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Undergraduate Program

Advising

Undeclared, undergraduate students interested data science can follow us on LinkedIn and Twitter and sign up for our undeclared major listserv. Both majors and non-majors can join the Undergraduate Data Science Council to connect with other students sharing data science projects outside of the classroom.

For undergraduates, the Goergen Institute only offers a data science major - it does not offer a minor or a cluster.

This page has information on the following:

Academic Advisors

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

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

The professional advisors at the College Center for Advising Services (CCAS) are available to help student with a wide range 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.

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Peer Advisors

For a student's perspective on the data science major, please contact the data science peer advisor.

Anindini SinghAnindini Singh (Class of 2021)

Email: asingh@u.rochester.edu

Majors: data science and financial economics
Minor: mathematics
Internship(s): machine learning intern at STEM Away
Research experience: data analyst at the Institute of Macroeconomics, University of Leibniz, Germany
Activities: workshop leader, CSC 171, 172; TA, ECO 108, CSC 171; member, The Opposite of People (TOOP); actor, The Matter of J. Robert Oppenheimer

Peer advisors are upper class data science majors who have been selected for their experience and knowledge. They are excellent resources for questions about study abroad, research, course content, clubs, internships, double majors, and more. Data science students may also want to consult with peer advisors in other areas in including computer science, mathematics, and the department of their application area.

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

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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.

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Declaring Your Major

We encourage students to declare their data science major once their pre-requisite courses (MATH 150, CSC 171, CSC 172, and a calculus sequence) are complete. Declared majors receive additional support, including information about career and research opportunities, and invitations to special talks and meetings. Those wishing to declare should meet with an academic or peer advisor to address general questions about course planning, major declaration, and transfer credits. You may find our major checklist (DOCX) and schedule (PDF) useful to your declaration.

To declare your major, fill out the Major/Minor Declaration Form. Then schedule a 30 minute meeting with Lisa Altman to review the declaration form for final approval.

Required degree courses cannot be taken on a satisfactory/fail 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. Students can talk to a CCAS advisor for more information.

If you plan to pair your data science degree with a separate minor, you will need to declare your minor with the appropriate department by filling out the Major/Minor Declaration Form.

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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 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.

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Updating or Changing 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.

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.

If you are planning to switch into/out of computer science or an engineering major, please use the Hajim Pre-Major Change Form to notify the Hajim school, since data science is officially a part of Arts & Sciences. Then, meet with the data science advisor to discuss your plans for your data science major.

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Support for Studies

The first step toward academic success for data science students is taking advantage of office hours and appointments with faculty and teaching assistants. 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.

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Instructor Permissions/Waitlists

Some courses, such as the capstone/practicum, require permissions to register on UR Student. For instructions on requesting permission/registering for a course that requires the instructor’s permission see the requesting permission to register PDF or video (QRV).

If you are unable to register for a data science (DSC/DSCC) course because it is full, fill out the data science waitlist form for spring 2021. Responses will be used to determine what can be done to accommodate additional students. Complete this form once for EACH course if you are locked out of more than one. If you are misusing this form to improperly gain access to a course, you may be subject to discipline outlined in AS&E’s academic dishonesty policy.

We encourage you to attend the first day of class even if you 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 who has attended their course since the beginning of classes.

For core courses DSC 240/440, DSC 261/462, and CSC 242/442, contact the Department of Computer Science to inquire about waitlists.

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Teaching Assistant Applications

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:

CourseTitleInstructor(s)Teaching Assistant(s)
DSCC 201/401Tools for Data ScienceBrendan MortKhoa Hoang, Meghana Murthy, Montel Yu
DSCC 202/402Data Science at ScaleBrendan Mort & Lloyd PallumTo be offered Spring 2021
DSCC 262/462Computational Introduction to StatisticsJoseph CiminelliJi-Ze Jang, Joshua Liao, Raunak Mahalik, Abdul Moid Munawar, Alexandra Poloway, Srishti Singh, Siyu Xue
DSCC 265/465Intermediate Statistical and Computational MethodsTBATo be offered Spring 2021
DSCC 267Database for Data Science & BusinessPedro FernandezTo be offered Spring 2021
DSCC 275/475Time Series AnalysisAjay AnandAjinkya Deshmuhk, Jayant Giridhar Rohra, Luke Nau
DSCC 383W/483DSC Capstone/PracticumAjay Anand & Pedro FernandezWade Bennett

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 can apply to, or maintain another TA position or job with other departments.

Use the TA application form to express your interest. You can contact the professor directly but you should still apply on the form. We will review all TA requests and send out decisions before the beginning of each semester.

Deadlines:

  • For spring 2021 classes, apply between October 1 and November 30, 2020. We will be send out decisions by January 2021.
  • For fall 2021 classes, apply between March 1 and April 30, 2021. We will be send out decisions by July 2021.

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Transfer/Study Abroad Requests

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.

Incoming transfer students should seek course approvals as soon as they arrive on campus. All other students taking courses outside the University of Rochester must obtain approval for transfer credits PRIOR to taking the course. This rule applies to education abroad courses as well. Course Approval Forms can be obtained from the College Center for Advising Services (CCAS) in Lattimore 312.

Since most courses in the data science curriculum are offered in computer science and mathematics, students should seek transfer approval from those departments.

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.

We will forward your request to the appropriate instructor for consideration and notify you of our 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.

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Substitution Requests

Data science is an emerging, cross-disciplinary field. Sometimes, a pre-existing or new course from an ancillary department may be used in place of a pre-approved course in our curriculum.

To seek approval for a course substitution, use the substitution request form for data science and 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.

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Research

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 can do research with affiliated faculty or with other departments including the medical center.

Check out the GIDS Research Opportunities for Students video, and the undergraduate summer research page.

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Independent Studies

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 online registration system. You must follow the steps above in order to properly register.

You must register for a 4.0 credit independent study by the third Wednesday of each semester.

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Graduate School

Data science majors frequently enter graduate schools for masters and doctoral programs in computer science, data analytics, statistics, and business. Below are some helpful resources if you’re considering graduate school.

Because our data science MS program overlaps with our bachelor’s program, we typically do not admit our 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 of Rochester students and alumni who apply to our MS program.

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Internships/Jobs

The Gwen M. Greene Career and Internship Center posts job and internship opportunities for data science majors, so make sure to become active on Handshake and join the career community for engineering, technology, data and physical sciences.

Data science also keeps information on organizations that have posted data science-related jobs and internships. You can browse through this information by stopping by the data science office in Wegmans Hall. We encourage you to keep your resume updated and use 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:

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