Artificial Intelligence in Business Associate in Science Degree
Division: Business and Computer Information Systems
PROGRAM CODE: 2S46095
The Artificial Intelligence in Business Associate in Science Degree is designed to prepare students for employment in AI-driven business roles. The focus of the program is skill-building in AI automation, data analysis, machine learning applications, and workflow optimization to improve decision-making and business operations. Emphasis on problem-solving, leadership, and critical thinking is stressed to prepare students for careers in business intelligence, marketing, finance, and operations, where AI-driven technologies are increasingly integrated. This degree requires 21-23 units.
| Code | Title | Units |
|---|---|---|
| Required Courses (15 units): | ||
| BUS 108 F | Living in an Online World | 3 |
| BUS 256 F | Artificial Intelligence and Prompt Engineering for Business | 3 |
| BUS 257 F | AI Applications for Business | 3 |
| CIS 201 F | Introduction to Python Programming | 3 |
| CIS 258 F | Applied AI: Machine Learning, Deep Learning and NLP | 3 |
| Restricted Electives (6-8 units): | 6-8 | |
| Introduction to Business and Data Analytics | 3 | |
| Leadership and Business Ethics | 3 | |
or CYBR 206 F | Ethical Hacking | |
or PHIL 160 F | Introduction to Ethics | |
| Database I | 3 | |
or CIS 235 F | Introduction to Cloud Computing | |
| Intro Augmented Virt Reality | 3 | |
| Artificial Intelligence in Game Programming | 3 | |
| Artificial Intelligence in Cybersecurity | 3 | |
| Artificial Intelligence in Marketing | 3 | |
| Elementary Statistics for Behavioral Science | 4-5 | |
or PSY 161HF | Honors Elementary Statistics for Behavioral Science | |
or SOSC 120 F | Introduction to Probability and Statistics | |
or STAT C1000 | Introduction to Statistics | |
or STAT C1000H | Introduction to Statistics - Honors | |
or STAT C1000E | Introduction to Statistics | |
| Total Units | 21-23 | |
Program Level Student Learning Outcomes
Outcome 1: Analyze the impact of artificial intelligence on business operations, decision-making, and customer engagement, demonstrating an understanding of AI-driven automation, predictive analytics, and business intelligence.
Outcome 2: Apply AI-powered tools and technologies, including machine learning, natural language processing (NLP), and generative AI, to optimize marketing, sales, finance, and operational strategies in business environments.
Outcome 3: Evaluate ethical, legal, and strategic considerations in AI adoption, including issues related to data privacy, algorithmic bias, and responsible AI implementation in business practices.
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