Data Science and Artificial Intelligence (Second Major Only)
The Data Science and Artificial Intelligence second major is open to all students as a second major; this means that the student will have some other discipline as their primary major. Students whose primary major is in Computer Science or Mathematics will find the Data Science and Artificial Intelligence program the easiest since there is considerable overlap between those programs and the Data Science and Artificial Intelligence requirements Students from other disciplines are also encouraged to participate, but will have to take more courses. All students are encouraged to take the individual courses in the program, regardless of whether they wish to fulfill the second major requirements.
Note that students who complete a second major in Data Science and Artificial Intelligence cannot also earn either a minor in Data Science or a minor in Artificial Intelligence.
These classes can be used to satisfy any degree requirements for any major, per degree separation requirements for those majors. At most eight (8) credits can be used to satisfy the requirements for any minor, except for the Computer Science Minor and Software Engineering Minor, which can use twelve credits.
Required Classes (44 credits)
Students must take the following classes:
| Code | Title | Hours |
|---|---|---|
| CSSE 120 | Introduction to Software Development | 4 |
| CSSE 220 | Object-Oriented Software Development | 4 |
| CSSE 230 | Data Structures and Algorithm Analysis | 4 |
| CSSE 313 | Artificial Intelligence | 4 |
| CSSE 333 | Intro to Database Systems | 4 |
| CSSE 416 | Deep Learning | 4 |
| or MA 416 | Deep Learning | |
| MA 223 | Engineering Statistics | 4 |
| or MA 382 | Introduction to Statistics with Probability | |
| MA 371 | Linear Algebra I | 4 |
| or MA 373 | Applied Linear Algebra for Engineers | |
| MA 381 | Introduction to Probability with Applications to Statistics | 4 |
| MA 384 | Data Mining | 4 |
| PHIL H202 | Business & Engineering Ethics | 4 |
Core Electives (12 credits)
Students must also take 12 credits of restricted electives from the following list:
| Code | Title | Hours |
|---|---|---|
| CSSE 314 | Bio-Inspired Artificial Intelligence | 4 |
| CSSE 315 | Natural Language Processing | 4 |
| CSSE 415 | Machine Learning (or MA 415) | 4 |
| or CSSE 286 | Introduction to Machine Learning | |
| CSSE 433 | Advanced Database Systems | 4 |
| CSSE 453 | Topics in Artificial Intelligence | 4 |
| CSSE 461 | Computer Vision | 4 |
| CSSE 463 | Image Recognition | 4 |
| CSSE 490 | Special Topics in Computer Science (Topic: Professional Software Engineering Practices ) | 4 |
| CSSE 490 | Special Topics in Computer Science (Topic: Software Engineering for Machine Learning ) | 4 |
| CSSE 490 | Special Topics in Computer Science (Topic: Topics in Artificial Intelligence) | 4 |
| CSSE 490 | Special Topics in Computer Science (Topic: Deep Reinforcement Learning ) | 4 |
| CSSE 490 | Special Topics in Computer Science (Topic: Impact of AI ) | 4 |
| CSSE 490 | Special Topics in Computer Science (Topic: Generative AI) | 4 |
| MA 483 | Bayesian Data Analysis | 4 |
Additional Electives (16 hours)
Students must also take 16 additional credits of electives, which can include any core elective not already used to satisfy that requirement or any class from the following list:
| Code | Title | Hours |
|---|---|---|
| BMTH 312 | Bioinformatics | 4 |
| CHE 525 | Process Analytics | 4 |
| ECE 597 | Special Topics in Electrical Engineering (Topic: High-Performance Computing and AI) | 4 |
| ECON S451 | Econometrics | 4 |
| MA 332 | Introduction to Computational Science | 4 |
| MA 335 | Introduction to Parallel Computing | 4 |
| or CSSE 335 | Introduction to Parallel Computing | |
| MA 342 | Computational Modeling | 4 |
| MA 386 | Statistical Programming | 4 |
| MA 482 | Biostatistics | 4 |
| MA 485 | Applied Linear Regression | 4 |
| PH 327 | Thermodynamics & Statistical Mechanics | 4 |
| PHIL H401 | Philosophy of Mind | 4 |
| PSYC S210 | Cognitive Psychology | 4 |
| PSYC S410 | Computational Psychology | 4 |
Data Science and Artificial Intelligence Program Educational Objectives
Graduates from the data science and artificial intelligence program will be prepared for many types of careers in the world of data and be prepared for graduate study in data science and in closely related disciplines. In the early phases of their careers, we expect Rose-Hulman data science graduates to be:
- Data Scientists in a variety of organizations, including ones doing traditional software development, technological innovation, and cross-disciplinary work
- Business and technological leaders within existing organizations
- Entrepreneurial leaders
- Recognized by their peers and superiors for their communication, teamwork, and leadership skills
- Actively involved in social and professional service locally, nationally, and globally
- Graduate students and researchers
- Leaders in government and law as government employees, policy makers, governmental advisors, and legal professionals
Data Science and Artificial Intelligence Program Student Outcomes
- Analyze and explain fundamental principles, mathematical foundations, core algorithms, and model architectures of modern Artificial Intelligence and Machine Learning.
- Develop, train, and evaluate a variety of prototype predictive and generative AI-based solutions to real-world problems.
- Analyze and interpret data to draw conclusions.
- Understand and apply principles of responsible AI, including identifying and mitigating biases, analyzing societal impacts, and addressing ethical challenges in AI system design and deployment.
- Effectively communicate the results, limitations, and implications of AI models and other analyses of large data sets to both technical and non-technical audiences.
- Work effectively in teams on complex AI development projects.