Requirements of Minor

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The five-course sequence will provide students with innovative analytical tools to approach problems and will improve their marketability — regardless of their major. 

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.

We also accept ITAO 30210: Data Analysis 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) Statistics

Introductory statistics, including any of the following:
*SOC 30903: Statistics for Sociological Research
*ECON 30330: Statistics for Economics
MATH 30540: Mathematical Statistics
PSY 30100: Experimental Psychology I: Statistics
ACMS 20340: Statistics for Life Sciences
ACMS 30440: Probability and Statistics
ACMS 30540: Mathemtatical Statistics
ITAO 20200/BAMG 20150: Statistical Inference in Business
POLS 40810: Quantitative Political Analysis
*includes seats reserved for MDSC students

Students may petition to have other statistics courses accepted to fulfill the requirement. Contact Mim Thomas (mthoma13@nd.edu).

If students are using the same statistics course to fulfill both the MDSC requirement and a college, university, major or other minor requirement, they must contact their dean or major advisor to see if an additional course (not another statistics course) is required or if the course can be double counted.

Higher level statistical classes can be used to either fulfill the statistics requirement, or fulfill an elective requirement (ex: ACMS 30600 or ECON 30331)

4 & 5) Two Topical 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. Student 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.


 

Flynn8714

“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