Requirements
Requirements for the Minor in Data Science
The five-course sequence will provide students with innovative analytical tools to approach problems and will improve their marketability — regardless of their major.
Prerequisite
Introductory statistics, including any of the following:
*SOC 30903: Statistics for Sociological Research
*ECON 30330: Statistics for Economics
GLAF 10003: Quantitative Methods for Global Affairs
MATH 30540: Mathematical Statistics
PSY 30100: Experimental Psychology I: Statistics
ACMS 10140: Elements of Statistics
ACMS 20340: Statistics for Life Sciences
ACMS 30440: Probability and Statistics
ACMS 30540: Mathematical Statistics
ITAO 20200/BAMG 20150: Statistical Inference in Business
POLS 40810 or 40811: Quantitative Political Analysis
BIOS 40411: Biostatistics
*includes seats reserved for MDSC students
Students who have taken an introductory statistics class that does not include multiple regression can enroll in the 1-credit ECON 30335: Introduction to Linear Regression to satisfy the prerequisite. For example, ACMS 10145 will require this extra credit.
Students may petition to have other statistics courses accepted to fulfill the requirement by emailing mdsc@nd.edu.
Higher-level statistical classes can be used to either fulfill the statistics requirement, or fulfill an elective requirement (ex: ACMS 30600 or ECON 30331)
1) Elements of Computing I: CSE 10101/CDT30010
Introduction to programming for students without prior programming experience. Programming structures suitable for basic computation. Elements of computer organization and networking. Development of programming skills including data manipulation, multimedia programming, and networking. Standards for exchange and presentation of data. Comprehensive programming experience using Python. The minor also accepts ACMS 20220: Scientific Computing with Python.
2) Introduction to Data Science: MDSC 20009/SOC 20009
This course will orient students to the field, to key concepts, to the types of questions addressed, to the technical aspects of data science, and to the process of making sense of data. Provides an overview from computer science, natural science, humanities, and social science perspectives. Prerequisite: CSE 10101
3, 4, & 5: Three Electives
Electives are available in a wide variety of areas, from philosophy to physics, and English to epidemiology. Students may choose a set of electives that enables them to specialize. There are at least two ways to think about specialization. Students may specialize in a particular phase of the data science workflow. For example, we accept three classes in data visualization. We also offer many classes in data analytics. Alternatively, students may specialize in data science applications within a thematic area or discipline.
Analytics Track
The data science minor-analytics track is designed for undergraduate students with a particular interest in the analytic/modeling phase of the data science workflow, and who have completed prerequisites of Calculus III and ACMS 30600 (or equivalent, as detailed below).
Requirements
Prerequisites
- MATH 20550: Calculus III (or equivalent)
- ACMS 30600: Mathematical Statistics1 (or equivalent)
Required Courses (6 Credits)
- CSE 10101: Elements of Computing I
- MDSC 20009: Introduction to Data Science
Approved Electives
Students in the Analytics Track must take 3 credits (3 courses) from the list of approved electives
- ACMS 40875: Statistical Methods in Data Mining1
- ACMS 30550: Mathematical Statistics1 (or ACMS 30540)
- ACMS 40842: Time Series1
- ACMS 40878: Statistical Computing in R1
- ACMS 40950: Topics in Statistics1
- ACMS 40852: Advanced Biostatistics1
- ACMS 40855: Spatio-Temporal Statistics1
- CSE 10102: Elements of Computing II
- PSY 40122: Machine Learning for Social and Behavior Research
- PSY 30109: R for Data Science2
- SOC 43919: Text Analysis for Social Science
Notes
ACMS 30600 is a prerequisite. Acceptable alternatives include 1) ECON 30331 if students also have demonstrated competency in R programming; 2) PSY 40120; and 3) other approved combinations of R programming, inference, and multiple regression. For approvals, please consult Prof. Alan Huebner, Director of Undergraduate Studies, ACMS.
PSY 30109 will not count if students have already taken ACMS 24215.
“The data science minor will offer students across the University the opportunity to understand the role of data generally as well as in their field of study, and to employ confidently the latest techniques that transform data into insights.”
—Patrick Flynn, professor and chair of the Department of Computer Science and Engineering