Artificial Intelligence for Business and Innovation Graduate Certificate Program

Machine learning and artificial intelligence have emerged as disruptive disciplines outperforming in many cognitive problems (natural language understanding, computer vision, speech recognition, etc.), commonly associated with human intelligence. The proliferation of machine learning and AI techniques, such as ChatGPT and Mid Journey, also raises awareness of the need for AI skills in many businesses to quickly build data driven decision making systems, perform predictive analytics, automate processes, generate new content marketing and engage customer with conversational chatbots just to mention a few.

The Master of Data Analytics and the Master of Artificial Intelligence programs are gaining increased attention of professional workers looking to obtain skills currently in high demand in practical AI and applied machine learning without extensive programming experience and their applications to a wide range of businesses, ranging from finance, accounting, marketing to creative work, innovation and entrepreneurship. 

The Artificial Intelligence for Business and Innovation graduate certificate aims to incrementally develop needed skills and competencies in practical AI (generative AI) and applied machine learning, and their applications to business and entrepreneurship. The certificate seeks to overcome the AI skills gap and train professional workers without extensive programming experience, utilizing low-code/no-code tools to facilitate learning and application to a wide range of business problems. The certificate includes three required courses:
   A-I 810 Artificial Intelligence in Practice
   A-I 820 Generative Artificial Intelligence
   A-I 830 Applied Machine Learning

These courses are structured to be accessible for those without extensive programming experience, utilizing low-code/no-code tools and hands-on case studies from finance, accounting, and marketing, innovation and entrepreneurship. 

The certificate can be completed as part of existing stackable credentials required to earn the Master of Data Analytics degree as defined in the Graduate Bulletin listing of the program.

Courses taken in the certificate program may be applied toward a master's degree in Data Analytics, but also could be offered as elective courses in the Master of Artificial Intelligence, Great Valley MBA, and Master of Software Engineering degree, subject to restrictions outlined in GCAC-309 Transfer Credit. Certificate students who wish to have certificate courses applied towards a master's degree must apply and be admitted to that degree program. Admission to the graduate degree program is a separate step and is not guaranteed.

Effective Semester: Spring 2025
Expiration Semester: Spring 2030

Admission Requirements

Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies. International applicants may be required to satisfy an English proficiency requirement; see GCAC-305 Admission Requirements for International Students for more information.

The successful applicant is generally expected to have a minimum combined junior/senior grade-point average of 3.0 (B) on a 4.0 scale.

Certificate Requirements

Requirements listed here are in addition to requirements listed in Graduate Council policy GCAC-212 Postbaccalaureate Credit Certificate Programs.

All courses must be completed with a minimum grade of C or better and an overall GPA of 3.0. 

Courses

Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.

Learning Outcomes

1. KNOW: Demonstrate proficiency in identifying disciplines in the AI landscape and mastering foundational concepts in machine learning, intelligent agents, search and generative AI techniques.
2. APPLY/CREATE: Demonstrate mastery of concepts and methods for modeling, designing, developing, and testing data-centric AI systems in business and entrepreneurship using no code / low code tools.

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