Minor in Data Science

Data science is an interdisciplinary field combining ideas from computer science, mathematics and statistics in order to collect and interpret data. The Data Science minor introduces the core concepts and techniques of data science while providing some training in each of the component areas. The program is set up so that students pursuing a major in computer science, mathematics or statistics are excused from corresponding parts of the technical training and instead study applications of data science. By contrast, students pursuing other majors will focus on technical skills in their minor, with the expectation that they will see relevant applications of data analysis within their own major.

Students with a Data Science minor may not declare a Data Science major. All other majors can minor in Data Science, subject to a double-counting rule: at most one course counting toward the 15 credits of the Data Science minor can be used as credit for a completed major or as a prerequisite for that major. Courses used for a major (or its prerequisites) can satisfy a core requirement for the minor, even if they are not used for credit toward the Data Science minor, and in that case additional electives can be taken to make up to 15 credits.

At least 12 of the 15 credits must be taken at the University of Michigan. Transfer credit will be considered for up to one course.

A Data Science minor who is not a major in Computer Science, Mathematics or Statistics takes 15 credits of core courses and so is not required to take any application electives. Such students are expected to see relevant data analysis applications within their own major field of study.

Requirements beginning Fall 2023

Prerequisites:

DATASCI 101 and Calc II (Math 116 or 121 or equivalent).*

The DATASCI 101 prerequisite is required to declare the minor, but is waived if students have both (i) introductory statistics (STATS 180, 250, 280, 412, 426; IOE 265; QMSS 201; TO 301; ECON 451), and (ii) introductory computer science (EECS 180, 183; ENGR 101).

*The Calc II prerequisite is not required to declare, but must be completed prior or concurrent with DATASCI 306.

The minor program in data science consists of 15 credits covering four core requirements. Each core requirement must be satisfied unless it is excused by a course used for a corresponding major and not double-counted.

Requirements:

Core Courses:

Core data science: DATASCI 306 or 406.
Computer science: EECS 280.
Mathematics: MATH 214 or 217 or 417 or ROB 101.
Statistics: STATS 401 or 413 or ECON 452.

Data Science Application Electives:

Courses from the following list can be taken to meet the credit requirement. Ask an adviser if you have in mind a course involving data science which you think should be considered for addition to this list. A Data Science minor who is not a major in Computer Science, Mathematics or Statistics takes 15 credits of core courses and so is not required to take any application electives. Such students are expected to see relevant data analysis applications within their own major field of study.

A list of courses that meet this requirement are available here.

More information on the minor double counting rule

The Data Science minor is subject to a double-counting rule which covers courses that are shared between the Data Science minor core courses and a student's major(s) (including major prerqequisites). The double counting rule doesn't apply to the prerequistes for the minor.

The following examples illustrate the rules:

A CoE computer science major can count DATASCI 306 and STATS 401 toward the 15 credits for the DataSci minor, whether or not these are used as flexible technical electives for the computer science major. Only one of EECS 280 and linear algebra can be counted toward the 15 credits. Therefore, the student must take 1 DataSci minor application elective.

An LSA computer science major, if they are not also a mathematics or statistics major, can obtain a DataSci minor without taking any application electives since only one DataSci minor core course coincides with a major requirement.

A statistics major, if they are not also a computer science major, must take EECS 280 and two DataSci minor application electives to obtain a DataSci minor. The other three core requirements for the DataSci major coincide with the statistics major requirements, so only one of them can be counted toward the 15 credits for the DataSci minor. The two DataSci minor application electives cannot be used for the statistics major.

Mathematics majors, who are not also statistics or computer science majors, do not usually need to take any DataSci application electives since these majors only have a one-course intersection with the DataSci minor core requirements. However, in some cases, Math majors may take Econ 452 in addition to linear algebra (Math 214 or 217) for their major. In this case, they would need one DataSci minor application elective to account for the two double-counted courses.

*Please note: For double majors, you are allowed a maximum of ONE double-counted course between the two majors, not one shared with each major.

Major	Double Counted Courses	Application Electives Needed
Statistics major	Datasci 306 or 406, Stats 413, Linear Algebra.	2
CS-Eng	Linear Algebra, EECS 280	1
CS-LSA	EECS 280	None
Other Majors	If one or less double-counted courses	None
*Statistics & CS double major	Datasci 306 or 406, Stats 413, Linear Algebra, EECS 280	3

Declaring

Students will need to follow the Statistics Department’s Declaration Process which includes using this Canvas link to enroll in the Statistics & Data Science Pre-declaration Tutorial. Completion of the tutorial will unlock the declaration request form.

To declare the Data Science minor a student must have DATASCI 101 and Calc II (Math 116 or 121 or equivalent).

*The DATASCI 101 prerequisite is required to declare the minor, but is waived if students have both (i) introductory statistics (STATS 180, 250, 280, 412, 426; IOE 265; QMSS 201; TO 301; ECON 451), and (ii) introductory computer science (EECS 180, 183; ENGR 101).