding from the Institute, students have attended the annual Grace Hopper Celebration celebrating women in computing, the MIT Sloan Sports Analytics Conference, and various hackathons and other events. To receive reimbursement for data science-related travel, students should complete the student conference travel application and email it to Lisa Altman (firstname.lastname@example.org) 5 weeks or more before traveling.
The University of Rochester - a top-tier research institution with a compact campus, flexible curriculum, and interdisciplinary focus - fosters unique opportunities for undergraduate research. Data science students perform research with data science affiliated faculty members and with other departments, including the University medical center.
Included below are some helpful resources for undergraduates interested in research:
Support for Research
Students seeking research opportunities should review the websites of GIDS affiliated faculty members and related University institutes and centers to find research topics that interest them. Reach out to faculty members directly to ask about research opportunities. We also encourage students to attend research talks on campus.
The Office of Undergraduate Research supports students preparing for, pursuing, and participating in research. Visit their website to learn more about undergraduate research opportunities in departments across campus.
Undergraduate students have the option to engage in research for credit. To enroll in an independent study or independent research (DSCC 391 or DSCC 395), you must:
- Collaborate with a full-time faculty member who will supervise and guide your independent work.
- Approach an affiliated faculty member who you would like to work with, and ask them if they would be able to/interested in supervising your work.
- Fill out the following online Independent Study Form alongside the faculty member who will be supervising your work (you will need to discuss credit hours, course title, course description, and how you will be evaluated).
You CANNOT register for an independent study via the University's online course registration system. You must follow the steps above in order to properly register.
Registration for a 4.0 credit independent study is due by the third Wednesday of each semester.
Data science majors frequently pursue masters and doctoral degrees in computer science, data analytics, statistics, and business. Below are some helpful resources if you’re considering graduate school.
- Gwen M. Greene Center for Career Education & Connections
- Computer Science Grad School Tips
- Research Experience for Undergraduates (REU) Finder
Because our Master of Science (MS) in data science program overlaps with our bachelor’s program, we typically do not admit our University of Rochester data science majors into our master’s program. However, students who have majored or minored in STEM or data-focused disciplines are ideal candidates. We will waive application fees for University students and alumni who apply to the MS program.
The Gwen M. Greene Center for Career Education & Connections posts job and internship opportunities for data science majors on Handshake. They also circulate opportunities in the career community for engineering, technology, data and physical sciences.
The Goergen Institute also maintains a log of organizations that have posted data science-related jobs and internships. You can browse through this information by stopping by the data science offices in Wegmans Hall. We encourage you to keep your resume updated and utilize professional networks by creating profiles on LinkedIn and Meliora Collective. We also encourage you to apply for internships – they are a great way to gain experience and become more marketable for future jobs or graduate school.
Check out the following videos for more information building a career in data science: