Data Science
The Data Science master's degree program is designed as a 30-credit hour interdisciplinary graduate program. The curriculum consists of required core courses and technical electives, providing opportunities to build knowledge and professional skills in various Data Science areas that are highly demanded in the current job market. This program can be completed fully online, in person, or a combination of both. Four specializations are recommended (not mandatory) for students with different interests in Data Science:
Computational Intelligence Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to solve complex data analytics problems through learning and adapting based on data.
Applications Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to develop effective data analytics solutions in selected application domains.
Business Analytics Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to apply intelligent strategies and technologies to support the collection, data analysis, presentation and dissemination of business information in enterprises.
Big Data Informatics Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to apply cutting-edge technologies and tools to tackle Big Data challenges that are essential for data processing and analytics in numerous applications.
Accelerated Master's Options for Undergraduate Students (4+1 Program)
The Computer and Information Science (CIS) department proposes the introduction of new accelerated master’s (4+1) programs designed to allow qualified undergraduate students to seamlessly transition into the department’s graduate programs. These programs will enable students to earn both a bachelor’s and a master’s degree in a reduced timeframe, enhancing their academic experience and providing a cost-effective pathway to advanced degrees.
Students enrolled in this option can take eligible 500-level courses during their junior and senior years, with up to 9 credit hours of such coursework being double-counted toward both degrees. Additionally, another 6 credit hours earned but not applied to the bachelor degree can later be counted toward the master’s degree. Depending on the number of graduate courses taken while working toward the bachelor program, students will need to complete 15-21 credit hours to finish the master’s program after earning their undergraduate degree.
BS in Computer and Information Science (CIS) or Software Engineering (SWE) can advance to MS in CIS, Data Science (DATA), Artificial Intelligence (AI), Software Engineering (SWE) or Cybersecurity and Information Assurance (CIA).
A maximum of 9 credits from combined undergraduate and graduate courses can be double-counted toward both the undergraduate and graduate degrees. This will streamline the process and reduce the total credit load required to complete both degrees. Any 500-level course that is part of the respective master’s program can be selected for double-counting, as shown in the following table. If there is a mismatch in credit hours between the combined course pair, only the smaller number of credits will be counted.
In addition, students may apply up to 6 additional credits of 500-level courses toward their master’s degree, taken during their undergraduate study, though these credits cannot be double-counted. This allows students to make substantial progress toward their graduate degree while still completing their undergraduate requirements. However, the courses of these six additional credits should be listed in the corresponding graduate program.
To ensure that students entering the 4+1 programs are well-prepared for the academic rigor of graduate-level coursework, the following admission criteria will apply:
- A minimum cumulative GPA of 3.2 at the University of Michigan-Dearborn after completing at least 60 credits.
- Letters of recommendation are waived.
- A regular admission review will be streamlined for students with a cumulative GPA of 3.4 or higher at the University of Michigan-Dearborn after completing at least 85 credits.
- Students must have completed CIS 310, CIS350/3501, CIS 375, and CIS 427 with a grade of B or better.
The following undergraduate programs are approved for the MS-DATA 4+1 program:
- BS in Computer Information Science (CIS)
- BS in Software Engineering (SWE)
Degree Requirements
Regular admission to the program requires a Bachelor degree in a Science, Technology, Engineering, or Mathematics (STEM) field earned from an accredited program with an average of B or better. Each applicant is required to present official, complete transcripts of prior college work. Two letters of recommendation are required for admission. At least one letter must be from someone familiar with the candidate's academic performance. An entering student should have completed one course in probability and statistics, one course in programming, and one course in calculus II. A course in calculus III and a course in linear algebra are recommended but not required.
To satisfy the requirements for the MS degree in Data Science, all students admitted to the program are expected to complete 30 credit hours of approved graduate coursework, with a cumulative grade point average of B or better.
Minimum Grade Requirement in addition to maintaining a minimum cumulative GPA of 3.0 or higher every semester:
- Courses in which grades of C- or below are earned cannot be used to fulfill degree requirements.
- A minimum of a 3.0 cumulative GPA or higher is required at the time of graduation.
