Bachelor of Science in Data Science
Required Courses
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Requirements | (24-25) | |
| DS 100 | Introduction to the Profession 1 | 3 |
| DS 151 | Introduction to Data Science | 3 |
| Select one of the two options: | 6-7 | |
| Introduction to Differential Equations and Introduction to Computational Mathematics | 7 | |
| Mathematical Foundations for Data Science I and Mathematical Foundations for Data Science II | 6 | |
| DS 261 | Data Ethics and Responsible AI | 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 | Linear Algebra: Theory and Applications | 3 |
| Computer Science Requirements | (13) | |
| Select one of the following sequences: | 4 | |
| Object-Oriented Programming I and Object-Oriented Programming II | 4 | |
| Accelerated Introduction to Computer Science | 4 | |
| CS 330 | Discrete Structures | 3 |
| or MATH 230 | Introduction to Discrete Math | |
| CS 331 | Data Structures and Algorithms | 3 |
| CS 425 | Database Organization | 3 |
| Data Science Communication | (3) | |
| Select one of the following: | 3 | |
| A.I.-Assisted Workflows | 3 | |
| Technical Communication | 3 | |
| Verbal and Visual Communication | 3 | |
| Communicating Science | 3 | |
| Communications for the Workplace | 3 | |
| Communication in the Workplace | 3 | |
| Ethics and Society | (3) | |
| Select one of the following: | 3 | |
| Legal and Ethical Issues in Information Technology | 3 | |
| Philosophy of Data Science | 3 | |
| Political Philosophy of Artificial Intelligence | 3 | |
| Ethics of Technology and Communication | 3 | |
| Artificial Intelligence, Philosophy and Ethics | 3 | |
| Bioethics | 3 | |
| Technology and Social Change | 3 | |
| AI and Public Policy | 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 | |
| Information and Knowledge Management Systems | 3 | |
| Introduction to Machine Learning | 3 | |
| Advanced Data Mining | 3 | |
| Deep Learning | 3 | |
| Machine Learning | 3 | |
| Big Data Technologies | 3 | |
| Linear Optimization | 3 | |
| Network modeling and statistics | 3 | |
| Introduction to Time Series | 3 | |
| Probability | 3 | |
| Statistics | 3 | |
| Computational Linear Algebra for Science and Engineering | 3 | |
| Regression | 3 | |
| Optimization I | 3 | |
| Introduction to Time Series | 3 | |
| Mathematical Statistics | 3 | |
| Regression | 3 | |
| Statistical Learning | 3 | |
| Bayesian Computational Statistics | 3 | |
| Computational Linear Algebra for Science and Engineering | 3 | |
| Data Science Electives | (9) | |
| Select 9 credit hours from the following courses, or any other courses in Data Science Technical Depth: | 9 | |
| Introduction to Geographic Information Systems | 3 | |
| Computer Organization and Assembly Language Programming | 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 | |
| Advanced Database Organization | 3 | |
| Parallel and Distributed Processing | 3 | |
| Cloud Computing | 3 | |
| Data-Intensive Computing | 3 | |
| Reinforcement Learning | 3 | |
| Online Social Network Analysis | 3 | |
| Probabilistic Graphical Models | 3 | |
| Natural Language Processing | 3 | |
| Data Science Practicum | 3-6 | |
| Data Science Projects | 3 | |
| Signals and Systems | 3 | |
| Introduction to Computer Cyber Security | 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 | |
| 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 | |
| Fundamentals of Web Development | 3 | |
| Human-Computer Interaction and Web Design | 3 | |
| Web Application Foundations | 3 | |
| Full-Stack Web Development | 3 | |
| Foundations of Secure AI Systems | 3 | |
| Coding Security | 3 | |
| Cyber Security Technologies | 3 | |
| Secure AI Systems Engineering and Defense | 3 | |
| Cyber Security Management | 3 | |
| Mathematical Modeling with Data | 3 | |
| Network Optimization | 3 | |
| Combinatorics | 3 | |
| Graph Theory and Applications | 3 | |
| Introduction to Stochastic Processes | 3 | |
| Design and Analysis of Experiments | 3 | |
| Machine Learning in Finance: From Theory to Practice | 3 | |
| Monte Carlo Methods | 3 | |
| Introduction to Geographic Information Systems | 3 | |
| Intermediate Geographic Information Systems | 3 | |
| Methods of Economic Impact Analysis | 3 | |
| Introduction to Survey Methodology | 3 | |
| Introductory Statistics | 3 | |
| Science Requirement and Electives | (7-10) | |
| See Illinois Tech Core Curriculum, Section D 2 | 7-10 | |
| Humanities and Social Science Requirements | (6-21) | |
| See Illinois Tech Core Curriculum, Sections B and C 3 | 6-21 | |
| Interprofessional Projects (IPRO) | (6) | |
| See Illinois Tech Core Curriculum, Section E | 6 | |
| Free Electives | (4-20) | |
| Select four to twenty credit hours 4 | 4-20 | |
Minimum degree credits required: 120
- 1
CS 100 or MATH 100 may be substituted by agreement with the academic advisor.
- 2
If chosen as a Data Science Elective, one 3-credit ECE course can double-count with 3 credits of Science Requirement and Electives of the Core Curriculum, making the additional requirement in this category 7 credits instead of 10.
- 3
Various combinations of selections of courses for the Data Science Communication, Ethics and Society, and Data Science Elective requirements can double-count for up to 15 credits of Humanities and Social Science Requirements of the Core Curriculum, making the additional requirements in this category as low as 6 credits instead of 21.
- 4
Four (4) free elective credits are needed when MATH 252 and MATH 350 are taken instead of DS 251 and DS 351, and no courses are double-counted with the core curriculum. Twenty (20) free elective credits are needed when DS 251 and DS 351 are taken instead of MATH 252 and MATH 350, and fifteen (15) free elective credits are double-counted between the (i) Data Science Communication, Ethics and Society, and Data Science Electives requirements and the (ii) Core Curriculum C: Human Sciences Module and/or D.2: Natural Science or Engineering part of the STEM Module.
Bachelor of Science in Data Science Curriculum
| Year 1 | |||
|---|---|---|---|
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| DS 100 | 3 | CS 116 | 2 |
| DS 151 | 3 | MATH 152 | 5 |
| 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 |
| CS 331 | 3 | CS 330 or MATH 230 | 3 |
| MATH 251 | 4 | CS 425 | 3 |
| MATH 332 | 3 | DS 261 | 3 |
| Science Elective | 3 | MATH 474 | 3 |
| Humanities or Social Science Elective | 3 | Science Elective | 3 |
| 16 | 15 | ||
| Year 3 | |||
| Semester 1 | Credit Hours | Semester 2 | Credit Hours |
| MATH 252 | 4 | CS 484 | 3 |
| DS Elective | 3 | MATH 350 | 3 |
| DS Elective | 3 | DS Communication | 3 |
| Humanities Elective (300+) | 3 | DS Tech Depth | 3 |
| Free Elective | 3 | Free Elective 1 | 3 |
| 16 | 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 | Social Science Elective (300+) | 3 |
| Humanities Elective (300+) | 3 | ||
| 15 | 12 | ||
| Total Credit Hours: 122 | |||
- 1
The 120 credit degree minimum can be achieved by taking fewer free elective credits than listed in this sample curriculum.
