Program Overview

Bachelor of Science in Data Science

The Bachelor of Science in Data Science program is designed to provide students with a comprehensive education in data science, including the principles of data analysis, machine learning, and data visualization.

Program Requirements

The program requires a minimum of 120 credit hours, including:

• Data Science Requirements (24-25 credit hours)
  • DS 100: Introduction to the Profession (3 credit hours)
  • DS 151: Introduction to Data Science (3 credit hours)
  • Select one of the two options:
    • DS 251 and DS 351: Mathematical Foundations for Data Science I and II (6 credit hours)
    • MATH 252 and MATH 350: Introduction to Differential Equations and Introduction to Computational Mathematics (7 credit hours)
  • DS 261: Ethics and Privacy in Data Science (3 credit hours)
  • DS 451: Data Science Life Cycle (3 credit hours)
  • or CSP 571: Data Preparation and Analysis
  • MATH 474: Probability and Statistics (3 credit hours)
  • or MATH 476: Statistics
  • MATH 484: Regression (3 credit hours)
  • or CS 484: Introduction to Machine Learning
• Applied Mathematics Requirements (17 credit hours)
  • MATH 151: Calculus I (5 credit hours)
  • MATH 152: Calculus II (5 credit hours)
  • MATH 251: Multivariate and Vector Calculus (4 credit hours)
  • MATH 332: Elementary Linear Algebra (3 credit hours)
• Computer Science Requirements (10-12 credit hours)
  • Select one of the following sequences:
    • CS 115 and CS 116: Object-Oriented Programming I and II (4 credit hours)
    • CS 104 and CS 201: Introduction to Computer Programming for Engineers and Accelerated Introduction to Computer Science (6 credit hours)
  • CS 331: Data Structures and Algorithms (3 credit hours)
  • CS 425: Database Organization (3 credit hours)
• Communication (3 credit hours)
  • Select one of the following:
    • COM 421: Technical Communication (3 credit hours)
    • COM 428: Verbal and Visual Communication (3 credit hours)
    • INTM 301: Communications for the Workplace (3 credit hours)
    • ITM 300: Communication in the Workplace (3 credit hours)
    • SCI 522: Public Engagement for Scientists (3 credit hours)
• Ethics and Society (3 credit hours)
  • Select one of the following:
    • ITMM 485: Legal and Ethical Issues in Information Technology (3 credit hours)
    • PHIL 374: Ethics in Computer Science (3 credit hours)
    • PHIL 375: Computer Ethics (3 credit hours)
    • PHIL 381: Artificial Intelligence, Philosophy and Ethics (3 credit hours)
    • SOC 362: Technology and Social Change (3 credit hours)
• Data Science Technical Depth (9 credit hours)
  • Select three of the following:
    • CS 422: Data Mining (3 credit hours)
    • CS 429: Information Retrieval (3 credit hours)
    • CS 430: Introduction to Algorithms (3 credit hours)
    • CS 451: Introduction to Parallel and Distributed Computing (3 credit hours)
    • CS 481: Artificial Intelligence Language Understanding (3 credit hours)
    • CS 522: Advanced Data Mining (3 credit hours)
    • CS 577: Deep Learning (3 credit hours)
    • CS 584: Machine Learning (3 credit hours)
    • CSP 554: Big Data Technologies (3 credit hours)
    • MATH 435: Linear Optimization (3 credit hours)
    • MATH 446: Introduction to Time Series (3 credit hours)
    • MATH 475: Probability (3 credit hours)
    • MATH 476: Statistics (3 credit hours)
    • MATH 535: Optimization I (3 credit hours)
    • MATH 546: Introduction to Time Series (3 credit hours)
    • MATH 563: Mathematical Statistics (3 credit hours)
    • MATH 564: Regression (3 credit hours)
    • MATH 569: Statistical Learning (3 credit hours)
    • MATH 574: Bayesian Computational Statistics (3 credit hours)
• Data Science Electives (12 credit hours)
  • Select 12 credit hours from the following courses, or any other courses in Data Science Technical Depth:
    • COM 383: Social Networks (3 credit hours)
    • CS 458: Introduction to Information Security (3 credit hours)
    • or ECE 443: Introduction to Computer Cyber Security
    • CS 480: Introduction to Artificial Intelligence (3 credit hours)
    • CS 487: Software Engineering I (3 credit hours)
    • CS 512: Computer Vision (3 credit hours)
    • CS 520: Data Integration, Warehousing, and Provenance (3 credit hours)
    • CS 546: Parallel and Distributed Processing (3 credit hours)
    • CS 553: Cloud Computing (3 credit hours)
    • CS 554: Data-Intensive Computing (3 credit hours)
    • CS 578: Interactive and Transparent Machine Learning (3 credit hours)
    • CS 579: Online Social Network Analysis (3 credit hours)
    • CS 583: Probabilistic Graphical Models (3 credit hours)
    • CS 585: Natural Language Processing (3 credit hours)
    • DS 472: Data Science Practicum (3-6 credit hours)
    • ECE 308: Signals and Systems (3 credit hours)
    • ECE 442: Internet of Things and Cyber Physical Systems (3 credit hours)
    • ECE 447: Artificial Intelligence and Edge Computing (3 credit hours)
    • ECE 449: Object-Oriented Programming and Machine Learning (3 credit hours)
    • ECE 481: Image Processing (3 credit hours)
    • ECE 501: Artificial Intelligence and Edge Computing (3 credit hours)
    • ECE 510: Internet of Things and Cyber Physical Systems (3 credit hours)
    • ECE 511: Analysis of Random Signals (3 credit hours)
    • ECE 520: Information Theory and Applications (3 credit hours)
    • ECE 521: Quantum Electronics (3 credit hours)
    • ECE 563: Artificial Intelligence in Smart Grid (3 credit hours)
    • ECE 565: Computer Vision and Image Processing (3 credit hours)
    • ECE 566: Machine and Deep Learning (3 credit hours)
    • ECE 567: Statistical Signal Processing (3 credit hours)
    • EMGT 363: Creativity, Inventions, and Entrepreneurship for Engineers and Scientists (3 credit hours)
    • ITMS 418: Coding Security (3 credit hours)
    • ITMS 448: Cyber Security Technologies (3 credit hours)
    • ITMS 478: Cyber Security Management (3 credit hours)
    • STAT 225: Introductory Statistics (3 credit hours)
    • MATH 380: Introduction to Mathematical Modeling (3 credit hours)
    • MATH 483: Design and Analysis of Experiments (3 credit hours)
    • MATH 497: Special Problems (1-20 credit hours)
    • MATH 527: Machine Learning in Finance: From Theory to Practice (3 credit hours)
    • MATH 565: Monte Carlo Methods (3 credit hours)
    • SSCI 325: Intermediate Geographic Information Systems (3 credit hours)
    • SSCI 480: Introduction to Survey Methodology (3 credit hours)
• Science Requirement and Electives (10 credit hours)
  • See Illinois Tech Core Curriculum, Section D
• Humanities and Social Science Requirements (21 credit hours)
  • See Illinois Tech Core Curriculum, Sections B and C
• Interprofessional Projects (IPRO) (6 credit hours)
  • See Illinois Tech Core Curriculum, Section E
• Free Electives (2-5 credit hours)
  • Select two to five credit hours

