Data Science, Bachelor's to Artificial Intelligence, M.S. Accelerated Program
Saint Louis University's data science B.S. to artificial intelligence M.S. accelerated program听allows a student to complete both the Bachelor of Science in Data Science and the Master of Science in Artificial Intelligence at SLU in a shorter time period than if both degrees were pursued independently.
For additional information, see the catalog entries for the following SLU programs:
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.
Non-Course Requirements
All Science and Engineering B.A. and B.S. students must complete an exit interview/survey near the end of their bachelor's program.听
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.听
Students who drop below听that GPA while in the accelerated program will be placed on a one-semester probationary period before being听dismissed from the accelerated program.听
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听unless otherwise noted. 听
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.
| Year One | ||
|---|---|---|
| Fall | Credits | |
| 颁厂颁滨听1070 | Introduction to Computer Science: Taming Big Data | 3 |
| 惭础罢贬听1660 | Discrete Mathematics | 3 |
| 惭础罢贬听1510 | Calculus I | 4 |
| 颁翱搁贰听1000 | Ignite First Year Seminar | 2 |
| 颁翱搁贰听1500 | Cura Personalis 1: Self in Community | 1 |
| 颁翱搁贰听1900 | Eloquentia Perfecta 1: Written and Visual Communication | 3 |
| 听 | Credits | 16 |
| Spring | ||
| CSCI听1300 | Introduction to Object-Oriented Programming | 4 |
| MATH听1520 | Calculus II | 4 |
| DATA听1800 | Data Science Practicum I | 1 |
| CORE听1600 | Ultimate Questions: Theology | 3 |
| General Electives | 3 | |
| 听 | Credits | 15 |
| Year Two | ||
| Fall | ||
| CSCI听2100 | Data Structures | 4 |
| MATH听2530 | Calculus III | 4 |
| CORE听1200 | Eloquentia Perfecta 2: Oral and Visual Communication | 3 |
| CORE听1700 | Ultimate Questions: Philosophy | 3 |
| 听 | Credits | 14 |
| Spring | ||
| STAT听3850 | Foundation of Statistics | 3 |
| DATA听2800 | Data Science Practicum II | 1 |
| CSCI听2300 | Object-Oriented Software Design | 3 |
| MATH听3110 | Linear Algebra for Engineers | 3 |
| CORE听2500 | Cura Personalis 2: Self in Contemplation | 0 |
| CORE听3800 | Ways of Thinking: Natural and Applied Sciences | 3 |
| General Electives | 3 | |
| 听 | Credits | 16 |
| Year Three | ||
| Fall | ||
| CSCI听3710 | Databases | 3 |
| STAT听4880 | Bayesian Statistics and Statistical Computing | 3 |
| CORE听2800 | Eloquentia Perfecta 3: Creative Expression | 3 |
| CORE听3400 | Ways of Thinking: Aesthetics, History, and Culture | 3 |
| General Electives | 3 | |
| 听 | Credits | 15 |
| Spring | ||
| STAT听5087 | Applied Regression (Critical course: 听Double-counted undergrad/grad) | 3 |
| CSCI/ STAT Elective | 3 | |
| CORE听3600 | Ways of Thinking: Social and Behavioral Sciences | 3 |
| General Electives | 6 | |
| 听 | Credits | 15 |
| Year Four | ||
| Fall | ||
| CSCI听4961 | Capstone Project I | 2 |
| CSCI听5740 | Introduction to Artificial Intelligence (Critical course: 听Only counts toward graduate degree) | 3 |
| CSCI听5750 | Introduction to Machine Learning | 3 |
| General Electives | 6 | |
| 听 | Credits | 14 |
| Spring | ||
| DATA听4962 | Capstone Project II | 2 |
| CSCI 5850 | High-Performance Computing (Double-counted undergrad/grad) | 3 |
| STAT 5xxx Elective (Double-counted undergrad/grad) | 3 | |
| General Electives | 9 | |
| 听 | Credits | 17 |
| Year Five | ||
| Fall | ||
| CSCI听5030 | Principles of Software Development | 3 |
| CSCI听5050 | Computing and Society (Critical course: 听See program notes) | 3 |
| Artificial Intelligence Applications Course | 3 | |
| 听 | Credits | 9 |
| Spring | ||
| CSCI听5961 | Artificial Intelligence Capstone Project | 3 |
| Artificial Intelligence Elective | 3 | |
| 听 | Credits | 6 |
| 听 | Total Credits | 137 |
Program Notes
CSCI听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 CSCI听5990 Thesis Research (0-6 cr) as part of the elective requirements.
Internship with Industry
Students may apply at most three credits of CSCI听5910 Internship with Industry (1-3 cr)听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).