With increasing availability of data, companies, governments, and nonprofits alike are striving to convert information into actionable information and insight. In the past, students trained in singular disciplines such as computer science, operations research, or statistics had the skill set needed to analyze the required data. But the “volume”, “velocity” and “variety” of today’s data and future data streams pose unique challenges and also creates unique opportunities. Present data sets requires more programming, mathematics/statistics, modeling skills, and domain knowledge than a traditional undergraduate curriculum offers. In fact, one of the obstacles that must be removed before government, business and social sectors are prepared to use large datasets to enhance their decision-making, is the acquisition of a trained workforce that can leverage it.
Decision makers require data and evidence before resources are committed. In the current environment, commitments are not made unless evidence supports that the opportunities are both cost effective and yield positive net benefits. Healthcare practitioners seek evidence-based medicine; social scientists engage in impact assessments; business analysts practice decision science and engineers and computer scientists desire facility with big data sets using a variety of statistical techniques.
The University of Michigan-Dearborn, with its strong Engineering, Mathematics, Social and Behavioral Sciences, and Business Management programs is in a strategic position to enhance both undergraduate and graduate education with data science course offerings and a data science major. UM-Dearborn’s recent addition of the Department of Health and Human Services is also uniquely positioned in time, developmental stage, and location, to benefit from data science offerings. In other words, a case could be made for data science programming that enhances student education and marketability in all four of UM-Dearborn’s Colleges--the College of Engineering; the College of Arts, Sciences and Letters; the College of Business and the newly formed College of Education, Health and Human Services.
The Data Science degree is housed within the College of Engineering and Computer Science. The interdisciplinary nature of this degree program will require resources from all academic units, namely the College of Business, the College of Engineering and Computer Science, the College of Arts, Sciences, and Letters and the College of Education, Health, and Human Services. Students in this program will take courses and be involved with scholarly activity from a number of departments and disciplines across campus including Management Studies, Computer and Information Science, and Health and Human Services, Behavioral Science, Social Science as well as the Mathematics and Engineering disciplines.
This program requires technical courses from each college on our campus and is highly multidisciplinary. Taking a multidisciplinary approach, the curriculum is designed to leverage existing courses on campus and combine these with foundational courses in data science. This creates synergy among academic units on campus, provides flexibility in scheduling, and allows for timely completion of the program. Students with varied backgrounds can take different courses to suit their needs, based on interest and guided by faculty advisors.
Dearborn Discovery Core Requirement
The minimum passing grade for a Dearborn Discovery Core (DDC) course is 2.0. The minimum GPA for the program is 2.0. In addition, the DDC permits any approved course to satisfy up to three credit hours within three different categories. Please see the General Education Program: The Dearborn Discovery Core section for additional information.
Areas of Inquiry
- Lecture/Lab Science Course
- Additional Science Course
|CIS 1501||CS I for Data Scientists||4|
|MATH 115||Calculus I||4|
|MATH 116||Calculus II||4|
|MATH 205||Calc III for Engin Students||3-4|
|or MATH 215||Calculus III|
|MATH 227||Introduction to Linear Algebra||3|
|Two courses, 7 credits, one of which is a laboratory course. Please consult the Dearborn Discovery Core.||7|
|HHS 470||Information Science and Ethics||3|
|ENGR 400||Appl Business Tech for Engr||3|
|or ENT 400||Entrepreneurial Thinking&Behav|
|Data Science Applications||18|
|Students should complete 18 credit hours in one of the following five analytics areas listed below. In addition, at least half of the elected courses selected should be designated as being in the analytics domain.|
|Applied Social and Behavioral Science Analytics|
Take an additional 18 credits from any of the following: Political Science, Economics, History, Criminal Justice, Sociology, Anthropology, and Psychology. Students must meet the prerequisites for each course. In addition, the 18 disciplinary credits must have the same prefix, e.g. POL, ECON, HIST, CRJ, SOC, ANTH, or PSYC. As an exception, a student may substitute 6 credits of GIS for 6 of the discipline specific credits.
Take DS 310 (3) Data Mining for Business Intelligence, plus 15 credit hours in one of the following: Accounting, Finance, Technology Management, and Supply Chain Management. Students must meet the prerequisites for the course. In addition, the additional 15 credit hours must have the same prefix, e.g. ACC, FIN, MKT, ITM, or OM)
Take an additional 18 credit hours from courses focusing on Applied Statistics, Mathematics or from CECS. The proposed coursework must be approved by a faculty advisor in the Department of Mathematics or CECS, respectively, prior to enrollment in the course.
|Health and Medicine Analytics|
Take an additional 18 credit hours from courses focusing on health and medicine. The proposed coursework must be approved by a faculty advisor in the Department of Health and Human Services prior to enrollment in the course.
|Natural and Physical Science Analytics|
Take an additional 18 credit hours from courses from one of the natural and physical sciences. Students must meet the prerequisites for the courses. In addition, the 18 disciplinary credits must have the same prefix, e.g. BIOL. The proposed coursework must be approved by a faculty advisor in the Department of Natural Sciences prior to enrollment in the course.
|Data Science Core|
|CIS 2001||CS II for Data Scientists||4|
|CIS 275||Discrete Structures I||4|
|or ECE 276||Discrete Math in Computer Engr|
|or MATH 276||Discrete Math Meth Comptr Engr|
|CIS 350||Data Struc and Algorithm Anlys||4|
|ECE 3100||Data Science I||4|
|CIS 3200||Data Science II||4|
|CIS 422||Massive Data Management||4|
|STAT 305||Intro. to Data Science||3|
|IMSE 317||Eng Probability and Statistics||3|
|or STAT 325||Applied Statistics I|
|STAT 326||Applied Statistics II||3|
|STAT 430||Applied Regression Analysis||3|
|Data Science Capstone|
|CIS 4971||Cap Sem for Data Sci I||2|
|CIS 4972||Cap Proj for Data Sci II||2|
|Data Science Electives||6-8|
|Discrete Structures II|
|Software Engineering II|
|Dec Support and Exp Systems|
|Intro to Artificial Intel|
|Introduction to Simulation|
|Digi Content Protec|
|Machine Learning in Engin|
|Intro to Operations Research|
|Eng Economy and Dec Anlys|
|Simulation in Systems Design|
|Prod, Inven Control & Lean Mfg|
|Intro to Numerical Analysis|
|Design and Analysis of Expermt|
|Multivariate Stat Analysis|
|Time Series Analysis|
|Total Credit Hours||101-106|