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. 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:

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. 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.
• All classes for the minor must be taken for credit. Students must receive a C+ or higher.

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

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
COMM 2550	Foundations in Data Science for Comm.

 

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
STAT 4310	Statistical Inference
MATH 3120	Linear Algebra
PSCI 3803	Advanced Statistical Methods in PSCI
GAFL 6110	Statistics for Public Policy

 

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
STAT 4730	Data Science Using ChatGPT

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	R/Python
COMM 3130	Computational Text Analysis for Communication Research	Python
ENVS 3700	GIS: Mapping Places & Analyzing Spaces	GIS
CIS 4210	Artificial Intelligence	Python
CIS 4500	Database and Information Systems
COGS 4290	Big Data, Memory and the Human Brain	Python
LING 2220	Phonetics II: Data Science
LING 2250	Computer Analysis and Modeling of Biological Signals and Systems
PHYS 2280	Physical Models of Biological Systems	Python
PSCI 3802	Political Polling	R
SOCI 2220	Health of Populations
STAT 4240	Text Analytics	Python
URBS 3300	GIS Applications in Social Science	GIS
ANTH 3307	Introduction to Digital Anthropology	GIS
MKTG 2120	Data Analysis for Marketing Decisions	R
ACCT 2700	Forensic Analytics	R
STAT 4750	Sample Survey Design	Python
COMM 4190	Talking AI Computational and Communication Approaches	Python
EESC 3376	Climate Change and Big Data	SQL
CIMS 2666	Algorithmic Ethics	Non-technical
CIS 4230	Ethical Algorithm Design
ANTH 1905	GIS for the Digital Humanities and Social Sciences	GIS
MKTG 3090	Experiments for Business Decision Making	R/Python
ECON 2310	Econometric Models & Methods	R