Effective Winter 2023
Exclusions:Those completing the major in Data Science may not earn a minor in Computer Science or Statistics.
Advising
Faculty advisors are available on both Central and North campuses with a common coordinator across the two programs.
Grade Policies
A grade of C or higher is required for all the required courses including the four required mathematics courses, all the EECS and STATS courses used toward the degree requirements, all the advanced technical electives in Data Science used toward the degree requirements and the capstone experience course.
The grade requirement applies to these courses irrespective of whether they are pre-major or major requirements.
Prerequisites
(each with minimum grade of C or higher)
- Calculus: MATH 115, 116, and 215 (each competed with a minimum grade of C or higher)
- Linear Algebra: MATH 214 or 217 (competed with a minimum grade of C or higher)
- Introductory Programming: One of EECS 183, ENGR 101, or ENGR 151
Requirements
A minimum of 42 credits is required (each with a minimum grade of C or higher), distributed as follows.
- Core:
- Computing and Discrete Mathematics
- EECS 203: Discrete Mathematics (preferred)
or
MATH 465: Introduction to Combinatorics - EECS 280: Programming and Elementary Data Structures.
- EECS 203: Discrete Mathematics (preferred)
- Computing and Statistics
- EECS 281: Data Structures and Algorithms.
- STATS 412: Introduction to Probability and Statistics.
- STATS 413: The General Linear Model and Its Applications
- Machine learning and data mining (minimum 4 credits):
- EECS 445: Machine Learning
or - STATS 415: Data Mining
- EECS 445: Machine Learning
- Data management and applications (minimum 4 credits):
- EECS 484: Database Management Systems
or - EECS 485: Web Database and Information Systems
- EECS 484: Database Management Systems
- Data Sciences Applied to a Domain (minimum 4 credits): A student must take at least one 400-level or higher course in which data science techniques are applied to a domain area.
- 400+ courses in Statistics and CSE on analytics in healthcare human behavioral analytics, financial analytics
- 400+ level courses in bioinformatics (specify: is this bioinformatics courses in any SUBJECT or courses in BIOINF)
- Computing and Discrete Mathematics
- Capstone Experience. One course of at least 4 credits approved as satisfying the Data Science Capstone Experience requirement. STATS 485 and the proposed Data Science-oriented CSE courses that also meet the Major Design Experience (MDE) requirements as playing this role.
If a student takes a required course that can also be used to provide capstone experience, the student must either not double count the credits or make up any overlapping credits by taking advanced elective courses. - Advanced Technical Electives in Data Science: At least 8 credits of advanced technical electives (at the 300-level or higher) that build on the foundation provided by the core courses and includes courses in data collection methods, scientific visualization, algorithms, security and privacy, mathematical modeling in biology, biostatistics, and optimization techniques. These courses must be selected from the list of courses below, or other courses by exception selected with advisor approval prior to taking the course.
- BIOINF 463 / BIOPHYS 463 / MATH 463: Mathematical Modeling in Biology
- BIOINF 527: Introduction to Bioinformatics & Computational Biology
- BIOINF 528: Structural Bioinformatics
- BIOINF 545 / STATS 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- BIOINF 547 / MATH 547 / STATS 547: Probabilistic Modeling in Bioinformatics
- BIOPHYS 463 / BIOINF 463 / MATH 463: Mathematical Modeling in Biology
- BIOSTAT 449 / STATS 449: Topics in Biostatistics
- BIOSTAT 646 / BIOINF 545 / STATS 545: High Throughput Molecular Genomic and Epigenomic Data Analysis
- COGSCI 445: Machin Learn for NLP
- EECS 388: Introduction to Computer Security
- EECS 442: Computer Vision
- EECS 444: Analysis of Societal Networks
- EECS 449: Conversational Artificial Intelligence
- EECS 467: Autonomous Robotics
- EECS 471: Applied Parallel Programming with GPUs
- EECS 476: Data Mining
- EECS 477: Introduction to Algorithms
- EECS 484: Database Management Systems
- EECS 485: Web Database and Information Systems
- EECS 487: Introduction to Natural Language Processing
- EECS 492: Introduction to Artificial Intelligence
- EECS 498: Special Topics (approved sections only. By default, EECS 498 sections will not count towards the Data Science advanced technical electives)
- EECS 505: Computational Data Science and Machine Learning
- EECS 545: Machine Learning
- EECS 549 / SI 650: Information Retrieval
- IOE 310: Introduction to Optimization Methods
- IOE 413: Optimization Modeling in Health Care
- MATH 420: Advanced Linear Algebra
- MATH 463 / BIOINF 463 / BIOPHYS 463: Mathematical Modeling in Biology
- MATH 472: Numerical Methods with Financial Applications
- MATH 547 / STATS 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- MATH 548 / STATS 548: Computations in Probabilistic Modeling in Bioinformatics
- ROB 320: Robot Operating Systems
- SI 649: Information Visualization
- SI 650 / EECS 549: Information Retrieval
- STATS 406: Introduction to Statistical Computing
- STATS 415: Data Mining and Statistical Learning
- STATS 426: Introduction to Theoretical Statistics
- STATS 430: Applied Probability
- STATS 449 / BIOSTAT 449: Topics in Biostatistics
- STATS 451: Bayesian Data Analysis
- STATS 470: Introduction to Design of Experiments
- STATS 480: Survey Sampling Techniques
- STATS 531: Analysis of Time Series
- STATS 545 / BIOINF 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- STATS 547 / MATH 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- STATS 548 / MATH 548: Computations in Probabilistic Modeling in Bioinformatics
Other Department Policies
For the purposes of fulfilling the 60-credits outside of the major requirement, all course work from the home departments of EECS and Statistics (EECS, STATS, and DATASCI subject areas) are to be considered inside the major department.
For the purposes of calculating the major GPA, all course work from the home departments of EECS and Statistics (EECS, STATS, and DATASCI subject areas) are to be included in the major GPA.
Dual Majors with Computer Science
For a dual major with Computer Science, the student will need to take an additional 14 credits in pertinent technical subjects, with advisor approval in both Computer Science and Data Science, beyond satisfying the requirements for each of the majors.
Residency
A minimum of fifteen (15) credits for the major must be taken on the Ann Arbor campus.
Distribution Policy
No course used to fulfill a major requirement may be used toward the LSA Distribution Requirement. In addition, courses in the STATS, DATASCI and EECS subject areas may not be used toward the Distribution Requirement.
Honors
Any LSA Data Science student with a current grade point average of at least 3.4 may apply for admission to the LSA Data Science Honors major program. Such application is made through a Statistics Department undergraduate advisor. Students in the Honors program must complete the regular major program with an overall GPA of at least 3.5. In addition, LSA Data Science Honors majors must elect the Senior Honors Seminar (STATS 499) and complete a project or a thesis under the direction of a member of the Statistics Department or EECS faculty.
Data Science (Major) (Winter 2021 - Fall 2022)
Effective Winter 2021
Exclusions:Those completing the major in Data Science may not earn a minor in Computer Science or Statistics.
Advising
Faculty advisors are available on both Central and North campuses with a common coordinator across the two programs.
Grade Policies
A grade of C or higher is required for all the required courses including the four required mathematics courses, all the EECS and STATS courses used toward the degree requirements, all the advanced technical electives in Data Science used toward the degree requirements and the capstone experience course.
The grade requirement applies to these courses irrespective of whether they are pre-major or major requirements.
Prerequisites
(each with minimum grade of C or higher)
- Calculus: MATH 115, 116, and 215 (each competed with a minimum grade of C or higher)
- Linear Algebra: MATH 214 or 217 (competed with a minimum grade of C or higher)
- Introductory Programming: One of EECS 183, ENGR 101, or ENGR 151
Requirements
A minimum of 42 credits is required (each with a minimum grade of C or higher), distributed as follows.
- Core:
- Computing and Discrete Mathematics
- EECS 203: Discrete Mathematics (preferred)
or
MATH 465: Introduction to Combinatorics - EECS 280: Programming and Elementary Data Structures.
- EECS 203: Discrete Mathematics (preferred)
- Computing and Statistics
- EECS 281: Data Structures and Algorithms.
- STATS 412: Introduction to Probability and Statistics.
- STATS 413: The General Linear Model and Its Applications
- Machine learning and data mining (minimum 4 credits):
- EECS 445: Machine Learning
or - STATS 415: Data Mining
- EECS 445: Machine Learning
- Data management and applications (minimum 4 credits):
- EECS 484: Database Management Systems
or - EECS 485: Web Database and Information Systems
- EECS 484: Database Management Systems
- Data Sciences Applied to a Domain (minimum 4 credits): A student must take at least one 400-level or higher course in which data science techniques are applied to a domain area.
- 400+ courses in Statistics and CSE on analytics in healthcare human behavioral analytics, financial analytics
- 400+ level courses in bioinformatics (specify: is this bioinformatics courses in any SUBJECT or courses in BIOINF)
- Computing and Discrete Mathematics
- Capstone Experience. One course of at least 4 credits approved as satisfying the Data Science Capstone Experience requirement. STATS 485 and the proposed Data Science-oriented CSE courses that also meet the Major Design Experience (MDE) requirements as playing this role.
If a student takes a required course that can also be used to provide capstone experience, the student must either not double count the credits or make up any overlapping credits by taking advanced elective courses. - Advanced Technical Electives in Data Science: At least 8 credits of advanced technical electives (at the 300-level or higher) that build on the foundation provided by the core courses and includes courses in data collection methods, scientific visualization, algorithms, security and privacy, mathematical modeling in biology, biostatistics, and optimization techniques. These courses must be selected from the list of courses below, or other courses by exception selected with advisor approval prior to taking the course.
- BIOINF 463 / BIOPHYS 463 / MATH 463: Mathematical Modeling in Biology
- BIOINF 527: Introduction to Bioinformatics & Computational Biology
- BIOINF 528: Structural Bioinf
- BIOINF 545 / STATS 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- BIOINF 547 / MATH 547 / STATS 547: Probabilistic Modeling in Bioinformatics
- BIOPHYS 463 / BIOINF 463 / MATH 463: Mathematical Modeling in Biology
- BIOSTAT 449 / STATS 449: Topics in Biostatistics
- BIOSTAT 646 / BIOINF 545 / STATS 545: High Throughput Molecular Genomic and Epigenomic Data Analysis
- COGSCI 445: Machin Learn for NLP
- EECS 388: Introduction to Computer Security
- EECS 442: Computer Vision
- EECS 467: Autonomous Robotics
- EECS 477: Introduction to Algorithms
- EECS 484: Database Management Systems
- EECS 485: Web Database and Information Systems
- EECS 492: Introduction to Artificial Intelligence
- EECS 498: Special Topics (approved sections only. By default, EECS 498 sections will not count towards the Data Science advanced technical electives)
- EECS 545: Machine Learning
- EECS 549 / SI 650: Information Retrieval
- IOE 310: Introduction to Optimization Methods
- IOE 413: Optim Mod Hlth Care
- MATH 463 / BIOINF 463 / BIOPHYS 463: Mathematical Modeling in Biology
- MATH 547 / STATS 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- MATH 548 / STATS 548: Computations in Probabilistic Modeling in Bioinformatics
- SI 639: Web Archiving
- SI 649: Information Visualization
- SI 650 / EECS 549: Information Retrieval
- STATS 403: Introduction to Quantitative Research Methods
- STATS 406: Introduction to Statistical Computing
- STATS 414: Special Topics in Statistics, section titled “ Introduction to Bayesian Data Analysis”
- STATS 426: Introduction to Theoretical Statistics
- STATS 449 / BIOSTAT 449: Topics in Biostatistics
- STATS 470: Introduction to Design of Experiments
- STATS 480: Survey Sampling Techniques
- STATS 508: Statistical Analysis of Financial Data
- STATS 531: Analysis of Time Series
- STATS 545 / BIOINF 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- STATS 547 / MATH 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- STATS 548 / MATH 548: Computations in Probabilistic Modeling in Bioinformatics
Other Department Policies
For the purposes of fulfilling the 60-credits outside of the major requirement, all course work from the home departments of EECS and Statistics (EECS, STATS, and DATASCI subject areas) are to be considered inside the major department.
For the purposes of calculating the major GPA, all course work from the home departments of EECS and Statistics (EECS, STATS, and DATASCI subject areas) are to be included in the major GPA.
Dual Majors with Computer Science
For a dual major with Computer Science, the student will need to take an additional 14 credits in pertinent technical subjects, with advisor approval in both Computer Science and Data Science, beyond satisfying the requirements for each of the majors.
Residency
A minimum of fifteen (15) credits for the major must be taken on the Ann Arbor campus.
Distribution Policy
No course used to fulfill a major requirement may be used toward the LSA Distribution Requirement. In addition, courses in the STATS, DATASCI and EECS subject areas may not be used toward the Distribution Requirement.
Honors
Any LSA Data Science student with a current grade point average of at least 3.4 may apply for admission to the LSA Data Science Honors major program. Such application is made through a Statistics Department undergraduate advisor. Students in the Honors program must complete the regular major program with an overall GPA of at least 3.5. In addition, LSA Data Science Honors majors must elect the Senior Honors Seminar (STATS 499) and complete a project or a thesis under the direction of a member of the Statistics Department or EECS faculty.
Data Science (Major) (Winter 2019 - Fall 2020)
Effective Winter 2019
Exclusions:Those completing the major in Data Science may not earn a minor in Computer Science or Statistics.
Advising
Faculty advisors are available on both Central and North campuses with a common coordinator across the two programs.
Grade Policies
A grade of C or higher is required for all the required courses including the four required mathematics courses, all the EECS and STATS courses used toward the degree requirements, all the advanced technical electives in Data Science used toward the degree requirements and the capstone experience course.
The grade requirement applies to these courses irrespective of whether they are pre-major or major requirements.
Prerequisites
(each with minimum grade of C or higher)
- Calculus: MATH 115, 116, and 215 (each competed with a minimum grade of C or higher)
- Linear Algebra: MATH 214 or 217 (competed with a minimum grade of C or higher)
- Introductory Programming: One of EECS 183, ENGR 101, or ENGR 151
Requirements
A minimum of 42 credits is required (each with a minimum grade of C or higher), distributed as follows.
- Core:
- Computing and Discrete Mathematics
- EECS 203: Discrete Mathematics (preferred)
or
MATH 465: Introduction to Combinatorics - EECS 280: Programming and Elementary Data Structures.
- EECS 203: Discrete Mathematics (preferred)
- Computing and Statistics
- EECS 281: Data Structures and Algorithms.
- STATS 412: Introduction to Probability and Statistics.
- STATS 413: The General Linear Model and Its Applications
- Machine learning and data mining (minimum 4 credits):
- EECS 445: Machine Learning
or - STATS 415: Data Mining
- EECS 445: Machine Learning
- Data management and applications (minimum 4 credits):
- EECS 484: Database Management Systems
or - EECS 485: Web Database and Information Systems
- EECS 484: Database Management Systems
- Data Sciences Applied to a Domain (minimum 4 credits): A student must take at least one 400-level or higher course in which data science techniques are applied to a domain area.
- 400+ courses in Statistics and CSE on analytics in healthcare human behavioral analytics, financial analytics
- 400+ level courses in bioinformatics (specify: is this bioinformatics courses in any SUBJECT or courses in BIOINF)
- Computing and Discrete Mathematics
- Capstone Experience. One course of at least 4 credits approved as satisfying the Data Science Capstone Experience requirement. STATS 485 and the proposed Data Science-oriented CSE courses that also meet the Major Design Experience (MDE) requirements as playing this role.
If a student takes a required course that can also be used to provide capstone experience, the student must either not double count the credits or make up any overlapping credits by taking advanced elective courses. - Advanced Technical Electives in Data Science: At least 8 credits of advanced technical electives (at the 300-level or higher) that build on the foundation provided by the core courses and includes courses in data collection methods, scientific visualization, algorithms, security and privacy, mathematical modeling in biology, biostatistics, and optimization techniques. These courses must be selected from the list of courses below, or other courses by exception selected with advisor approval prior to taking the course.
- BIOINF 463 / BIOPHYS 463 / MATH 463: Mathematical Modeling in Biology
- BIOINF 527: Introduction to Bioinformatics & Computational Biology
- BIOINF 545 / STATS 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- BIOINF 547 / MATH 547 / STATS 547: Probabilistic Modeling in Bioinformatics
- BIOPHYS 463 / BIOINF 463 / MATH 463: Mathematical Modeling in Biology
- BIOSTAT 449 / STATS 449: Topics in Biostatistics
- BIOSTAT 646 / BIOINF 545 / STATS 545: High Throughput Molecular Genomic and Epigenomic Data Analysis
- EECS 388: Introduction to Computer Security
- EECS 442: Computer Vision
- EECS 467: Autonomous Robotics
- EECS 477: Introduction to Algorithms
- EECS 484: Database Management Systems
- EECS 485: Web Database and Information Systems
- EECS 492: Introduction to Artificial Intelligence
- EECS 498: Special Topics (approved sections only. By default, EECS 498 sections will not count towards the Data Science advanced technical electives)
- EECS 4xx: Data Science and Healthcare
- EECS 4xx: Data Science and Human Behavior and Emotion Analytics
- EECS 545: Machine Learning
- EECS 549 / SI 650: Information Retrieval
- IOE 310: Introduction to Optimization Methods
- MATH 463 / BIOINF 463 / BIOPHYS 463: Mathematical Modeling in Biology
- MATH 547 / STATS 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- MATH 548 / STATS 548: Computations in Probabilistic Modeling in Bioinformatics
- SI 639: Web Archiving
- SI 649: Information Visualization
- SI 650 / EECS 549: Information Retrieval
- STATS 403: Introduction to Quantitative Research Methods
- STATS 406: Introduction to Statistical Computing
- STATS 414: Special Topics in Statistics, section titled “ Introduction to Bayesian Data Analysis”
- STATS 426: Introduction to Theoretical Statistics
- STATS 449 / BIOSTAT 449: Topics in Biostatistics
- STATS 470: Introduction to Design of Experiments
- STATS 480: Survey Sampling Techniques
- STATS 508: Statistical Analysis of Financial Data
- STATS 531: Analysis of Time Series
- STATS 545 / BIOINF 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- STATS 547 / MATH 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- STATS 548 / MATH 548: Computations in Probabilistic Modeling in Bioinformatics
Other Department Policies
Dual Majors with Computer Science
For a dual major with Computer Science, the student will need to take an additional 14 credits in pertinent technical subjects, with advisor approval in both Computer Science and Data Science, beyond satisfying the requirements for each of the majors.
Distribution Policy
No course used to fulfill a major requirement may be used toward the LSA Distribution Requirement. In addition, courses in the STATS subject area may not be used toward the Distribution Requirement.Honors
Any LSA Data Science student with a current grade point average of at least 3.4 may apply for admission to the LSA Data Science Honors major program. Such application is made through a Statistics Department undergraduate advisor. Students in the Honors program must complete the regular major program with an overall GPA of at least 3.5. In addition, LSA Data Science Honors majors must elect the Senior Honors Seminar (STATS 499) and complete a project or a thesis under the direction of a member of the Statistics Department or EECS faculty.
Data Science (Major) (Fall 2016 - Fall 2018)
Effective Fall 2016
Exclusions:Those completing the major in Data Science may not earn a minor in Computer Science or Statistics.
Advising
Faculty advisors are available on both Central and North campuses with a common coordinator across the two programs.
Grade Policies
A grade of C or higher is required for all the required courses including the four required mathematics courses, all the EECS and STATS courses used toward the degree requirements, all the advanced technical electives in Data Science used toward the degree requirements and the capstone experience course.
The grade requirement applies to these courses irrespective of whether they are pre-major or major requirements.
Prerequisites
(each with minimum grade of C or higher)
- Calculus: MATH 115, 116, and 215 (each competed with a minimum grade of C or higher)
- Linear Algebra: MATH 214 or 217 (competed with a minimum grade of C or higher)
- Introductory Programming: One of EECS 183, ENGR 101, or ENGR 151
Requirements
A minimum of 42 credits is required (each with a minimum grade of C or higher), distributed as follows.
- Core:
- Computing and Discrete Mathematics
- EECS 203: Discrete Mathematics (preferred)
or
MATH 465: Introduction to Combinatorics - EECS 280: Programming and Elementary Data Structures.
- EECS 203: Discrete Mathematics (preferred)
- Computing and Statistics
- EECS 281: Data Structures and Algorithms.
- STATS 412: Introduction to Probability and Statistics.
- STATS 413: The General Linear Model and Its Applications
- Machine learning and data mining (minimum 4 credits):
- EECS 445: Machine Learning
or - STATS 415: Data Mining
- EECS 445: Machine Learning
- Data management and applications (minimum 4 credits):
- EECS 484: Database Management Systems
or - EECS 485: Web Database and Information Systems
- EECS 484: Database Management Systems
- Data Sciences Applied to a Domain (minimum 4 credits): A student must take at least one 400-level or higher course in which data science techniques are applied to a domain area.
- 400+ courses in Statistics and CSE on analytics in healthcare human behavioral analytics, financial analytics
- 400+ level courses in bioinformatics (specify: is this bioinformatics courses in any SUBJECT or courses in BIOINF)
- Computing and Discrete Mathematics
- Capstone Experience. One course of at least 4 credits approved as satisfying the Data Science Capstone Experience requirement. STATS 485 and the proposed Data Science-oriented CSE courses that also meet the Major Design Experience (MDE) requirements as playing this role.
