Bachelor of Science in Data Science & Artificial Intelligence
Total Credits: ~120-130 credits
Lower Division Courses
DSAI 1101 — Introduction to Data Science (3)
DSAI 1201 — Statistics for Data Science (3)
DSAI 1301 — Python for Data Analysis (4)
DSAI 1401 — Programming Fundamentals (3)
DSAI 1501 — Discrete Mathematics (3)
DSAI 1601 — Foundations of Machine Learning (3)
DSAI 1701 — Data Structures & Algorithms (3)
Upper Division Courses
DSAI 2101 — Machine Learning (3)
DSAI 2201 — Big Data Systems (3)
DSAI 2301 — Ethics in AI (2)
DSAI 2401 — Data Visualization & Communication (3)
DSAI 2501 — Deep Learning (3)
DSAI 2601 — Natural Language Processing (3)
DSAI 2701 — Capstone Project (4)
Prerequisites & Electives
Foundational courses in programming, math, and statistics
Electives such as Computer Vision, Reinforcement Learning, Data Engineering, AI Ethics
Bachelor of Science in Data Science & Artificial Intelligence
Lower Division Courses
Lower Division Courses
1. DSAI 1101 - Introduction to Data Science
- Credits: 3
- Prerequisites: None
- Description: Overview of data science fundamentals, data types, data lifecycle, and basic analysis techniques.
2. DSAI 1201 - Statistics for Data Science
- Credits: 3
- Prerequisites: None
- Description: Descriptive and inferential statistics, probability, hypothesis testing, and data distributions.
3. DSAI 1301 - Python for Data Analysis
- Credits: 4
- Prerequisites: None
- Description: Programming in Python, data manipulation with pandas, data visualization, and basic scripting.
Upper Division Courses
4. DSAI 2101 - Machine Learning
- Credits: 3
- Prerequisites: DSAI 1201 (Statistics for Data Science) and DSAI 1301 (Python for Data Analysis)
- Description: Supervised and unsupervised learning, models, evaluation metrics, and applications.
5. DSAI 2201 - Big Data Systems
- Credits: 3
- Prerequisites: DSAI 1301 (Python for Data Analysis) or equivalent
- Description: Distributed data storage, processing frameworks (e.g., Hadoop, Spark), and handling large datasets.
6. DSAI 2301 - Ethics in AI
- Credits: 2
- Prerequisites: None
- Description: Ethical considerations, bias, fairness, privacy, and social implications of AI and data science.
7. DSAI 2901 - Capstone Project
- Credits: 4
- Prerequisites: Senior standing, completion of core courses, and project approval
- Description: Practical project applying data science methods to real-world problems, culminating in a presentation and report.
Prerequisites & Electives
Prerequisites Overview:
- Foundational courses in programming, statistics, and mathematics.
- Progression through core courses with satisfactory academic standing.
Elective Courses:
- Deep Learning & Neural Networks
- Natural Language Processing
- Data Visualization & Dashboard Design
- Reinforcement Learning
- Time Series Analysis
- Data Engineering & ETL Pipelines
- Privacy-Preserving Data Mining
- AI for Business & Society
Electives enable specialization in advanced AI techniques, big data engineering, or ethical practices.