Bachelor of Science in Data Science
Required Courses
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Requirements | (24-25) | |
| DS 100 | Introduction to the Profession | 3 |
| DS 151 | Introduction to Data Science | 3 |
| Select one of the two options: | 6-7 | |
| Mathematical Foundations for Data Science I and Mathematical Foundations for Data Science II | 6 | |
| Introduction to Differential Equations and Introduction to Computational Mathematics | 7 | |
| DS 261 | Ethics and Privacy in Data Science | 3 |
| DS 451 | Data Science Life Cycle | 3 |
| or CSP 571 | Data Preparation and Analysis | |
| MATH 474 | Probability and Statistics | 3 |
| or MATH 476 | Statistics | |
| MATH 484 | Regression | 3 |
| or CS 484 | Introduction to Machine Learning | |
| Applied Mathematics Requirements | (17) | |
| MATH 151 | Calculus I | 5 |
| MATH 152 | Calculus II | 5 |
| MATH 251 | Multivariate and Vector Calculus | 4 |
| MATH 332 | Elementary Linear Algebra | 3 |
| Computer Science Requirements | (10-12) | |
| Select one of the following sequences: | 4-6 | |
| Object-Oriented Programming I and Object-Oriented Programming II | 4 | |
| Introduction to Computer Programming for Engineers and Accelerated Introduction to Computer Science | 6 | |
| CS 331 | Data Structures and Algorithms | 3 |
| CS 425 | Database Organization | 3 |
| Communication | (3) | |
| Select one of the following: | 3 | |
| Technical Communication | 3 | |
| Verbal and Visual Communication | 3 | |
| Communications for the Workplace | 3 | |
| Communication in the Workplace | 3 | |
| Public Engagement for Scientists | 3 | |
| Ethics and Society | (3) | |
| Select one of the following: | 3 | |
| Legal and Ethical Issues in Information Technology | 3 | |
| Ethics in Computer Science | 3 | |
| Computer Ethics | 3 | |
| Artificial Intelligence, Philosophy and Ethics | 3 | |
| Technology and Social Change | 3 | |
| Data Science Technical Depth | (9) | |
| Select three of the following: | 9 | |
| Data Mining | 3 | |
| Information Retrieval | 3 | |
| Introduction to Algorithms | 3 | |
| Introduction to Parallel and Distributed Computing | 3 | |
| Artificial Intelligence Language Understanding | 3 | |
| Advanced Data Mining | 3 | |
| Deep Learning | 3 | |
| Machine Learning | 3 | |
| Big Data Technologies | 3 | |
| Linear Optimization | 3 | |
| Introduction to Time Series | 3 | |
| Probability | 3 | |
| Statistics | 3 | |
| Optimization I | 3 | |
| Introduction to Time Series | 3 | |
| Mathematical Statistics | 3 | |
| Regression | 3 | |
| Statistical Learning | 3 | |
| Bayesian Computational Statistics | 3 | |
| Data Science Electives | (12) | |
| Select 12 credit hours from the following courses, or any other courses in Data Science Technical Depth: | 12 | |
| Social Networks | 3 | |
| Introduction to Information Security | 3 | |
or ECE 443 | Introduction to Computer Cyber Security | |
| Introduction to Artificial Intelligence | 3 | |
| Software Engineering I | 3 | |
| Computer Vision | 3 | |
| Data Integration, Warehousing, and Provenance | 3 | |
| Parallel and Distributed Processing | 3 | |
| Cloud Computing | 3 | |
| Data-Intensive Computing | 3 | |
| Interactive and Transparent Machine Learning | 3 | |
| Online Social Network Analysis | 3 | |
| Probabilistic Graphical Models | 3 | |
| Natural Language Processing | 3 | |
| Data Science Practicum | 3-6 | |
| Signals and Systems | 3 | |
| Internet of Things and Cyber Physical Systems | 3 | |
| Artificial Intelligence and Edge Computing | 3 | |
| Object-Oriented Programming and Machine Learning | 3 | |
| Image Processing | 3 | |
| Artificial Intelligence and Edge Computing | 3 | |
| Internet of Things and Cyber Physical Systems | 3 | |
| Analysis of Random Signals | 3 | |
| Information Theory and Applications | 3 | |
| Quantum Electronics | 3 | |
| Artificial Intelligence in Smart Grid | 3 | |
| Computer Vision and Image Processing | 3 | |
| Machine and Deep Learning | 3 | |
| Statistical Signal Processing | 3 | |
| Creativity, Inventions, and Entrepreneurship for Engineers and Scientists | 3 | |
| Coding Security | 3 | |
| Cyber Security Technologies | 3 | |
| Cyber Security Management | 3 | |
| Introductory Statistics | 3 | |
| Introduction to Mathematical Modeling | 3 | |
| Design and Analysis of Experiments | 3 | |
| Special Problems | 1-20 | |
| Machine Learning in Finance: From Theory to Practice | 3 | |
| Monte Carlo Methods | 3 | |
| Intermediate Geographic Information Systems | 3 | |
| Introduction to Survey Methodology | 3 | |
| Science Requirement and Electives | (10) | |
| See Illinois Tech Core Curriculum, Section D | 10 | |
| Humanities and Social Science Requirements | (21) | |
| See Illinois Tech Core Curriculum, Sections B and C | 21 | |
| Interprofessional Projects (IPRO) | (6) | |
| See Illinois Tech Core Curriculum, Section E | 6 | |
| Free Electives | (2-5) | |
| Select two to five credit hours 1 | 2-5 | |
Minimum degree credits required: 120
- 1
Students who complete MATH 252 and MATH 350 instead of DS 251 and DS 351 will need to take 4 credits of free electives. Students who complete CS 104 and CS 201 instead of CS 115 and CS 116 will need to take 3 credits of free electives. Students who complete all of MATH 252, MATH 350, CS 104, and CS 201 instead of DS 251, DS 351, CS 115, and CS 116 will need to take 2 credits of free electives.
Bachelor of Science in Data Science Curriculum
| Year 1 | |||
|---|---|---|---|
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| DS 100 | 3 | MATH 152 | 5 |
| DS 151 | 3 | CS 116 | 2 |
| MATH 151 | 5 | Ethics and Society | 3 |
| CS 115 | 2 | Science Elective | 4 |
| Humanities 200-level course | 3 | Social Science Elective | 3 |
| 16 | 17 | ||
| Year 2 | |||
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| MATH 251 | 4 | MATH 474 | 3 |
| MATH 332 | 3 | DS 261 | 3 |
| CS 331 | 3 | CS 425 | 3 |
| Science Elective | 3 | Social Science Elective (300+) | 3 |
| Humanities or Social Science Elective | 3 | Science Elective | 3 |
| 16 | 15 | ||
| Year 3 | |||
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| DS 251 | 3 | DS 351 | 3 |
| CS 484 | 3 | Communication | 3 |
| DS Elective | 3 | DS Tech Depth | 3 |
| Humanities Elective (300+) | 3 | DS Elective | 3 |
| Free Elective | 2-3 | Free Elective | 3 |
| 14-15 | 15 | ||
| Year 4 | |||
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| DS 451 | 3 | DS 472 | 3 |
| DS Tech Depth | 3 | DS Tech Depth | 3 |
| IPRO | 3 | IPRO | 3 |
| Social Science Elective (300+) | 3 | Humanities Elective (300+) | 3 |
| DS Elective | 3 | ||
| 15 | 12 | ||
| Total Credit Hours: 120-121 | |||
