Effective Winter 2025
Advising
PCAS is the focus for introductory undergraduate education around computation in LSA. Students wishing to pursue a minor in Computing for Scientific Discovery should discuss their plans with a PCAS undergraduate advisor. See the department website for contact information: https://lsa.umich.edu/computingfor/Advising.html
Prerequisites
None
Requirements
Foundation Requirement: Students should choose one from the approved list
- DATASCI 101/STATS 206: Introduction to Data Science
- CMPLXSYS 100: Introduction to Complexity
- PHIL 183: Critical Reasoning
- QMSS 201: Introduction to Quantitative Methods in the Social Sciences
- SOC 210: Introduction to Statistics for Social Science, section 020 titled, "Data Analysis"
- STATS 250: Introduction to Statistics and Data Analysis
- STATS 280: Honors Introduction to Statistics and Data Analysis
- STATS 412: Introduction to Probability and Statistics
Introductory Programming Requirement: Students should choose one from the approved list
- COMPFOR/BIOLOGY 131: Python Programming for the Sciences
- COMPFOR/ANTHRBIO 133: Fundamentals of Scientific Computing in R
- DATASCI 306: Introduction to Statistical Computing
- EARTH 133: Programming in Earth and Environmental Sciences
- ENGR 101: Introduction to Computers and Programming
- ENGR 151: Accelerated Introduction to Computers and Programming
- EECS 183: Elementary Programming Concepts
- LING 123/COMPFOR 150: The ABCs of Python
- PHYSICS 104: Python Programming for Introductory Science Courses
- SI 106: Programs, Information and People
- SOC 251/CMPLXSYS 251: Computational Social Sciences
Thematic Courses Requirement: Students should choose 3 courses from any of the thematic areas below for a minimum 9 credits total; at least two courses should be at the 300-level or above
Domain application courses:
- ASTRO 361: Astronomical Techniques
- ASTRO 406: Computational Astrophysics
- BIOPHYS/BIOLOGY 233: Introduction to Quantitative and Computational Biology
- CHEM 462: Computational Chemistry Laboratory
- COGSCI/LING 445: Introduction to Machine Learning for Natural Language Processing
- ECON 251: Introduction to Statistics and Econometrics II
- ECON 452: Intermediate Introduction to Statistics and Econometrics II
- LING 441: Introduction to Computational Linguistics
- PHYSICS 411: Introduction to Computational Physics
- QMSS 301: Quantitative Social Science Analysis and Big Data
Information, modeling and inference courses:
- ASTRO 416: Data Science for Astrophysicists
- BIOPHYS/CMPLXSYS/MATH/PHYSICS 445: Entropy and Information: Concepts and Applications
- DATASCI 415: Data Mining and Statistical Learning
- ENVIRON/EARTH 309: GIS Explorations of the Past, Present, and Future
- EARTH 468: Data Analysis, Inference, and Estimation
Breadth courses:
- AMCULT/DIGITAL/SI/STS 410: Ethics and Information Technology
- PHIL 303: Introduction to Symbolic Logic
- PHIL 340: Minds and Machines
- PHIL 443: Foundations of Rational Choice Theory
Other coursework not represented here may be able to count towards the requirements of the minor. Students wishing to take alternative courses are encouraged to speak with an advisor to gain approval.
Residency
At least 9 of the credits must be taken at the University of Michigan Ann Arbor campus. Transfer credit will be considered but is subject to advisor approval.
Computing for Scientific Discovery (Minor) (Fall 2024)
Effective Fall 2024
Advising
PCAS is the focus for introductory undergraduate education around computation in LSA. Students wishing to pursue a minor in Computing for Scientific Discovery should discuss their plans with a PCAS undergraduate advisor. See the department website for contact information: https://lsa.umich.edu/computingfor/Advising.html
Grade Policies
Students may not elect the pass/fail grading option for courses that are meeting the minor requirements. The average GPA of all graded courses taken for the minor must be 2.0 or higher by the conclusion of the student's degree program.
