Graduate Program

• A completed Bachelor’s degree (all majors are considered)
• One year (or equivalent) of coursework in undergraduate calculus and linear algebra
• Proficiency in introductory programming and data structures (Python or Java) through coursework or equivalent experience
• Interest in and motivation to pursue large scale quantitative data analytics

To apply, submit an online application with the following material:

• Official transcript(s) from bachelor’s degree (and any other higher education experiences)
• Personal statement describing:
  
  • Prior experience
  • Career and educational goals
• Resume/CV
• Two letters of recommendation (must be received by application deadline)
• GRE and TOEFL/IELTS scores not required

Apply by March 15 to be considered for summer or fall semester admissions.

Apply by November 15 to be considered for spring semester admissions.

There is no application fee for the advanced certificate as this is a PART-TIME program.

The Advanced Certificate in Data Science program is 16 credits, or the equivalent of four, graduate level courses. The program can be completed in two to four semesters of part-time study. Courses are currently offered in the fall and spring semesters during the day, and attendance is required. Some courses have been offered during the summer session but are not guaranteed every year. At this time, we do not offer courses in the evenings.

There are three different course plans designed to suit each student’s unique background and level of preparation. See the course descriptions/schedules website for a description of each course.

Certificate Plan A (Typical)

• DSCC 462: Computational Introduction to Statistics (fall/summer)
• DSCC 401: Tools for Data Science (fall/spring)
• DSCC 465: Introduction to Statistical Machine Learning (formerly Intermediate Statistical and Computational Methods) (fall/spring)
• DSCC 440: Data Mining (fall/spring)

Certificate Plan B

Designed for students with an academic background in computational statistics (equivalent of DSCC 462):

• DSCC 401: Tools for Data Science (fall/spring)
• DSCC 465: Introduction to Statistical Machine Learning (formerly Intermediate Statistical and Computational Methods) (fall/spring)
• DSCC 440: Data Mining (fall/spring)
• One Elective

Certificate Plan C

Designed for students with prior academic experience in data science programming tools (equivalent of DSCC 401):

• DSCC 462: Computational Introduction to Statistics (fall/summer)
• DSCC 465: Introduction to Statistical Machine Learning (formerly Intermediate Statistical and Computational Methods) (fall/spring)
• DSCC 440: Data Mining (fall/spring)
• One Elective

Electives

• DSCC 402: Data Science at Scale (spring only)
• DSCC 435: Optimization for Machine Learning (fall only)
• DSCC 475: Time Series Analysis & Forecasting in Data Science (fall only)
• CSC 461: Database Systems (spring/summer/fall)
• CSC 449: Machine Vision

Courses are 4 ASE graduate credits and part-time students will have no fees. For tuition information, please review the part-time schedule of tuition and fees.

Current PhD students may be awarded tuition scholarships on a competitive basis.

University of Rochester employees may be eligible for tuition benefits.

Students are permitted to take one course as an unmatriculated student and use it toward the advanced certificate. Courses MUST be at the graduate (400) level and not undergraduate (200) level.

Students interested in transferring credits earned from the advanced certificate to the Master of Science (MS) in data science program should contact the program advisor at gids-advcert@rochester.edu.

For additional information on the advanced certificate program, contact gids-advcert@rochester.edu.