MS in Data Science
About the Program
Data science is an interdisciplinary field that uses techniques and theories from mathematics, statistics, computer science, domain knowledge, and information science. It was considered as the fourth (after empirical, theoretical, and computational) paradigm of science by Turing award winner, Jim Gray. A report from McKinsey Global Institute indicates that data scientist is one of the best jobs in U.S. and various industries demand a staggering number of data scientists in the next 10 years.
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. Four specializations are recommended (not mandatory) for students with different interests in Data Science:
- Computational Intelligence Specialization
- Applications Specialization
- Business Analytics Specialization
- Big Data Informatics Specialization.
This program provides students with opportunities to choose from a variety of courses offered by relevant departments from all four colleges at UM-Dearborn to fulfill students' specific career objectives in Data Science. These courses have access to a wide variety of computing and other resources across different units at the university.
The department schedules all DS courses during late afternoons or evenings to enable students to earn their master's degree through part-time study. The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.
If you have questions about the curriculum, please contact the program director: Dr. Jie Shen (shen@umich.edu). If you have questions about the application process, deadlines, tuition, scholarships and assistantships, and other questions please contact the Office of Graduate Studies (umd-graduatestudies@umich.edu ).
Program Details
- 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.
MS in Data Science Eligibility 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.
Prerequisite Courses
| Programming (one of these or equivalent required) | Mathematics | Statistics (one of these or equivalent required) |
|---|---|---|
| CIS 2001 | MATH 116 (or equivalent required) |
IMSE 317 |
| CIS 200 | MATH 215 (or equivalent recommended) | STAT 326 |
| MATH 227 (or equivalent recommended) | MATH 425 |
Deficiencies in the prerequisites may be satisfied after entrance into the program. Students with such deficiencies must complete the missing prerequisite course(s) with a grade of "B" or better within the first two semesters after entering the program.
Prerequisite Courses
| Programming (one of these or equivalent required) | Mathematics | Statistics (one of these or equivalent required) |
|---|---|---|
| CIS 2001 | MATH 116 (or equivalent required) |
IMSE 317 |
| CIS 200 | MATH 215 (or equivalent recommended) | STAT 326 |
| MATH 227 (or equivalent recommended) | MATH 425 |
Programming
CIS 200 Computer Science II
- Prerequisites: MATH 115 and (CIS 150 or IMSE 150 or CCM 150)
- Description: This course presents techniques for the design, writing, testing, and debugging of medium-sized programs, and an introduction to data structures (stacks, queues, linked tests) using an object-oriented programming language. Topics covered include pointers, templates, and inheritance. The principles of UML modeling are continued. This course will consist of three lecture hours and one two-hour laboratory. (4 credits).
CIS 2001 Computer Science II for Data Scientists
- Prerequisites: CIS 1501 and MATH 115
- Description: This course presents techniques for the design, writing, testing, and debugging of medium-sized programs, and an introduction to data structures (stacks, queues, linked tests) using an object-oriented programming language for data science applications. Topics covered include pointers, templates, and inheritance. The principles of UML modeling are continued. This course will consist of three lecture hours and one two-hour laboratory. The labs will cover various data science applications. (4 credits).
Mathematics
MATH 116 Calculus II
- Prerequisites: MATH 115
- Description: Transcendental functions, techniques of integration, improper integral, infinite sequences and series, Taylor's theorem, topics in analytic geometry, polar coordinates, and parametric equations. This course includes computer labs. (4 credits).
MATH 215 Calculus III (Recommended)
- Prerequisites: MATH 116
- Description: Vectors in the plane and space, vector-valued functions and curves, functions of several variables including limits, continuity, partial differentiation and the chain rule, multiple integrals and coordinate transformations, integration in vector fields, and Green's and Stokes' theorems. This course includes computer labs. (4 credits).
MATH 227 Introduction to Linear Algebra (Recommended)
- Prerequisites: MATH 116
- Description: An introduction to the theory and methods of linear algebra with matrices. Topics include: systems of linear equations, algebra of matrices, matrix factorizations, vector spaces, linear transformations, eigenvalues and eigenvectors, science and engineering applications, and computational methods. (3 credits).
Statistics
IMSE 317 Engineering Probability and Statistics
- Prerequisites: MATH 116 or MATH 114
- Description: Set theory, combinatorial analysis, probability and axioms, random variables, continuous and discrete distribution functions, expectations, Chebychev's inequality, weak law of large numbers, central limit theorem, sampling statistics and distributions, point and interval estimation and linear regression. (3 credits).
STAT 326 Applied Statistics II
- Prerequisites: STAT 325
- Description: A continuation of STAT 325. This course treats both the principles and applications of statistics. Elementary theory of estimation and hypothesis testing, the use of the normal, chi- square, F and t distributions in statistics problems will be covered. Other topics selected from regression and correlation, the design of experiments, analysis of variance, analysis of categorized data, nonparametric inference, and sample surveys. (3 credits).
MATH 425 Mathematical Statistics
- Prerequisites: MATH 325
- Description: Interval estimation and pivotal quantities, maximum likelihood estimation, hypothesis tests, linear models and analysis of variance, bivariate normal distribution, regression and correlation analysis, and nonparametric methods. (3 credits).
Curriculum
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.
Please contact the Computer and Information Science Department about the policy on the minimum grade for a course to satisfy graduation requirements.
The 30 semester hours of required coursework are distributed as follows:
Core Courses (18 credit hours)
-
Required (3 credit hours)
- CIS 556/IMSE 556 Database Systems
-
Choose one course (3 credit hours) from:
- CIS 5570 Introduction to Big Data
- IMSE 586 Big Data Analytics and Visualization
-
Choose one course (3 credit hours) from:
- ECE 537/CIS 568 Data Mining
- ECE 579 Intelligent Systems
- CIS 581 Computational Learning
- CIS 583 Deep Learning
- STAT 531 Machine Learning and Computational Statistics
- DS 633 Data Mining for Business Applications
-
Choose one course (3 credit hours) from:
- IMSE 514 Multivariate Statistics
- STAT 530 Applied Regression Analysis
- STAT 535 Data Analysis and Modeling
- STAT 560 Time Series Analysis
-
Choose one course (3 credit hours) from:
- DS 570 Management Science
- IMSE 500 Models of Operational Research
-
Choose one course (3 credit hours) from:
- CIS 545 Data Security and Privacy
- CIS 546 Security and Privacy in Wireless Networks
- ECE 527 Multimedia Security & Forensics
- HHS 570 Information Science and Ethics
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.
- CIS 511 Natural Language Processing
- CIS 555 Decision Support and Expert Systems
- CIS 5700 Advanced Data Mining
- CIS 579 Artificial Intelligence
- CIS 581 Computational Learning
- CIS 583 Deep Learning
- CIS 585 Advanced AI
- CIS 685 Research Advances in Artificial Intelligence
- ECE 537/CIS 568 Data Mining
- ECE 552 Fuzzy Systems
- CIS 5570 Introduction to Big Data
- ECE 579 Intelligent Systems
- ECE 5831 Pattern Recognition & Neural Networks
- ECE 588 Robotic Vision
- ECE 679 Advanced Intelligent Systems
- IMSE 505 Optimization
- IMSE 5205 Engineering Risk-Benefit Analysis
- IMSE 559 System Simulation
- IMSE 605 Advanced Optimization
- MATH 520 or ECE 555 Stochastic Processes
- MATH 562 Mathematical Modeling
- MATH 573 Matrix Computations
- STAT 530 Applied Regression Analysis
- STAT 531 Machine Learning and Computational Statistics
- STAT 545 Reliability & Survival Analysis
- STAT 560 Times Series Analysis
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.
- CIS 580 Data Analytics in Software Engineering
- ESCI 585 Spatial Analysis & GIS
- FIN 531 Finance Fundamentals & Value Creation
- HIT 520 Clinical & Evidence-Based Medicine
- IMSE 516 Project Management & Control
- IMSE 561 Total Quality Management & Six Sigma
- IMSE 5655 Supply Chain Management
- IMSE 567 Reliability Analysis
- IMSE 580 Production and Operations Engineering
- MKT 515 Marketing Management
- OM 521 Operations Management
- OM 571 Supply Chain Management
- STAT 545 Reliability & Survival Analysis
- STAT 560 Times Series Analysis
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.
-
Choose two courses from:
- DS 630 Applied Forecasting
- DS 631 Decision Analysis
- DS 632 System Simulation
- Choose one course from:
- FIN 531 Financial Fundamentals & Value Creation
- MIS 525 Computer & Information Systems
- MKT 515 Marketing Management
- OM 521 Operations Management
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.
- CIS 511 Natural Language Processing
- CIS 515 Computer Graphics
- CIS 525 Web Technology
- CIS 534 Semantic Web
- CIS 536 Text Mining and Information Retrieval
- CIS 540 Foundation of Information Security
- CIS 545 - Data Security and Privacy
- CIS 546 Security and Privacy in Wireless Networks
- CIS 548 Security & Privacy in Cloud Computing
- CIS 552 Information Visualization & Multimedia Gaming
- CIS 554 Information Systems Analysis & Design
- CIS 559 Principles of Social Network Science
- CIS 560 Electronic Commerce
- CIS 562 Web Information Management
- CIS 5570 Introduction to Big Data
- CIS 5700 Advanced Data Mining
- CIS 571 Web Services
- CIS 577/IMSE 577 User Interface Design & Analysis
- CIS 586 Advanced Data Management
- CIS 589 Edge Computing
- CIS 652 Advanced Information Visualization and Virtualization
- CIS 658 Research Advances in Data Management
- ECE 524 Interactive Media
- ECE 525 Multimedia Data Storage & Retrieval
- ECE 5251 Multimedia Design Tools I
- ECE 5252 Multimedia Design Tools II
- ECE 528 Cloud Computing
- ECE 576 Information Engineering
- ESCI 585 Spatial Analysis & GIS
- IMSE 564 Applied Data Analytics and Modeling for Enterprise Systems
- IMSE 570 Enterprise Information Systems
- IMSE 586 Big Data Analytics & Visualization
- OM 665 Information in Supply Chain Management
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:
- CIS 695 Master's Project
- DS 635 Analytics Experience Capstone
- ECE 695 Master's Project
- EMGT 590 Capstone Project
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). Students only need to take two (instead of three) specialization courses (6 credit hours) in this option. Acceptable thesis courses are:
- CIS 699
- IMSE 699
- ECE 699
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).