Requirements
Core Courses (18 credit hours)
| Code | Title | Credit Hours |
|---|---|---|
| Required | ||
| CIS/IMSE 556 | Database Systems 1 | 3 |
| Choose one course (3 credit hours) from: | ||
| CIS 5570 | Introduction to Big Data | 3 |
| IMSE 586 | Big Data Aanal & Visuliztn | 3 |
| Choose one course (3 credit hours) from: | ||
| ECE 537/CIS 568 | Data Mining | 3 |
| ECE 579 | Intelligent Systems | 3 |
| CIS 581 | Computational Learning 1 | 3 |
| CIS 583 | Deep Learning 1 | 3 |
| STAT 531 | Machine Learning and Computational Statistics | 3 |
| DS 633 | Machine Learning for Business Intelligence | 3 |
| Choose one course (3 credit hours) from: | ||
| IMSE 514 | Multivariate Statistics | 3 |
| STAT 530 | Applied Regression Analysis | 3 |
| STAT 535 | Data Analysis and Modeling | 3 |
| STAT 560 | Time Series Analysis | 3 |
| Choose one course (3 credit hours) from: | ||
| DS 570 | Prescriptive Business Analytics | 3 |
| IMSE 500 | Models of Oper Research | 3 |
| IMSE 516 | Project Management and Control | 3 |
| IMSE 561 | Tot Qual Mgmt and Six Sigma | 3 |
| Choose one course (3 credit hours) from: | ||
| CIS 545 | Data Security and Privacy 1 | 3 |
| CIS 546 | Security and Privacy in Wireless Networks 1 | 3 |
| ECE 527 | Multimedia Secur & Forensics | 3 |
| HHS 570 | Information Science and Ethics | 3 |
- 1
Simultaneous credit toward eligible undergraduate majors and MS Artificial Intelligence for students admitted to the 4+1 option. Please see the College's website for admission requirements and program details.
Specialization Courses (9 credit hours)
Note that the specializations are offered for guidance only. Students may select three courses from one specialization or three courses from multiple specializations for a broader approach to the degree. For students who are interested in selecting the Business Analytics Specialization, they need to choose 3 courses in that specialization as specified.
One of the following specializations is recommended:
Computational Intelligence Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to solve complex data analytics problems through learning and adapting based on data.
| Code | Title | Credit Hours |
|---|---|---|
| CIS 511 | Introduction to Natural Language Processing 1 | 3 |
| CIS 512 | Introduction to Quantum Computing 1 | 3 |
| CIS 5570 | Introduction to Big Data | 3 |
| CIS 5700 | Advanced Data Mining | 3 |
| CIS 579 | Artificial Intelligence 1 | 3 |
| CIS 581 | Computational Learning 1 | 3 |
| CIS 582 | Trustworthy Artificial Intelligence 1 | 3 |
| CIS 583 | Deep Learning 1 | 3 |
| CIS 585 | Advanced Artificial Intelligence | 3 |
| CIS 685 | Research Advances in Artificial Intelligence | 3 |
| ECE 537/CIS 568 | Data Mining | 3 |
| ECE 552 | Fuzzy Systems | 3 |
| ECE 555 | Stochastic Processes | 3 |
| ECE 579 | Intelligent Systems | 3 |
| ECE 5831 | Pat Rec & Neural Netwks | 3 |
| ECE 588 | Robot Vision | 3 |
| ECE 679 | Adv Intelligent Sys | 3 |
| IMSE 505 | Optimization | 3 |
| IMSE 5205 | Eng Risk-Benefit Analysis | 3 |
| IMSE 559 | System Simulation | 3 |
| IMSE 605 | Advanced Optimization | 3 |
| MATH 520 | Stochastic Processes | 3 |
| MATH 523 | Applied Linear Algebra | 3 |
| MATH 562 | Mathematical Modeling | 3 |
| STAT 530 | Applied Regression Analysis | 3 |
| STAT 531 | Machine Learning and Computational Statistics | 3 |
| STAT 560 | Time Series Analysis | 3 |
- 1
Simultaneous credit toward eligible undergraduate majors and MS in Data Science for students admitted to the 4+1 option. Please see the College's website for admission requirements and program details.
