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. Note that courses are not necessarily taught every semester or every year.

#### Academic Advising

Students interested in declaring the data science minor should carefully review the requirements on this page. 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.

#### 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). Please allow for a few weeks for exceptions to be reviewed.

###### 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 below.

###### Exception for an Elective

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

- 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.
- 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. Either PSCI 1800 and CRIM 4002 could be counted, but both PSCI 1800 and CRIM 4002 could not be counted.
- You may count an additional intro programming course towards an elective, if it teaches a different programming language than the class you have taken for intro programming.
- 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. College AU16 courses are considered College courses.
- Students can count one non-technical AI or data ethics course towards an elective. Half-credit courses can count as students’ one ethics course.

### 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.)

###### R

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 |

###### Python

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.)

BIOL 2150 | Statistics for Biologists |

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.)

###### R

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 |

STAT 4700 | Data Analytics and Statistical Computing |

###### Python

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 |

PHYS 2200 | Applied Data Science- Deep Learning and Artificial Intelligence |

##### 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 | Sample Survey Design |

COMM 4190 | Talking AI Computational and Communication Approaches |

EESC 3376 | Climate Change and Big Data |

CIMS 2666 | Algorithmic Ethics |

CIS 4230 | Ethical Algorithm Design |

#### 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*

*Faculty Advisor for students in the Social Sciences*

*Faculty Advisor for students in the Natural Sciences*