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MS in Data Science

The Data Science program, offered jointly by the department of Computer & Information Science in the College of Engineering and the department of Mathematics in the College of Arts & Sciences, provides advanced education to prepare students for professional positions in data analysis, informatics, data-driven decision-making, and related fields.

You'll gain a strong foundation in information theory, mathematics and computer science with current methodologies and tools to enable data-driven discovery, problem solving, and decision-making.

Prospective students: learn more about the MS in Data Science

Program goals

  1. Meet the growing regional and national demand for high-level information systems/science skills;
  2. Provide a path for individuals from diverse fields to rapidly transition to data science career paths;
  3. Enable established information technology and computing professionals to upgrade their technical management and development skills;
  4. Prepare graduates to apply data science techniques for knowledge discovery and dissemination to assist researchers or decision makers in achieving organizational objectives;
  5. Establish stronger ties to alumni to enhance opportunities for continued learning and leadership;
  6. Create innovators, entrepreneurs, business professionals who will lead the development of next generation information systems.

Learning outcomes

At the time of graduation, students will:

  • be able apply contemporary techniques for managing, mining, and analyzing big data across multiple disciplines;
  • be able to use computation and computational thinking to gain new knowledge and to solve real-world problems of high complexity;
  • have the ability to communicate their ideas and findings persuasively in written, oral and visual form and to work in a diverse team environment;
  • apply advanced knowledge of computing and information systems applications to areas such as networking, database, security and privacy, and Web technologies;
  • be better prepared for career advancement in all areas of information science and technology;
  • be committed to continuous learning about emerging and innovative methods, technologies, and new ideas, and be able to bring them to bear to help others; 
  • have an appreciation for the professional, societal and ethical considerations of data collection and use

Graduate program curriculum outline

Major Required (Core) Courses (Total # of courses required = 5)

Course Number

Course Title

Credit Hours

MTH 522

Mathematical Statistics

3

CIS 452/552

Database Design

3

DSC 520/EAS520

Computational Methods

3

DSC 530

Data Visualization Workshop

3

DSC 550

Data Science Practicum

3

 

SubTotal # Core Credits Required

15

Elective Course Choices (Total courses required = 5)

TBD

Elective in application domain

3

TBD

Elective in application domain

3

TBD

Elective in application domain

3

TBD

Elective in application domain

3

TBD

Elective in application domain

3

 

SubTotal # Elective Credits Required

15

Curriculum Summary

Total number of courses required for the degree

10

Total credit hours required for degree

30

MS in Data Science Course Summary

Data Science Core Courses

MTH 522 Mathematical Statistics (3 credits)

CIS 452 - Database Systems (3 credits)  OR CIS 552 - Database Design (3 credits)
      Note:
If you have taken an undergraduate database course before you should take CIS 552.

DSC 520 - Computational Methods (3 credits)
DSC 530 Data Visualization (3 credits)
DSC 550 Data Science Practicum (3 credits)

Data Science Technical Electives 

MTH 463 - Math Modeling (3 credits)
MTH 440/540 - Mathematical and Computational Consulting (3 credits)
MTH 464/564 Simulations (3 credits)
MTH 473/573 Numerical Linear Algebra (3 credits)
MTH 474/574 Numerical Optimization (3 credits)
CIS 430 – Data Mining and Knowledge Discovery (3 credits)
CIS 431 - Human and Computer Interaction (4 credits)
CIS 454 - Computer Graphics (3 credits)
CIS 455 - Bioinformatics (3 credits)
CIS 467 Image Analysis and Processing (3 credits)
CIS 490 Machine Learning (3 credits)
CIS 522 - Algorithms & Complexity (3 credits)

CIS 550 - Advanced Machine Learning (3 credits)
CIS 554 - Advanced Computer Graphics (3 credits)
CIS 555 - Advanced Bioinformatics (3 credits)
CIS 563 MultiAgent Systems (3 credits)
CIS 569 -Visual Analytics (3 credits)
CIS 581 - Design and Verification of Information Systems (3 credits)
CIS 585 - Image Processing and Machine Vision (3 credits)
CIS 602 - Pattern Analysis (3 credits)
      Note: CIS 602 is a special topics course. Only Pattern Analysis is approved as an elective.

CIS 602 - Human-Computer Interaction (3 credits)
      Note: CIS 602 is a special topics course. Only  Human-Computer Interaction is approved as an elective.

DSC 531/ CIS 530 Advanced Data Mining (3 credits)

EAS 502 - Numerical Methods (3 credits)

EGR 500 - Internship (1 to 3 credits)
      Note:
Curricular Practical Training (CPT). The US government's rules are that CPT must be a part of your educational experience. As such, the internship must be relevant to your field of study and contribute to your degree. For example, if you have already taken 30 credits, or other required coursework will put you at 30 credits, EGR 500 cannot be taken.

MIS 432 - Business Data Systems (3 credits)

MIS 433 Advanced Database E-Business Applications Development (3 credits)

MIS 670 - Managing Information (3 credits)

MIS 681 Business Intelligence and Knowledge Management (3 credits)

MKT 671 Marketing Research (3 credits)

POM 500 Statistical Analysis (3 credits)

POM 681 Business Analytics and Data Mining (3 credits)

PSY 502 Statistical Methods in Psychology (3 credits)

Important Notes

  • DSC 530 will not be offered over the next year. As a result, the following policy is in effect: DSC masters students can take either CIS430/530 (data Mining) or CIS 550 (Advanced Machine Learning) to satisfy the DSC 530 (data visualization) core course requirement. Note that students are also required to take a course in Database Design (CIS 452/552). Students who have taken an undergraduate course in databases are allowed to take CIS 530 to satisfy their database requirement. In such cases, the student must take CIS 550 to satisfy the data visualization requirement.
  • As many as two undergraduate electives (6 credits) may serve as graduate electives. This includes the courses CIS430 and CIS 452 that are mentioned above

Frequently Asked Questions

Q: Are we allowed to work in a team of 2 for the capstone project? 
Projects must be completed under the supervision of a DSC faculty member, and students must enroll in DSC 550. Typically, there will be one instructor per student. But there could be multiple students assigned to a faculty member in the form of a group project -- its up to your project mentor to decide this, and it ultimately up to them to determine if you have completed the project.
Q: Are we allowed to do an internship along with the capstone? Are we allowed to substitute an internship for the project in the final semester?
You can do an internship and capstone concurrently. An internship could in principle be "substituted" for the capstone, but this would have to be arranged by your capstone project faculty mentor. In particular, you would still need to register for DSC 550 under the direction of a DSC faculty member. If would be up to you, the faculty mentor, and the internship to define the scope of the project and how it would be carried out. 

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