Applications Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to develop effective data analytics solutions in selected application domains.
| Code | Title | Credit Hours |
|---|---|---|
| CIS 580 | Data Analytics in Software Engineering | 3 |
| ESCI 585 | Spatial Analysis and GIS | 3 |
| FIN 531 | Fin Fundament & Value Creation | 3 |
| HIT 520 | Clinical & Evidence Based Med | 3 |
| IMSE 516 | Project Management and Control | 3 |
| IMSE 561 | Tot Qual Mgmt and Six Sigma | 3 |
| IMSE 5655 | Supply Chain Management | 3 |
| IMSE 567 | Reliability Analysis | 3 |
| IMSE 580 | Prod & Oper Engineering I | 3 |
| MKT 515 | Marketing Management | 3 |
| OM 521 | Operations Management | 3 |
| OM 571 | Supply Chain Management | 3 |
| STAT 560 | Time Series Analysis | 3 |
Business Analytics Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to apply intelligent strategies and technologies to support the collection, data analysis, presentation and dissemination of business information in enterprises.
| Code | Title | Credit Hours |
|---|---|---|
| Choose two courses from: | ||
| DS 630 | Applied Forecasting with Python | 3 |
| DS 631 | Decision Analysis and Simulation | 3 |
| DS 632 | System Simulation | 3 |
| Choose one course from: | ||
| FIN 531 | Fin Fundament & Value Creation | 3 |
| ISM 525 | Fundamentals of Information Systems | 3 |
| MKT 515 | Marketing Management | 3 |
| OM 521 | Operations Management | 3 |
Big Data Informatics Specialization
This specialization is recommended for those students who are interested in building their knowledge and professional skills to apply cutting-edge technologies and tools to tackle Big Data challenges that are essential for data processing and analytics in numerous applications.
| Code | Title | Credit Hours |
|---|---|---|
| CIS 511 | Introduction to Natural Language Processing 1 | 3 |
| CIS 515 | Computer Graphics and Visual Computing 1 | 3 |
| CIS 525 | Web Technology 1 | 3 |
| CIS 534 | Semantic Web | 3 |
| CIS 536 | Text Mining and Information Retrieval 1 | 3 |
| CIS 540 | Foundation of Information Security | 3 |
| CIS 545 | Data Security and Privacy 1 | 3 |
| CIS 546 | Security and Privacy in Wireless Networks 1 | 3 |
| CIS 548 | Security and Privacy in Cloud Computing | 3 |
| CIS 552 | Information Visualization with Parallel Computing 1 | 3 |
| CIS 559 | Principles of Social Network Science | 3 |
| CIS 562 | Web Information Management | 3 |
| CIS 5570 | Introduction to Big Data | 3 |
| CIS 5700 | Advanced Data Mining | 3 |
| CIS 571 | Web Services | 3 |
| CIS 577 | S/W User Interface Dsgn&Analys | 3 |
| CIS 586 | Advanced Data Management | 3 |
| CIS 589 | Edge Computing 1 | 3 |
| CIS 652 | Advanced Information Visualization and Virtualization | 3 |
| CIS 658 | Research Advances in Data Management | 3 |
| ECE 524 | Interactive Media | 3 |
| ECE 525 | Multimedia Data Stor & Retr | 3 |
| ECE 5251 | MM Design Tools I | 3 |
| ECE 5252 | MM Design Tools II | 3 |
| ECE 528 | Cloud Computing | 3 |
| ECE 576 | Information Engineering | 3 |
| ESCI 585 | Spatial Analysis and GIS | 3 |
| IMSE 564 | Applied Data Analytics and Modeling for Enterprise Systems | 3 |
| IMSE 570 | Enterprise Information Systems | 3 |
| IMSE 577 | Human-Computer Interaction | 3 |
| IMSE 586 | Big Data Aanal & Visuliztn | 3 |
| OM 665 | ERP in SCM | 3 |
- 1
Simultaneous credit toward eligible undergraduate majors and MS in Data Science for students admitted to the 4+1 option. Please see the College's website for admission requirements and program details.
Coursework/Capstone/Thesis (3 credit hours)
Students in this program should choose one of three options: (1) coursework, (2) capstone project, or (3) thesis.
Option 1: Coursework. Students choosing this option must take one additional course (3 credit hours) from a specialization area listed above.
Option 2: Capstone Project. Students choosing this option must complete a capstone project under the supervision of a faculty advisor through a capstone course (3 credit hours). Acceptable capstone courses are:
| Code | Title | Credit Hours |
|---|---|---|
| CIS 695 | Master's Project | 3 |
| DS 635 | Business Analytics Experience | 3 |
| ECE 695 | Master's Project | 3 |
| EMGT 590 | Capstone Project | 3 |
Option 3: Thesis. Students choosing this option must complete a thesis under the supervision of a faculty advisor through a thesis course (6 credit hours). Acceptable thesis courses are: CIS 699, IMSE 699, and ECE 699. Students only need to take two (instead of three) specialization courses (6 credit hours) in this option.
Note that no more than a total of 15 credit hours may be taken in the College of Business for this degree (core, specializations, and capstone/thesis).