Undergraduate Program

Departmental Advisors

Lisa Altman is the main point of contact for questions related to the data science major.

Lisa Altman, Data Science Academic Advisor
Email: lisa.altman@rochester.edu
Schedule a Zoom meeting with Lisa

University Advisors

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

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.

Meg Hanson (Class of 2025), Data Science Peer Advisor

Hometown: West Windsor, NJ

Email: mhanson8@u.rochester.edu

• Academics: BA in Data Science with computer science/mathematics/statistics concentration area (Natural Science); Computer Science minor; Japanese minor (Humanities); Psychology as a Social Science cluster.
• Study Abroad: Nanzan University in Nagoya, Japan during Fall 2023.
• Teaching Assistant Experiences: Math and Computer Science Tutor at The Learning Center; CSC 161: Introduction to Python with Professor Read McFarland
• Internship Experiences: Assistant Counselor with Tokyo Coding Club (2021); Data Analyst Intern on Sales Management Portfolio at IBM Chief Analytics Office (2023); Data Migration Consultant Intern at RMS (2024) 
• Extra-Curricular Activities: Varsity Softball (2021-2022), Varsity Rowing (2022-present), Rochester Campus Ministries, Alpha Phi sorority
• Post Graduation Plan: Working as a data analyst or technology consultant and eventually earn a master's degree.

Marla Litsky (Class of 2024), Data Science Peer Advisor

Hometown: East Fishkill, NY

Email: mlitsky@u.rochester.edu

Schedule a meeting with Marla

• Academics: BS in Data Science with political science application area, BS in Political Science (Social Science), Studio Art (Humanities) Cluster, e5 Program (Experiential Fifth Year)
• Teaching Assistant Experience: Tutoring at The Learning Center
• Research Experience: Research with Professor Cantay Caliskan: “Measuring Distance as a Result of Polarization on the Turkish Streets"
• Internship Experience: Measures for Justice
• Extra-Curricular Activities: Social Chair for Women and Minorities in Computing, Hillel, Student Alumni Ambassador
• Campus Job: Meridian (Admissions Tour Guide)
• Post Graduation Plan: graduate school for data science and/or an career in a social justice/non-profit organization

Interested in becoming a data science peer advisor? Email Lisa Altman to indicate your interest.

In addition to the peer advisor, you will find students with strong interests in data science involved with the following clubs:

• Undergraduate Data Science Council
• Women and Minorities in Computing
• Computer Science Undergraduate Council
• Dandyhacks
• Sports Analytics Club
• Society of Undergraduate Statistics Students

Faculty Advisors

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.

Intended or declared majors receive additional support from the department, including information about career and research opportunities, and invitations to special talks and meetings.

Those intending or declaring 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 planning the major:

• data science major checklist (DOCX)
• sample schedule (PDF)
• WRTG 273 advising guidelines (PDF)

Changing Your Intended Major

If you are planning to switch into/out of the data science pre-major, please complete the Intended Major Change Form to notify the college of your intent to change your pre-major. Then, meet with the data science advisor to discuss your plans for a data science major.

The college permits up to two intended pre-majors as planning dual majors/double degrees is common.

How to Declare

1. Complete the data science pre-requisite courses. You should have an average pre-req GPA above 2.0 and no more than one course below a grade of C.  Transfered courses are permitted for any pre-requsite course.
2. Fill out the Major/Minor Declaration Form.
   • Do not list pre-requisite courses on major declaration form.
   • A BA will typically have 12 courses listed to complete degree.  A BS will typically have 15 courses listed to complete degree.
   • Check boxes for the two courses intended to fulfill upper level writing courses (DSCC383W and WRTG273 or other acceptible course.)
   • Note any overlaps with a cluster, minor or other major in the comments section.
3. 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.
   • If you are doing a 2nd major, see rules below if you need to complete the Petition For an Exception to a Faculty Rule or Regulation.
4. 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, after declaring your second major you must fill out the Petition For an Exception to a Faculty Rule or Regulation. 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.

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 the Learning Center.

Refer to the University’s complete list of academic services to support your academic success.

Departmental Distinction

Data science student major GPA is used for departmental distinction to reflect the quality of performance within a major.

• 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

Math Minor

A data science BS major can easily earn a math minor by taking one additional math course, MATH 235: Linear Algebra.  The following shows the courses need for the math minor and how it is permitted with exceptions from the overlap policy.

• MATH150 – no overlap per policy/prereqs in data science/foundational for math minor
• MATH161 & MATH 162 - no overlap per policy/prereqs in data science/foundational for math minor (or MATH 141, 142 & 143 OR MATH 171 & MATH 172)
• MATH165 - no overlap per policy/core for data science/foundational for math minor
• MATH201 – overlap/BS requirement for data science/advanced course for math minor
• MATH203 – overlap/BS requirement for data science/advanced course for math minor
• MATH235 – no overlap

Computer Science Minor

A data science major can earn a computer science minor by taking two additional computer science courses.  The following shows the courses need for the computer science minor and how it is permitted with exceptions from the overlap policy.

• CSC171 - no overlap per policy/prereqs in data science
• CSC172 - no overlap per policy/prereqs in data science
• CSC/DSCC240, CSC/DSCC261, CSC/DSCC242 – Use two of these courses for the two overlaps/core requirement for data science/computer science minor
• Two non overlapping computer science courses above the level of 130.

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:

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.

Deadlines:

• 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.

AP/IB Courses

Fundamental courses taken in high school from the data science curriculum are approved by other departments. The college has information in the advising handbook for getting credit from Advanced Placement (AP) and International Baccalaureate (IB) courses.

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.

Study Abroad

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 (ccasrec@ur.rochester.edu).

Substitution Requests

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 (lisa.altman@rochester.edu) 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.

For an example of data science student research, read about Sarah Lee '24.

Included below are some helpful resources for undergraduates interested in research:

• AURA (University of Rochester's research finder platform)
• GIDS Undergraduate Summer Research Experience
• Research Experience for Undergraduates (REU) Finder

Support for Research

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.

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.

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:

1. Collaborate with a full-time faculty member who will supervise and guide your independent work.
2. 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.
3. 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 and Connections
• Computer Science Grad School Tips
• Research Experience for Undergraduates (REU) Finder

The Computational Medicine Program offers current University of Rochester undergraduate and master’s students preferred admission to Thomas Jefferson University’s Sidney Kimmel Medical College (SKMC). Students admitted into this early assurance program will get a chance to work with SKMC faculty during the summer of their junior year. So long as all requirements are met, the Medical College Admission Test (MCAT) requirements will be waived for students in the program. Entry to the Computational Medicine Program is very competitive, with only five to ten Rochester students chosen each year. Students should apply to this program during the second semester of their sophomore year and successful applicants will be notified prior to the start of their junior year.

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 and Connections posts job and internship opportunities for data science majors on Handshake. They also circulate opportunities in the career community for emerging technology.

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: