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.
Program Educational Objectives:
- Our graduates will be successfully employed in Data Science related fields or other career paths, including industrial, academic, governmental, and non-governmental organizations, or will be successful graduate students in a program preparing them for such employment.
- Our graduates will lead and participate in culturally diverse and inclusive teams, becoming global and ethical collaborators.
- Our graduates will continue their professional development through, for example, obtaining continuing education credits, professional registration or certifications, or post-graduate study credits or degrees.
Student Outcomes:
To achieve the educational objectives of the program, graduates of the BS in Data Science program will have an ability to:
- Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders' needs.
Dearborn Discovery Core
Please see the Dearborn Discovery Core (General Education) webpage or additional information.
Foundational Studies
Writing and 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
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.
Major Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Prerequisite Courses | ||
| COMP 105 | Writing & Rhetoric I | 3 |
| COMP 270 | Tech Writing for Engineers (Also fulfills 3 credits of DDC Written and Oral Communication) | 3 |
| MATH 115 | Calculus I | 4 |
| MATH 116 | Calculus II | 4 |
| MATH 215 | Calculus III | 4 |
| MATH 227 | Introduction to Linear Algebra | 3 |
| CIS 1501 | CS I for Data Scientists | 4 |
| CIS 2001 | CS II for Data Scientists | 4 |
| One course from the following: | ||
| CIS 275 | Discrete Structures I | 4 |
| MATH 276 | Discrete Math Meth Comptr Engr | 4 |
| MATH 315 | Applied Combinatorics | 3 |
| Select one laboratory science sequence from the following: | 8 | |
| Intro Org and Environ Biology and Field Biology | ||
| General Chemistry IA and General Chemistry IIA | ||
| Physical Geology and Historical Geology | ||
| Introductory Physics I and Introductory Physics II | ||
| General Physics I and General Physics II | ||
| Data Science Major Core | ||
| CIS 350 | Data Struc and Algorithm Anlys | 4 |
| CIS 375 | Software Engineering I | 4 |
| ECE 3100 | Data Science I | 4 |
| CIS 3200 | Data Science II | 4 |
| CIS 422 | Massive Data Management | 4 |
| ENGR 400 | Appl Business Tech for Engr | 3 |
| or ENT 400 | Entrepreneurial Thinking&Behav | |
| HHS 470 | Information Science and Ethics | 3 |
| STAT 305 | Introduction to Data Science for All | 3 |
| STAT 325 | Applied Statistics I | 4 |
| or IMSE 317 | Eng Probability and Statistics | |
| STAT 430 | Applied Regression Analysis | 3 |
| CIS 4971 | Cap Sem for Data Sci I | 2 |
| CIS 4972 | Cap Proj for Data Sci II | 2 |
| Data Science Applications | 18 | |
| 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 18 credits from any of the following: Anthropology, Criminology and Criminal Justice, Economics, History, Political Science, Psychology, Sociology. Students must meet the prerequisites for each course. These 18 credits must be from the same subject area. | ||
| Business Analytics | ||
Take DS 310 (3) Data Mining for Business Intelligence, plus 15 credit hours in one of the following: Accounting, Finance, Information System Management, Marketing, Operation Management. Students must meet the prerequisites for each course. These 15 credits must be from the same subject area. | ||
| 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 Electives | 3-4 | |
| Choose 3-4 credits from list below | ||
| Discrete Structures II | ||
| Introduction to Natural Language Processing | ||
| Dec Support and Exp Systems | ||
| Information Systems | ||
| Text Mining and Information Retrieval | ||
| Wireless & Mobi Comp Security | ||
| Intro to Software Security | ||
| Intro to Artificial Intel | ||
| Computational Learning | ||
| Deep Learning | ||
| Edge Computing | ||
| Data Security and Privacy | ||
| Introduction to Simulation | ||
| Digi Content Protec | ||
| Cloud Computing | ||
| Introduction to Machine Learning | ||
| Experiential Honors Prof. Prac | ||
| Exper Honors Directed Research | ||
| Exper Hnrs Dir Dsgn | ||
| Intro to Operations Research | ||
| Eng Economy and Dec Anlys | ||
| Applied stat models in engin | ||
| Simulation in Systems Design | ||
| Prod, Inven Control & Lean Mfg | ||
| Probability | ||
| Stochastic Processes | ||
| Mathematical Statistics | ||
| Mathematics of Finance | ||
| Mathematical Modeling | ||
| Introduction to Numerical Analysis | ||
| Matrix Computation | ||
| Statistical Computing | ||
| Machine Learning and Computational Statistics | ||
| Design and Analysis of Expermt | ||
| Multivariate Stat Analysis | ||
| Time Series Analysis | ||
| General Electives | ||
| Any 100 to 400 level course, (that is, courses not on the No Credit list, which is found at the end of the CECS Student Handbook), as needed to get a minimum of 120 credits for graduation. | ||
Learning Goals
- Students will be able to manage large-scale, complex data.
- Students will be able to recognize and evaluate the opportunities, needs, and limitations of data.
- Students will be able to formulate and design data analytic solutions.
- Students will be able to interpret data analytics and communicate the implications to stakeholders.