Data Science with Applied Artificial Intelligence (M.A.S.)
Build intelligent applications from real-world data through advanced statistics, machine learning, and applied AI.
Turn Data into Intelligent Solutions
Advance from data preparation and statistical modeling to machine learning, natural language processing, and practical AI applications that solve professional challenges.
Program Overview
杏吧原创 Tech鈥檚 M.A.S. in Data Science with Applied Artificial Intelligence combines rigorous data science foundations with advanced AI coursework. Develop expertise in statistical modeling, data preparation, time series, machine learning, and communication, then apply that foundation to modern AI methods.
Explore the data-to-deployment lifecycle: prepare reliable datasets, select and evaluate models, and develop applications using natural language, computer vision, and deep learning. Build the technical and analytical skills to turn complex information into useful, evidence-based solutions for business, science, technology, and research.
Career Opportunities
Prepare for opportunities in applied data science, machine learning, analytics engineering, and AI solution development. Build the expertise to translate messy real-world data into models and applications that support better decisions and practical outcomes.
- Applied AI Data Scientist
- Machine Learning Engineer
- AI Solutions Architect
The 33-credit program includes 18 credits of data-science core courses, nine credits of applied AI coursework, a three-credit applied AI elective, and a three-credit general elective. The curriculum combines statistics, data engineering, advanced machine learning, and communication.
Applicants must hold an accredited bachelor鈥檚 degree or international equivalent and demonstrate preparation appropriate to the program. The graduate catalog lists a minimum cumulative undergraduate GPA of 3.0/4.0 for degree-seeking admission; meeting minimum requirements does not guarantee admission. Required materials include official post-secondary transcripts, a professional statement, r茅sum茅/CV, and any program-required test scores or recommendations. Additional foundation courses may be required. Applicants should demonstrate a strong foundation in mathematics, statistics, computing, or a related quantitative discipline. The program evaluates academic preparation and any required prerequisite coursework.
See the for full requirements, deadlines, and international applicant information.
Tuition and Fees
Learn more about the university鈥檚 current graduate tuition and fees on the Student Accounting website. Tuition, fees and program costs may vary by academic year, enrollment status and program.
To explore your options for financing graduate study, review tuition and aid information and contact Graduate Admission or Student Accounting for current program-specific costs.