Curriculum

The curriculum for the Bachelor of Science in Data Science program is as follows:

• Year 1:
  • Semester 1:
    • DS 100: Introduction to the Profession (3 credit hours)
    • DS 151: Introduction to Data Science (3 credit hours)
    • MATH 151: Calculus I (5 credit hours)
    • CS 115: Object-Oriented Programming I (2 credit hours)
    • Humanities 200-level course (3 credit hours)
  • Semester 2:
    • MATH 152: Calculus II (5 credit hours)
    • CS 116: Object-Oriented Programming II (2 credit hours)
    • Ethics and Society (3 credit hours)
    • Science Elective (4 credit hours)
    • Social Science Elective (3 credit hours)
• Year 2:
  • Semester 1:
    • MATH 251: Multivariate and Vector Calculus (4 credit hours)
    • MATH 332: Elementary Linear Algebra (3 credit hours)
    • CS 331: Data Structures and Algorithms (3 credit hours)
    • Science Elective (3 credit hours)
    • Social Science Elective (3 credit hours)
  • Semester 2:
    • MATH 474: Probability and Statistics (3 credit hours)
    • DS 261: Ethics and Privacy in Data Science (3 credit hours)
    • CS 425: Database Organization (3 credit hours)
    • Social Science Elective (300+ level) (3 credit hours)
    • Science Elective (3 credit hours)
• Year 3:
  • Semester 1:
    • DS 251: Mathematical Foundations for Data Science I (3 credit hours)
    • CS 484: Introduction to Machine Learning (3 credit hours)
    • DS Elective (3 credit hours)
    • Humanities Elective (300+ level) (3 credit hours)
    • Free Elective (2-3 credit hours)
  • Semester 2:
    • DS 351: Mathematical Foundations for Data Science II (3 credit hours)
    • Communication (3 credit hours)
    • DS Tech Depth (3 credit hours)
    • DS Elective (3 credit hours)
    • Free Elective (3 credit hours)
• Year 4:
  • Semester 1:
    • DS 451: Data Science Life Cycle (3 credit hours)
    • DS Tech Depth (3 credit hours)
    • IPRO (3 credit hours)
    • Social Science Elective (300+ level) (3 credit hours)
    • DS Elective (3 credit hours)
  • Semester 2:
    • DS 472: Data Science Practicum (3 credit hours)
    • DS Tech Depth (3 credit hours)
    • IPRO (3 credit hours)
    • Humanities Elective (300+ level) (3 credit hours)

Notes

• 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.