Data Science and Analytics Minor

The Data Science and Analytics minor provides students with a foundation in the methodology of data science coupled with disciplinary applications that students can select in accordance with their interests. The minor is open to all students and can be tailored to best complement any major field of study. The goal of the minor is for students to develop skills in the theory and application of quantitative methods used to conduct original research in their field. Details on the curriculum and requirements for the minor are available here. Note that courses are not necessarily taught every semester or every year.

Data Science and Analytics Minor

Academic Advising

Students interested in declaring the data science minor should carefully review the requirements. If you have any questions, please consult with your faculty advisor and DDDI’s Assistant Director for the DASA Minor, Joelle Gross. Students must meet with Joelle before declaring the minor. Remaining questions about the program or the approval process for declaring the minor can be addressed to Dr. Colin Twomey. Students who are expecting to graduate in May 2024 should contact us as soon as possible to make sure the requirements are met.

Proposing an Exception

Students may propose that a course not listed in the existing curriculum for the DASA minor be counted towards the requirements for the minor. To make an exception request, please contact Joelle Gross with the course that you would like to count. Please send the syllabus for the proposed exception, along with a rationale for the request (following the criteria below).

Exception for a Core Course

To substitute for a course in one of the three core topic areas (Introductory Data Science and Programming, Math and Statistics, or Applied Data Science), the course must cover substantially similar material as one of the approved courses. Your request should specify which of the approved courses is most similar to the proposed exception.

Exception for an Elective

An exception for an elective should meet one or more of the following criteria:

  1. A theoretical, computational, or applied treatment of data science methods. Topics that would meet this criterion include: optimization, linear algebra, and database tools for working with large datasets.
  2. Substantial analysis of empirical data. For example, a course in which students write a final research paper that involves original data analysis would count for this criterion, but a course in which students summarize existing data analysis would not.

Additional Requirements

  • Students must have at least three courses that count only towards the DASA minor’s requirements. In other words, at most three of the six courses required for the DASA minor may overlap with the requirements for other majors or minors.
  • At least one of the three electives in your course of study must have a substantial data analysis component. In general, every elective should be chosen to enhance practical and/or theoretical data science skills.
  • Two courses that substantially overlap in material covered cannot both be counted towards the minor. E.g. only one of MATH 2400 and MATH 3120 would be counted, but both MATH 2400 and MATH 3130 (Computational Linear Algebra), or both MATH 2400 and MATH 3140 (Advanced Linear Algebra) could be counted.
  • Only one study abroad course may be counted towards the minor.
  • At least three of the six courses for the minor must be in the College. Note that one of these three may be from a study abroad course if it is counted in the College.

Course Offerings

The minor consists of six courses, three of which are foundational and must fall into specific components (Introductory Data Science & Programming, Math & Statistics, Applied Data Science) and the remaining three are electives that must have a strong link to data science.


Introductory Data Science & Programming (1 c.u.)
COMM 2550 Foundations in Data Science for Comm.
CRIM 4002 Criminal Justice Data Analytics
LING 0700 Data Science for Studying Language and the Mind
PSCI 1800 Introduction to Data Science
STAT 4700 Data Analytics and Statistical Computing
BIOL 2150 Statistics for Biologists
CIS 1050 Computational Data Exploration
ENGL 1670 Data Science for the Humanities
PHYS 1100 Foundations of Data Science
PHYS 2260 Introduction to Computational Physics
COMM 3810 Introduction to Python for Data Journalism


Math & Statistics (1 c.u.)
CRIM 1200 Statistics for the Social Sciences I
ECON 2300 Statistics for Economists
ENM 3600 Introduction to Data-driven Modeling
ENM 3750 Biological Data Science I – Fundamentals of Biostatistics
ESE 3010 Engineering Probability
PHYS 3358 Data Analysis for the Natural Sciences I: Fundamentals
PSCI 1801 Statistical Methods PSCI
SOCI 2010 Social Statistics
STAT 1120 Introductory Statistics
STAT 1020 Introductory Business Statistics
STAT 4300 Probability
MATH 3120 Linear Algebra


Applied Data Science (1 c.u.)
BIOL 4511 Biological Data Analysis
ECON 4330 Econometric Machine Learning Methods and Models
PSCI 3800 Applied Data Science
STAT 4420 Introduction to Bayesian Data Analysis
STAT 4710 Modern Data Mining
CRIM 4012 Machine Learning for the Social Sciences
GAFL 5310 Data Science for Public Policy
CIS 4190 Applied Machine Learning
PHYS 3359 Data Analysis for the Natural Sciences II: Machine Learning
CIS 5450 Big Data Analytics
ESE 3600 Tiny Machine Learning
ESE 2000 Artificial Intelligence Lab: Data, Systems, and Decisions


Electives (3 c.u)
ASTR 1250 Astronomical Techniques
BIOL 4536 Introduction to Computational Biology & Biological Modeling
COMM 3130 Computational Text Analysis for Communication Research
ENVS 3700 GIS: Mapping Places & Analyzing Spaces
CIS 4210 Artificial Intelligence
CIS 4500 Database and Information Systems
COGS 4290 Big Data, Memory and the Human Brain
LING 2220 Phonetics II: Data Science
LING 2250 Computer Analysis and Modeling of Biological Signals and Systems
PHYS 2280 Physical Models of Biological Systems
PSCI 3802 Political Polling
SOCI 2220 Health of Populations
STAT 4240 Text Analytics
URBS 3300 GIS Applications in Social Science
ANTH 3307 Introduction to Digital Anthropology
MKTG 2120 Data Analysis for Marketing Decisions
ACCT 2700  Forensics Analytics
STAT 4750 Survey Research & Methods
COMM 4190 Talking AI Computational and Communication Approaches
EESC 3376 Climate Change and Big Data


Data Science at Penn

Penn offers a number of pathways when it comes to integrating data science into your course of study. Which path is right for you will depend on your goals.

  • Data Science and Analytics Minor (DASA; this page). The DASA minor is designed to complement any major field of study in the natural and social sciences. The path offered by this minor trains students to use data to answer applied research questions in their respective field.
  • Survey Research & Data Analytics Minor (SRDA). The Survey Research & Data Analytics minor focuses on understanding public opinion and elections through the use of survey research and data analysis. The SRDA path offers students a deeper substantive focus on politics, elections, and public opinion.
  • Digital Humanities Minor. This course of study is intended for students in the humanities rather than the natural and social sciences.
  • SEAS Data Science Minor (DATS). The DATS minor introduces students to a wide range of mathematical and computational tools for data science with application areas ranging from information systems and finance to data mining.
  • Wharton Statistics and Data Science Minor. Relative to the College Data Science & Analytics minor, this course of study emphasizes classical probability theory and statistics, along with their mathematical underpinnings.
  • Annenberg Data and Network Science concentration. The focus of this concentration is on data science for understanding social networks and communication. It is only open to Communications Majors.

Contact Information

Joelle Gross

Assistant Director, Data Science and Analytics Minor

Dr. Colin Twomey

Interim Executive Director for DDDI

Prof. John Lapinski

Faculty Advisor for students in the Social Sciences

Prof. Masao Sako

Faculty Advisor for students in the Natural Sciences