If a student takes a required course that can also be used to provide capstone experience, the student must either not double count the credits or make up any overlapping credits by taking advanced elective courses. - Advanced Technical Electives in Data Science: At least 8 credits of advanced technical electives (at the 300-level or higher) that build on the foundation provided by the core courses and includes courses in data collection methods, scientific visualization, algorithms, security and privacy, mathematical modeling in biology, biostatistics, and optimization techniques. These courses must be selected from the list of courses below, or other courses by exception selected with advisor approval prior to taking the course.
- BIOINF 463 / BIOPHYS 463 / MATH 463: Mathematical Modeling in Biology
- BIOINF 527: Introduction to Bioinformatics & Computational Biology
- BIOINF 545 / STATS 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- BIOINF 547 / MATH 547 / STATS 547: Probabilistic Modeling in Bioinformatics
- BIOPHYS 463 / BIOINF 463 / MATH 463: Mathematical Modeling in Biology
- BIOSTAT 449 / STATS 449: Topics in Biostatistics
- BIOSTAT 646 / BIOINF 545 / STATS 545: High Throughput Molecular Genomic and Epigenomic Data Analysis
- EECS 388: Introduction to Computer Security
- EECS 442: Computer Vision
- EECS 467: Autonomous Robotics
- EECS 477: Introduction to Algorithms
- EECS 484: Database Management Systems
- EECS 485: Web Database and Information Systems
- EECS 492: Introduction to Artificial Intelligence
- EECS 498: Special Topics (approved sections only. By default, EECS 498 sections will not count towards the Data Science advanced technical electives)
- EECS 4xx: Data Science and Healthcare
- EECS 4xx: Data Science and Human Behavior and Emotion Analytics
- EECS 545: Machine Learning
- EECS 549 / SI 650: Information Retrieval
- IOE 310: Introduction to Optimization Methods
- MATH 463 / BIOINF 463 / BIOPHYS 463: Mathematical Modeling in Biology
- MATH 547 / STATS 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- MATH 548 / STATS 548: Computations in Probabilistic Modeling in Bioinformatics
- SI 639: Web Archiving
- SI 649: Information Visualization
- SI 650 / EECS 549: Information Retrieval
- STATS 403: Introduction to Quantitative Research Methods
- STATS 406: Introduction to Statistical Computing
- STATS 414: Special Topics in Statistics, section titled “ Introduction to Bayesian Data Analysis”
- STATS 426: Introduction to Theoretical Statistics
- STATS 449 / BIOSTAT 449: Topics in Biostatistics
- STATS 470: Introduction to Design of Experiments
- STATS 480: Survey Sampling Techniques
- STATS 508: Statistical Analysis of Financial Data
- STATS 531: Analysis of Time Series
- STATS 545 / BIOINF 545 / BIOSTAT 646: High Throughput Molecular Genomic and Epigenomic Data Analysis
- STATS 547 / MATH 547 / BIOINF 547: Probabilistic Modeling in Bioinformatics
- STATS 548 / MATH 548: Computations in Probabilistic Modeling in Bioinformatics
Other Department Policies
Dual Majors with Computer Science
For a dual major with Computer Science, the student will need to take an additional 14 credits in pertinent technical subjects, with advisor approval in both Computer Science and Data Science, beyond satisfying the requirements for each of the majors.
Distribution Policy
No course used to fulfill a major requirement may be used toward the LSA Distribution Requirement. In addition, courses in the STATS subject area may not be used toward the Distribution Requirement.Honors
Students are responsible for finding a faculty mentor whose research area aligns with the student’s interest and who is willing to supervise their project. Statistics and CSE will designate a Capstone Thesis course that can be used to satisfy both the Data Science Honors requirement in LSA and the Capstone Experience requirement.
- Complete a research project under the direction of a faculty mentor in Computer Science or Statistics (and an optional a co-advisor from any department) by registering for a Capstone Thesis course in EECS or STATS. (The Capstone Thesis course, if completed successfully, will also count toward the Capstone Experience requirement, irrespective of whether the Honors designation is awarded.)
- Write an original thesis report on the research project and make a public presentation of the work. Satisfy the advisor and a second reader that the thesis report and the public presentation are worthy of the Honors designation.
- 3.5 GPA in the major and pre-major courses
- 3.4 overall U-M GPA (at the time of graduation)
