Data Science, B.S. to Software Engineering, M.S. Accelerated Program

Saint Louis University's data science B.S. to software engineering M.S. accelerated program allows a student to complete both the Bachelor in Science in Data Science and the Master of Science in Artificial Intelligence in a shorter time period than if both degrees were pursued independently.

For additional information see the catalog entries for the following programs:

Data Science, B.S.

Software Engineering, M.S.

Students who want to apply to this accelerated program should have completed all 2000-level coursework required of the data science bachelor's program and have completed at least 75 credits at the time of application.

At the time of application, students must have a cumulative GPA of at least 3.00 and a GPA of at least 3.00 in their computer science coursework. Contact the graduate coordinator for more details.

Continuation Standards

Students must maintain a cumulative GPA of at least 3.00 and a GPA of at least 3.00 in their computer science coursework. 

Students who drop below that GPA while in the accelerated program will be placed on a one-semester probationary period before being dismissed from the accelerated program. 

Only grades of "B" or better in the graduate courses taken while an undergraduate can be applied to the master's degree.

Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.  

Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.

This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester.  Requirements, course availability and sequencing are subject to change.

Plan of Study Grid
Year One
FallCredits
Critical course:  CSCI 10xxIntroduction to Computer Science 3
MATH 1660 Discrete Mathematics 3
University Core and/or General Electives 9
 Credits15
Spring
Critical course:  CSCI 1300 Introduction to Object-Oriented Programming 4
Critical course:  MATH 1510 Calculus I 4
University Core and/or General Electives 6
 Credits14
Year Two
Fall
Critical course:  CSCI 2100 Data Structures 4
Critical course:  MATH 1520 Calculus II 4
University Core and/or General Electives 9
 Credits17
Spring
Critical course:  CSCI 2300 Object-Oriented Software Design 3
Critical course:  CSCI 2400 Computer Architecture 3
University Core and/or General Electives 9
 Credits15
Year Three
Fall
Critical course:  CSCI 3500 Operating Systems 3
Additional Mathematics (2000+) 3
University Core and/or General Electives 9
 Credits15
Spring
Critical course:  Application Course 3
Critical course:  Theory Course 3
PHIL 3410 Computer Ethics 3
University Core and/or General Electives 6
 Credits15
Year Four
Fall
CSCI 4961 Capstone Project I 2
CSCI 5090 Computer Science Colloquium 1
CSCI 51##CSCI Elective 3
University Core and/or General Electives 9
 Credits15
Spring
Critical course:  CSCI 4962 Capstone Project II 2
CSCI 51xxCSCI Elective 3
University Core and/or General Electives 9
 Credits14
Year Five
Fall
CSCI 5030 Principles of Software Development 3
CSCI 5050 Computing and Society 3
CSCI 5090 Computer Science Colloquium 1
Theory ElectiveTheory courses numbered CSCI5100-5199 3
Software Engineering ElectiveSoftware Engineering courses numbered CSCI5300-5399 3
 Credits13
Spring
CSCI 5090 Computer Science Colloquium 1
CSCI Systems ElectiveSystems courses numbered CSCI5500-5599 3
Graduate Elective 3
Graduate Elective 3
Graduate Elective 3
 Credits13
 Total Credits146

Introduction to Computer Science

Introduction to Computer Science: Principles
Introduction to Computer Science: Bioinformatics
Introduction to Computer Science: Cybersecurity
Introduction to Computer Science: Game Design
Introduction to Computer Science: Mobile Computing
Introduction to Computer Science: Multimedia
Introduction to Computer Science: Scientific Programming
Introduction to Computer Science: Taming Big Data
Introduction to Computer Science: World Wide Web
Introduction to Computer Science: Special Topics
With permission, a computing-intensive course from another discipline may be substituted. Examples of such courses include:
Biomedical Engineering Computing
Civil Engineering Computing
Foundation of Statistics

Applied Systems 

Advanced Operating Systems
Computer Security
Computer Networks
Concurrent and Parallel Programming
Distributed Computing

Theory Courses 

Algorithms
Programming Languages

Graduate Electives

The general requirements must include a course from at least two of the following categories:

  • CSCI 5200-5299 (Language/Compilers courses)
  • CSCI 5600-5699 (Large Scale Systems courses)
  • CSCI 5700-5799 (Knowledge Systems)
  • CSCI 5800-5899 or BCB 5200/5250 (Advanced Applications)

Program Notes

CSCI 5050 Computing and Society (3 cr) requirement will be waived for students who took Computer Ethics as an
undergraduate; these hours would become an additional graduate elective.

Thesis Option

A master's thesis is optional. Students completing a thesis should take six credits of Thesis Research Thesis Research (CSCI 5990) as part of the elective requirements.

Internship with Industry

Students may apply at most three credits of Internship with Industry (CSCI 5910) toward the degree requirements.

Closely Related Disciplines

With approval, students may include up to six credits of elective graduate coursework in closely related disciplines (e.g. mathematics and statistics, bioinformatics and computational biology, electrical and computer engineering).