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 | |
| Women in Computing History | 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 | (12) | |
| Select four of the following: | 12 | |
| 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 | (9) | |
| Select nine credit hours | 9 | |
| Total Credit Hours | 127-130 | |
Bachelor of Science in Data Science Curriculum
| Year 1 | |||
|---|---|---|---|
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| DS 100 | 3 | ETHICS AND SOCIETY | 3 |
| DS 151 | 3 | MATH 152 | 5 |
| MATH 151 | 5 | CS 116 | 2 |
| 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 |
| FREE ELECTIVE | 3 | DS TECH DEPTH | 3 |
| HUMANITIES ELECTIVE (300+) | 3 | DS ELECTIVE | 3 |
| 15 | 15 | ||
| Year 4 | |||
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| DS 451 | 3 | DS 472 | 3-6 |
| FREE ELECTIVE | 3 | FREE ELECTIVE | 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 | ||
| 18 | 15-18 | ||
| Total Credit Hours: 127-130 | |||
