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Data Science Minor

Effective Fall 2024

Exclusions:

Students with a Data Science minor may not declare a Data Science major. All other majors can minor in Data Science, but certain majors are subject to a double-counting rule. Please speak with a department advisor for more information. 

Advising

Students wishing to pursue a minor in Data Science should discuss their plans with a Statistics Department undergraduate advisor. See the department website for contact information: lsa.umich.edu/stats/undergraduate-students/advising.

Prerequisites

  1. Introductory data science (DATASCI 101). This prerequisite can also be met by satisfying both of: (a) Introductory statistics  (STATS 180, 250, 280, 412, 426; IOE 265; QMSS 201; TO 301; ECON 451; (b) Introductory computer science (EECS 180, 183, ENGR 101, or ENGR 151).
  2. Calculus I (MATH 115 or 120 or 185 or 275 or 295) 
  3. Calculus II (MATH 116 or 121 or 156 or 186 or 276 or 296)

To declare, students should have completed prerequisites (1) Introductory data science and (2) Calculus I. Prerequisite (3), Calculus II, is not required to declare, but must be completed prior or concurrent with DATASCI 306.

Requirements

Minimum Credits: 15

The minor program in Data Science (DataSci) 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.

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

No more than one course counted toward the 15 credit requirement for the DataSci minor can be used to also meet a requirement, either a program requirement or prerequisite, in a major for which the student is declared. If students have completed more than one DataSci minor core course as part of their major(s) then they must complete DataSci minor electives to reach the 15 credit minimum. 

For this double counting rule, courses used to meet a College of Engineering (CoE) core requirement are counted as prerequisites for a major; however, courses used to meet a CoE flexible technical elective are not considered part of the major. 

Data Science Application Electives: Courses from the following list can be taken to meet the credit requirement. Ask an adviser if you have a course involving data science which you think should be considered for addition to this list. A student who has declared a Data Science minor and is not a major in Computer Science, Mathematics or Statistics takes 15 credits of core courses and 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.

CLIMATE 324/ SPACE 324. Instrumentation for Atmospheric and Space Science
CLIMATE 423/ SPACE 423. Data Analysis and Visualization for Geoscientists
DATASCI 315. Statistics and Artificial Intelligence
EARTH 408/ENVIRON 403: Introduction to GIS in the Earth Sciences
ENVIRON 473/ANTHROBIO 463/PSYCH 463: Statistical Modeling in R
QMSS 301: Quantitative Social Analysis and Big Data
SI 311 (Topics course, section 3): Sports Analytics
SI 340: Experiment Design and Analyses
SOC 310: Sociological Research Methods
STATS/BIOSTAT 449: Topics in Biostatistics
STATS 470: Design of Experiments
ASTRO 361: Astronomical Techniques
ASTRO 406: Computational Astrophysics
BIOINF 463/BIOPHYS 463/MATH 463: Mathematical Modeling in Biology
BIOPHYS 445/CMPLXSYS 445/PHYSICS 445: Introduction to Information Theory for the Natural Sciences
CHE 431: Engineering Statistics and Problem Solving
CMPLXSYS 466/EEB 466/MATH 466: Mathematical Ecology
CMPLXSYS 510/MATH 550: Introduction to Adaptive Systems
COGSCI 445/LING 445: Machine Learning for NLP
ECON 258: Topics in Applied Data Analysis
ECON 309: Experimental Economics
ECON 409: Game Theory
EEB 315/ENVIRON 315: Ecology and Evolution of Complex Disease
EEB 391: Introduction to Evolution: Quantitative Approach
EEB 408: Modeling for Ecology and Evolutionary Biology
EEB 430/CMPXSYS 430: Modeling Infectious Diseases
HONORS 365/PHYSICS 365/SI 365: Cyberscience: Computational Science and the Rise of the Fourth Paradigm
IOE 413: Optimization Modeling in Health Care
IOE 465: Design of Experiments
IOE 466/MFG 466: Statistical Quality Control
MKT 418: Marketing Analytics
POLSCI 300: Quantitative Empirical Methods of Political Science
POLSCI 387: Comparative Analysis of Government Institutions
POLSCI 485: Elections Forensic
SM 450: Introduction to Sports Analytics

Constraints

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.

Residency

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.

Data Science (Minor) (Fall 2023 - Summer 2024)

Effective Fall 2023

Exclusions:

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.

Advising

Students wishing to pursue a minor in Data Science should discuss their plans with a Statistics Department undergraduate advisor. See the department website for contact information: lsa.umich.edu/stats/undergraduate-students/advising.

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.

Requirements

Minimum Credits: 15

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.


Core Requirements:
1. Core data science: DATASCI 306. Statistics majors can double-count this with their major, if this is the only course double-counted.
2. Computer science: EECS 280. Computer Science majors can double-count this with their major prerequisite, if this is the only course double-counted.
3. Mathematics: MATH 214 or 217 or 417. Mathematics and statistics majors can double-count this with their major prerequisite, if this is the only course
double-counted.
4. Statistics: STATS 401 or 413 or ECON 452. Statistics majors can double-count STATS 413 with the major, if this is the only course double-counted.

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.


CLIMATE 324/ SPACE 324. Instrumentation for Atmospheric and Space Science
CLIMATE 423/ SPACE 423. Data Analysis and Visualization for Geoscientists
DATASCI 315. Statistics and Artificial Intelligence
EARTH 408/ENVIRON 403: Introduction to GIS in the Earth Sciences
ENVIRON 473/ANTHROBIO 463/PSYCH 463: Statistical Modeling in R
QMSS 301: Quantitative Social Analysis and Big Data
SI 311 (Topics course, section 3): Sports Analytics
SI 340: Experiment Design and Analyses
SOC 310: Sociological Research Methods
STATS/BIOSTAT 449: Topics in Biostatistics
STATS 470: Design of Experiments
ASTRO 361: Astronomical Techniques
ASTRO 406: Computational Astrophysics
BIOINF 463/BIOPHYS 463/MATH 463: Mathematical Modeling in Biology
BIOPHYS 445/CMPLXSYS 445/PHYSICS 445: Introduction to Information Theory for the Natural Sciences
CHE 431: Engineering Statistics and Problem Solving
CMPLXSYS 466/EEB 466/MATH 466: Mathematical Ecology
CMPLXSYS 510/MATH 550: Introduction to Adaptive Systems
COGSCI 445/LING 445: Machine Learning for NLP
ECON 258: Topics in Applied Data Analysis
ECON 309: Experimental Economics
ECON 409: Game Theory
EEB 315/ENVIRON 315: Ecology and Evolution of Complex Disease
EEB 391: Introduction to Evolution: Quantitative Approach
EEB 408: Modeling for Ecology and Evolutionary Biology
EEB 430/CMPXSYS 430: Modeling Infectious Diseases
HONORS 365/PHYSICS 365/SI 365: Cyberscience: Computational Science and the Rise of the Fourth Paradigm
IOE 413: Optimization Modeling in Health Care
IOE 465: Design of Experiments
IOE 466/MFG 466: Statistical Quality Control
MKT 418: Marketing Analytics
POLSCI 300: Quantitative Empirical Methods of Political Science
POLSCI 387: Comparative Analysis of Government Institutions
POLSCI 485: Elections Forensic
SM 450: Introduction to Sports Analytics

 

Constraints

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.

Residency

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.