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Major in Data Science

Data Science is a rapidly growing field providing students with exciting career paths, and opportunities for advanced study. The Data Science major gives students a foundation in those aspects of computer science, statistics, and mathematics that are relevant for analyzing and manipulating voluminous and/or complex data. Students majoring in Data Science will learn computer programming, data analysis and database systems, and will learn to think critically about the process of understanding data. Students will also take a capstone experience course that aims to synthesize the skills and knowledge learned in the various disciplines that encompass data science. The Data Science major is a rigorous program that covers the practical use of Data Science methods as well as the theoretical properties underpinning the performance of the methods and algorithms.

The Data Science major is open to students in the Colleges of LSA and Engineering. This document is intended for students pursuing the Data Science major in LSA. Students in the College of Engineering who are interested in Data Science should visit Undergraduate Program in Data Science site.

Advising

The LSA Data Science program office is located in 311 West Hall. To contact the program directly please e-mail datascience@umich.edu. Appointments with Data Science program advisors can be scheduled at 734-615-3789.

Program Requirements

The Data Science major in LSA consists of a total of 42 required credit hours, not including pre-requisites or pre-major courses. All courses must be completed with a minimum grade of C. Note that the EECS department limits students to two attempts for CS 203, CS 280, and CS 281.

Program Prerequisites

  • EECS 183 (4 credits): Introductory programming 
  • Math 115, 116, 215 (4 credits each): Calculus 1-3 
  • Math 214 or 217 (4 credits): Linear algebra

Program Core

  • EECS 203 (4 credits): Discrete Mathematics. Acceptable alternative: Math 465. 
  • EECS 280 (4 credits): Programming and Elementary Data Structures.
  • EECS 281 (4 credits): Data Structures and Algorithms.
  • STATS 412 (3 credits): Introduction to Probability and Statistics. 
  • STATS 413 (4 credits): Applied Regression Analysis (F16)

Additional required courses

  • Machine learning and data mining elective: EECS 445 or STATS 415. Note that credit is granted for only one of these courses.
  • Data management and applications elective: EECS 484 or EECS 485.
  • Data science applications elective (4 credits): The current list of courses that meet this requirement is availablehere.

Advanced Technical Electives

Eight credits of Advanced Technical Electives for Data Science. A list of the courses that meet this requirement are available here.

Capstone experience

A 4 credit capstone data science course must be taken, typically during the senior year. A list of regular courses meeting the capstone requirement is available here. Another way to meet the capstone experience requirement is to take an independent study (EECS 499 or Stat 489). The latter option will normally involve research in a core aspect of data science or research in a domain area making use of data science methods, possibly as part of an honors degree. The independent study may also document an internship experience that involved substantial activities relating to data science. Any path to meeting the capstone requirement other than pre-approved regular courses must be pre-approved by a Data Science advisor. The course grade for an independent study must be based on a final project report documenting the activities undertaken, and the report must be provided to the DS program office.

Declaring

Students can declare Data Science by visiting the Data Science program office in 311 West Hall. In order to declare the major, students should have already completed EECS 183 and Math 215 with grades of C or higher.

Students must complete the linear algebra prerequisite requirement, but can do this after declaring.

Honors

Students interested in pursuing an honors degree in Data Science should discuss the requirements with an advisor as early as possible. The primary requirement for the honors program is to complete an original research project under faculty supervision, and produce a written report of the findings. Specific requirements are:

  • The honors project and faculty advisor must be approved by a DS program advisor before commencing work.
  • Honors thesis research must be undertaken as part of a 3 or 4 credit independent study course, taken on a graded basis. These credits will count towards the required 42 credit hours for the Data Science major, and will also satisfy the capstone course requirement.
  • A copy of the honors thesis must be provided to the Data Science program office upon completion.
  • A 3.5 GPA in DS major and pre-major courses is required.
  • A 3.4 overall U-M GPA at time of graduation is required.

Minors and Double Majors

Students interested in obtaining a double major in LSA Computer Science and Data Science may only use a limited number of courses to meet requirements in both majors. Students considering such a double major should consult an advisor early in their academic career and are responsible for verifying that their plan is acceptable to both programs. Students majoring in Data Science are not eligible to minor in either Statistics or Computer Science.

Advising

The LSA Data Science program office is located in 311 West Hall. To contact the program directly please e-mail datascience@umich.edu. Appointments with Data Science program advisors can be scheduled at 734-615-3789.

Program Requirements

The Data Science major in LSA consists of a total of 42 required credit hours, not including pre-requisites or pre-major courses. All courses must be completed with a minimum grade of C. Note that the EECS department limits students to two attempts for CS 203, CS 280, and CS 281.

Program Prerequisites

  • EECS 183 (4 credits): Introductory programming 
  • Math 115, 116, 215 (4 credits each): Calculus 1-3 
  • Math 214 or 217 (4 credits): Linear algebra

Program Core

  • EECS 203 (4 credits): Discrete Mathematics. Acceptable alternative: Math 465. 
  • EECS 280 (4 credits): Programming and Elementary Data Structures.
  • EECS 281 (4 credits): Data Structures and Algorithms.
  • STATS 412 (3 credits): Introduction to Probability and Statistics. 
  • STATS 413 (4 credits): Applied Regression Analysis (F16)

Additional required courses

  • Machine learning and data mining elective: EECS 445 or STATS 415. Note that credit is granted for only one of these courses.
  • Data management and applications elective: EECS 484 or EECS 485.
  • Data science applications elective (4 credits): The current list of courses that meet this requirement is available here.

Advanced Technical Electives

Eight credits of Advanced Technical Electives for Data Science. A list of the courses that meet this requirement are available here.

Capstone experience

A 4 credit capstone data science course must be taken, typically during the senior year. A list of regular courses meeting the capstone requirement is available here. Another way to meet the capstone experience requirement is to take an independent study (EECS 499 or Stat 489). The latter option will normally involve research in a core aspect of data science or research in a domain area making use of data science methods, possibly as part of an honors degree. The independent study may also document an internship experience that involved substantial activities relating to data science. Any path to meeting the capstone requirement other than pre-approved regular courses must be pre-approved by a Data Science advisor. The course grade for an independent study must be based on a final project report documenting the activities undertaken, and the report must be provided to the DS program office.

Declaring

Students can declare Data Science by visiting the Data Science program office in 311 West Hall. In order to declare the major, students should have already completed EECS 183 and Math 215 with grades of C or higher.

Students must complete the linear algebra prerequisite requirement, but can do this after declaring.

Honors

Students interested in pursuing an honors degree in Data Science should discuss the requirements with an advisor as early as possible. The primary requirement for the honors program is to complete an original research project under faculty supervision, and produce a written report of the findings. Specific requirements are:

  • The honors project and faculty advisor must be approved by a DS program advisor before commencing work.
  • Honors thesis research must be undertaken as part of a 3 or 4 credit independent study course, taken on a graded basis. These credits will count towards the required 42 credit hours for the Data Science major, and will also satisfy the capstone course requirement.
  • A copy of the honors thesis must be provided to the Data Science program office upon completion.
  • A 3.5 GPA in DS major and pre-major courses is required.
  • A 3.4 overall U-M GPA at time of graduation is required.

Minors and double majors

Students interested in obtaining a double major in LSA Computer Science and Data Science may only use a limited number of courses to meet requirements in both majors. Students considering such a double major should consult an advisor early in their academic career and are responsible for verifying that their plan is acceptable to both programs. Students majoring in Data Science are not eligible to minor in either Statistics or Computer Science.