Every student majoring in Data Science and Engineering (DATA) must complete the following curriculum:
MATH/SCIENCE
| Course | Course Name | Credit Hours |
|---|---|---|
| MATH 101 | Calculus I | 4 |
| MATH 102 | Calculus II | 4 |
| MATH 201 | Calculus III | 3 |
| MATH 210 | Introduction to Sets and Structures | 3 |
| MATH 208 | Differential Equations & Linear Algebra | 3 |
| Total | 17 | |
GENERAL STUDIES
| Course | Course Name | Credit Hours |
| ENGL 101 | Introduction to Academic Discourse | 3 |
| IAS 111 | Belief and its Consequences | 2 |
| PE 101 | Health & Physical Education I | 1 |
| ENGL 102 | Introduction to Report Writing | 3 |
| IAS 121 | Language Foundation | 2 |
| ENGL 214 | Academic & Prof. Communication | 3 |
| IAS 212 | Ethics and Governance | 2 |
| GS XXX | GS Elective | 3 |
| IAS XXX | Islamic/Arabic Elective | 2 |
| CGS 392 | Career Essentials | 1 |
| Total | 22 | |
DIGITAL AND BUSINESS FOUNDATION
| Course | Course Name | Credit Hours |
|---|---|---|
| BUS 200 | Business & Entrepreneurship | 3 |
| ICS 104 | Intro. to Programm. in Python & C | 3 |
| DATA 211 (Current Code ISE 291) | Introduction to Data Science | 3 |
| COE 292 | Introduction to Artificial Intelligence | 3 |
| Total | 12 | |
MAJOR AREA CORE REQUIREMENTS
| Course | Course Name | Credit Hours |
|---|---|---|
| DATA 201 | Probability for Data Science | 3 |
| DATA 301 | Data, Inference, and Decisions | 3 |
| DATA 311 | Data Engineering | 3 |
| DATA 321 | Matrix Theory for Data Science | 3 |
| DATA 322 | Mathematical Modeling for Data Science | 3 |
| DATA 341 | Statistical Methods for Data Science | 3 |
| DATA 351 | Human Contexts and Ethics of Data | 3 |
| DATA 361 | Fundamentals of Database Systems | 3 |
| DATA 399 | Summer Training | 1 |
| DATA 411 | Senior Design Project I | 0 |
| DATA 412 | Senior Design Project II | 3 |
| DATA 421 | Optimization for Data Science | 3 |
| DATA 471 | Big Data Analytics | 3 |
| ICS 108 | Object-Oriented Programming | 4 |
| ICS 202 | Data Structures and Algorithms | 4 |
| ICS 485 | Machine Learning | 3 |
| STAT 201 | Introduction to Probability and Statistics | 3 |
| STAT 460 | Time Series (Analysis) | 3 |
| SWE 206 | Introduction to Software Engineering | 3 |
| SWE 363 | Web Engineering and Development | 3 |
| Total | 57 | |
DATA SCEINCE ELECTIVES
| Course | Pool of Courses | Credit Hours |
|---|---|---|
| Data Science Electives I, II and III | See Table 1 | 9 |
| Total | 9 | |
Table 1: List of Data Science Elective Courses
| Course | Title |
|---|---|
| ICS 344 (3-0-3) | Information Security |
| ICS 353 (3-0-3) | Design and Analysis of Algorithms |
| ICS 355 (3-0-3) | Theory of Computing |
| ICS 381 (3-0-3) | Principles of Artificial Intelligence |
| ICS 410 (3-0-3) | Programming Languages |
| ICS 424 (3-0-3) | Advanced Database Systems |
| ICS 437 (3-0-3) | Distributed Systems |
| ICS 440 (3-0-3) | Cryptography and Blockchain Applications |
| ICS 441 (3-0-3) | Digital Forensics Techniques |
| ICS 471 (3-0-3) | Deep Learning |
| ICS 472 (3-0-3) | Natural Language Processing |
| ICS 473 (3-0-3) | Bioinformatics Mining and Algorithms |
| ICS 483 (3-0-3) | Computer Vision |
| ICS 488 (3-0-3) | Knowledge-Based System and Soft Computing |
| CEO 421 (3-0-3) | Fault-Tolerant Computing |
| CEO 423 (3-0-3) | Distributed Systems |
| CEO 427 (3-0-3) | Distributed Computing |
| CEO 466 (3-0-3) | Quantum Architecture and Algorithms |
TECHNICAL ELECTIVE
| Course | Course Name | Credit Hours |
|---|---|---|
| XE xxx (Technical Elective I) | See Description Below | 3 |
| XE xxx (Technical Elective II) | See Description Below | 3 |
| XE xxx (Technical Elective III) | See Description Below | 3 |
| XE xxx (Technical Elective IV) | See Description Below | 3 |
| Total | 12 | |
Provided that the prerequisites are fulfilled, and cross-listed courses are observed, students can take any course that satisfies the following requirements: