Data Science

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 Bachelor of Science in Data Science. 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 Bachelor of Science in 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.

(120 hours minimum)

Dearborn Discovery Core Requirement

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.

Foundational Studies

Written and Oral Communication (GEWO) – 6 Credits

Upper Level Writing Intensive (GEWI) – 3 Credits

Quantitative Thinking and Problem Solving (GEQT) – 3 Credits

Critical and Creative Thinking (GECC) – 3 Credits

Areas of Inquiry

Natural Science (GENS) – 7 Credits

  • Lecture/Lab Science Course
  • Additional Science Course

Social and Behavioral Analysis (GESB) – 9 Credits

Humanities and the Arts (GEHA) – 6 Credits

Intersections (GEIN) – 6 Credits

Capstone

Capstone (GECE) – 3 Credits

Concentration Requirements

A candidate for the degree Bachelor of Science in Data Science is required to pursue scholastic quality and to complete satisfactorily the following program of study:

In addition to completion of the Dearborn Discovery Core, the following courses are required to earn a B.S. degree in Data Science from UM-Dearborn.

General Requirements
Tech Writing for Engineers (Also fulfills 3 credits of DDC Written and Oral Communication)
Basic Requirements
MATH 115Calculus I4
MATH 116Calculus II4
MATH 215Calculus III4
MATH 227Introduction to Linear Algebra3
Natural Science
Two course lab science sequence, choose from:8
BIOL 130
BIOL 140
Intro Org and Environ Biology
and Intro Molec & Cellular Biology
4-8
CHEM 134
CHEM 136
General Chemistry IA
and General Chemistry IIA
8
GEOL 118
GEOL 218
Physical Geology
and Historical Geology
8
PHYS 125
PHYS 126
Introductory Physics I
and Introductory Physics II
8
PHYS 150
PHYS 151
General Physics I
and General Physics II
8
Ethics
HHS 470Information Science and Ethics3
Business Course
ENGR 400Appl Business Tech for Engr (Fulfills 3 credits of DDC Intersections)3
or ENT 400 Entrepreneurial Thinking&Behav
Data Science Applications18
Students should complete 18 credit hours in one of the following analytics areas listed below. Application area courses must be approved in advance by Department Chair.
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.
Business Analytics
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)
Computational Analytics
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.
Data Science Core
CIS 1501CS I for Data Scientists4
CIS 2001CS II for Data Scientists4
Take one of the following three courses:4
Discrete Structures I
Discrete Math Meth Comptr Engr
Applied Combinatorics
CIS 350Data Struc and Algorithm Anlys4
ECE 3100Data Science I4
CIS 3200Data Science II4
CIS 422Massive Data Management4
STAT 305Intro. to Data Science3
IMSE 317Eng Probability and Statistics3
or STAT 325 Applied Statistics I
STAT 430Applied Regression Analysis3
Data Science Capstone
CIS 4971Cap Sem for Data Sci I2
CIS 4972Cap Proj for Data Sci II2
Data Science Electives9-10
Choose 9-10 credits from list below
Discrete Structures II
Software Engineering I
Dec Support and Exp Systems
Information Systems
Intro to Artificial Intel
Computational Learning
Data Security and Privacy
Introduction to Simulation
Digi Content Protec
Cloud Computing
Machine Learning in Engin
Experiential Honors Prof. Prac
Exper Honors Directed Research
Exper Hnrs Dir Dsgn
Intro to Operations Research
Eng Economy and Dec Anlys
Simulation in Systems Design
Prod, Inven Control & Lean Mfg
Stochastic Processes
Mathematical Statistics
Mathematical Modeling
Intro to Numerical Analysis
Matrix Computation
Statistical Computing
Design and Analysis of Expermt
Multivariate Stat Analysis
Time Series Analysis
General Electives
As needed to get a minimum of 120 credits for graduation