Prerequisites
None
Requirements
Foundation Requirement: Students should choose one from the approved list
- DATASCI 101/STATS 206: Introduction to Data Science
- CMPLXSYS 100: Introduction to Complexity
- QMSS 201: Introduction to Quantitative Methods in the Social Sciences
- SOC 210: Introduction to Statistics for Social Science, section 020 titled, "Data Analysis"
- PHIL 183: Critical Reasoning
- STATS 250: Introduction to Statistics and Data Analysis
- STATS 280: Honors Introduction to Statistics and Data Analysis
Introductory Programming Requirement: Students should choose one from the approved list
- COMPFOR/BIOLOGY 131: Python Programming for the Sciences
- PHYSICS 104: Python Programming for Introductory Science Courses
- EARTH 133: Programming in Earth and Environmental Sciences
- SOC 251/CMPLXSYS 251: Computational Social Sciences
- EECS 183: Elementary Programming Concepts
- LING 123/COMPFOR 150: The ABCs of Python
Thematic Courses Requirement: Students should choose 3 courses from any of the thematic areas below for a minimum 9 credits total; at least two courses should be at the 300-level or above
Domain application courses:
- ASTRO 361: Astronomical Techniques
- ASTRO 406: Computational Astrophysics
- CHEM 462: Computational Chemistry Laboratory
- ECON 251: Introduction to Statistics and Econometrics II
- LING 441: Introduction to Computational Linguistics
- COGSCI/LING 445: Introduction to Machine Learning for Natural Language Processing
- PHYSICS 411: Introduction to Computational Physics
- QMSS 301: Quantitative Social Science Analysis and Big Data
Information and inference courses:
- BIOPHYS/CMPLXSYS/MATH/PHYSICS 445: Entropy and Information: Concepts and Applications.
- ENVIRON/EARTH 309: GIS Explorations of the Past, Present, and Future
- EARTH 468: Data Analysis, Inference, and Estimation
Breadth courses:
- AMCULT/DIGITAL/SI/STS 410: Ethics and Information Technology
- PHIL 303: Introduction to Symbolic Logic
- PHIL 340: Minds and Machines
- PHIL 443: Foundations of Rational Choice Theory
Other coursework not represented here may be able to count towards the requirements of the minor. Students wishing to take alternative courses are encouraged to speak with an advisor to gain approval.
Residency
At least 9 of the credits must be taken at the University of Michigan Ann Arbor campus. Transfer credit will be considered but is subject to advisor approval.
Computing for Scientific Discovery (Minor) (Winter 2024 - Summer 2024)
Effective Winter 2024
Advising
PCAS is the focus for introductory undergraduate education around computation in LSA. Students wishing to pursue a minor in Computing for Scientific Discovery should discuss their plans with a PCAS undergraduate advisor. See the department website for contact information: https://lsa.umich.edu/computingfor/Advising.html
Prerequisites
None
Requirements
Foundation Requirement: Students should choose one from the approved list
- DATASCI 101/STATS 206: Introduction to Data Science
- CMPLXSYS 100: Introduction to Complexity
- QMSS 201: Introduction to Quantitative Methods in the Social Sciences
- SOC 210: Introduction to Statistics for Social Science, section 020 titled, "Data Analysis"
- PHIL 183: Critical Reasoning
- STATS 250: Introduction to Statistics and Data Analysis
- STATS 280: Honors Introduction to Statistics and Data Analysis
Introductory Programming Requirement: Students should choose one from the approved list
- COMPFOR/BIOLOGY 131: Python Programming for the Sciences
- PHYSICS 104: Python Programming for Introductory Science Courses
- EARTH 133: Programming in Earth and Environmental Sciences
- SOC 251/CMPLXSYS 251: Computational Social Sciences
Thematic Courses Requirement: Students should choose 3 courses from any of the thematic areas below for a minimum 9 credits total; at least two courses should be at the 300-level or above
Domain application courses:
- ASTRO 361: Astronomical Techniques
- ASTRO 406: Computational Astrophysics
- CHEM 462: Computational Chemistry Laboratory
- ECON 251: Introduction to Statistics and Econometrics II
- LING 441: Introduction to Computational Linguistics
- COGSCI/LING 445: Introduction to Machine Learning for Natural Language Processing
- PHYSICS 411: Introduction to Computational Physics
- QMSS 301: Quantitative Social Science Analysis and Big Data
Information and inference courses:
- BIOPHYS/CMPLXSYS/MATH/PHYSICS 445: Entropy and Information: Concepts and Applications.
- ENVIRON/EARTH 309: GIS Explorations of the Past, Present, and Future
- EARTH 468: Data Analysis, Inference, and Estimation
Breadth courses:
- AMCULT/DIGITAL/SI/STS 410: Ethics and Information Technology
- PHIL 303: Introduction to Symbolic Logic
- PHIL 340: Minds and Machines
- PHIL 443: Foundations of Rational Choice Theory
Other coursework not represented here may be able to count towards the requirements of the minor. Students wishing to take alternative courses are encouraged to speak with an advisor to gain approval.
Residency
At least 9 of the credits must be taken at the University of Michigan Ann Arbor campus. Transfer credit will be considered but is subject to advisor approval.
