Overview

The Master of Science in Data Science (M.S.D.S.) provides students with technical expertise in computational modeling, data collection and integration, data storage and retrieval, data processing, modeling and analytics, and visualization. Students graduating from this program will be able to handle large data sets (big data), write software to work with these large data sets, and apply the statistical skills to model and analyze sub-data sets of interest. The job opportunities in this field are rapidly growing. 

Minimum admission requirement for full admission includes completion of a four-year bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or related program with GPA of 2. 75 or higher on 4.0 scale. Applicants with a baccalaureate degree in a major other than computer science or related program may be admitted to the program and must successfully complete the following three additional bridge courses with a grade of B or above in the first two semesters of the program: 

• Data Structure and Algorithms (CS 210)
• Data Engineering (CS 410)
• Applied Probability and Statistics (ST A 345)

Whether an applicant meets the above requirements will be based on the information provided in the admission application and transcripts. International students must meet MU English proficiency standards and all other admission criteria prior to registering for the first semester of courses. 

The M.S.D.S. degree requires 30 credit hours (CR) of graduate work. The 30 CR is composed of the following components: 

• Required core courses (18 CR)
• Domain Emphasis (12 CR)
  ​The Domain Emphasis gives students a good understanding of a particular domain. A student is required to take 9 credits hours in one domain emphasis and 3 credit hours of free elective in any of the three domain areas:
  • Computing: This domain emphasis tackles computing areas including high performance computing, cloud computing, IoT, Artificial Intelligence, Cybersecurity, bioinformatics, etc. 
  • Information Systems: This domain emphasizes the use of information technology and their expected utility of their information systems. 
  • Predictive Analytics: This domain emphasis gives students the opportunity to learn the use of various statistical modelling techniques that are applicable to predictive analytics. 

Thesis Option in Domain Emphasis

Students may choose a thesis option replacing two courses from in the Domain Emphasis. The thesis option (Thesis I and 2) offers students an opportunity for in-depth understanding and investigation into an area of interest. Students must summarize their thesis work in the form of a formal written document and deliver an oral presentation. Thesis work is typically conducted over two semesters. The thesis option can be taken after the completion of 12 credit hours. The 6 CR of the thesis option cannot be combined in a semester. If a student in the thesis option wishes to switch to the non-thesis option, the credit hours for the thesis will not count toward fulfilling the graduation requirement.

Course List

Code	Title	Credit Hours
Required Core Courses
CS 511	Advanced Programming	3
CS 630	Machine Learning	3
CS 660	Big Data Systems	3
CS 670	Visual Analytics	3
STA 535	Statistical Data Mining	3
STA 634	Stat Mtds for Researchers	3
Domain Emphasis - choose one		12
Computing (see below)
Information Systems (see below)
Predictive Analytics (see below)
Total Credit Hours		30

Course List

Code	Title	Credit Hours
Computing Domain
Select 9 hours from the following		9
CS 505	Computing for Bioinformatics
CS 540	Digital Image Processing
CS 600	Advanced Web Technology
CS 601	The Internet of Things
CS 602	Cloud Computing
CS 620	Applied Algorithms
CS 645	Advanced Topics Bioinformatics
CS 681	Thesis
Free elective from any of the domain areas		3
Total Credit Hours		12

Course List

Code	Title	Credit Hours
Information Systems Domain
Select 9 hours from the following:		9
IS 600	Management Information Systems
MIS 624	Data Warehousing
MIS 610	Systems Quality Assurance
MIS 610	Systems Quality Assurance
IS 621	Information Structures I
MIS 622	Emerging Tech in Info Systems
IS 623	Database Management
HIN 535	Applied Healthcare DB
IS 665	Health Care Enterpr Info Syst
MIS 681	Thesis
One free elective from any of the domain areas		3
Total Credit Hours		12

Course List

Code	Title	Credit Hours
Predictive Analytics Domain
Select 9 hours from the following:		9
STA 512	Regression Analysis
STA 513	Experimental Designs
STA 520	Nonparametric Statistics
STA 564	Statistical Computing
STA 570	Applied Survival Analysis
STA 662	Appl Multivariate Stat Methods
STA 663	Time Series Forecasting
STA 664	Bayesian Statistics
STA 665	Advanced Stat Learning
STA 681	Thesis
One free elective from any of the domain areas		3
Total Credit Hours		12