The Master of Science in Applied Data Intelligence, offered by the Department of Applied Data Science , is developed in partnership with industry experts targeting students from diverse academic and professional backgrounds. Students receive the advanced education necessary to apply data intelligence to solve real-world problems by immersing them in the full applied data science process lifecycle.

Today, most organizations treat data and AI as strategic assets embedded across functions. The MSADI prepares graduates for careers across industries, including AI engineer, applied AI/LLM engineer, machine learning engineer, data scientist, data engineer, business intelligence analyst, and information research scientist.

The curriculum emphasizes modern AI (machine learning, deep learning, generative AI and LLM applications, agentic and embodied AI) together with the data engineering and computational foundations required to deploy reliable systems at scale-reflecting Silicon Valley’s leadership and demand for talent.

Students build portfolios of applied data science projects by leveraging the interrelationship between domain knowledge and AI technologies. Students advance their knowledge and skills through:

• Identifying domain specific problems, defining requirements, and establishing measurable organizational objectives. 
• Exploring, preparing, visualizing, and governing data with reproducible practices. 
• Bringing together data engineering and computational foundations with ML, deep learning, generative AI (LLMs), agentic and embodied AI, and distributed systems to deliver reliable, real-world descriptive, diagnostic, predictive, and prescriptive systems. 
• Developing, validating, deploying, and monitoring AI systems to meet requirements and objectives responsibly.

For more information, visit the MS Applied Data Intelligence program website. 

Program Delivery

Hybrid Program (includes Special Session)

Special Session Program Information

Academic Programs offered through Special Session are operated by Professional and Continuing Education. Registration and enrollment in a Special Session course or program are subject to special session fees and course schedules. Note that regular session students seeking to enroll simultaneously in a special session course or the program will trigger a separate and additional set of fees. This may require an additional enrollment appointment from the Registrar and it may have implications for financial aid status or requirements.

University Admission Requirements

Applicants must submit a complete graduate application by applying through the Cal State Apply system and meet all the university admission requirements .

In addition to holding a bachelor’s degree, international applicants (or applicants who earned their degrees in a country where the primary language is not English) must achieve a minimum English-language proficiency test score as indicated on the Graduate Program Test Requirements webpage.

An applicant can be admitted in either classified or conditionally classified standing.

Admission to Graduate Standing - Classified

An applicant can be admitted to classified standing if the applicant has a bachelor’s degree from an accredited institution with a grade point average of 3.0 or better. In addition, this applicant’s preparation for advanced graduate work in mathematics and computer programming must be considered adequate to meet the course prerequisites and other departmental requirements. 

Admission to Graduate Standing - Conditionally Classified

An applicant might be conditionally admitted to the program with a marginal deficiency in the above requirements. The admission letter will explain the required coursework, terms, and conditions for removing deficiencies and attaining classified standing.

Waiver exams assess competency equivalent to DATA 200 , DATA 201 , and DATA 202 ; based on results, students may be required to complete the corresponding course(s).

Advancement to Candidacy

The university requirements for advancement to candidacy  for the master’s degree are outlined in the Graduate Policies and Procedures  section. Students should seek advancement to candidacy as soon as possible for their given program and on the advice of their graduate program coordinator. Graduate students may submit a Petition for Advancement to Graduate Candidacy form to the Graduate Admissions & Program Evaluations (GAPE) office after satisfying the following: achieved classified status; completed a minimum of nine letter-graded units with all grades “C” or higher; fulfilled the Campus Graduate Graduation Writing Assessment Requirement (CGGWAR) requirement; achieved a minimum cumulative and program 3.0 GPA, and, graduate within the seven-year time limit for degree completion. Graduate students must submit their petition no later than one semester prior to graduating. Courses that satisfy the CGGWAR are listed in the course requirements for the program.

MSADI program course requirements and the links to specialization course requirements are listed at sjsu.edu/applied-data-science/.

Program of Study Requirements 

Students must complete all residency, curriculum, unit, GPA, and culminating experience requirements as outlined in the Graduation Requirements  section of the Graduate Policies and Procedures . Students must comply with all other graduate requirements contained in this catalog.

Course Requirements

The program consists of 30 semester units of 200-level courses with a cumulative GPA of 3.0 or better. The program has developed two specializations: Specialization in Analytics Technologies and Specialization in Data Engineering. These specializations will be offered according to industry trends, student demands, and resource availability. For more information, visit the MS in Applied Data Intelligence program website.

Culminating Experience (Plan A or Plan B)

All students must complete one of the following culminating experience options as part of their 30-unit program requirement.

Plan A (Thesis)

Students opting to complete a master’s thesis will take the DATA 299A  and DATA 299B  as a two-course sequence. The student is responsible for securing the commitment of a full-time tenured or tenure-track faculty member of the Applied Data Science Department who agrees to serve as the thesis committee chair. The student must also secure the commitments of two additional university faculty members, one of whom must be a full-time tenured or tenure-track faculty member of the Applied Data Science Department, to serve as the student’s thesis committee. The student must write a thesis proposal and have it approved by the thesis committee and pass the DATA 299A  course before enrolling in DATA 299B . The thesis must meet university requirements as stipulated in this catalog and in the SJSU Master’s Thesis and Doctoral Dissertation Guidelines. It will be written under the guidance of the candidate’s thesis committee chair with the assistance of the thesis committee.

Plan B (Project)

The graduate project is a research or development effort performed by a team of students on a topic chosen by mutual agreement between an advisor and the team. The choice of project topic must also be approved by the instructor of DATA 298A . DATA 298A  is the first part of the master’s project in which students develop a comprehensive plan and preliminary design of a data analytics project. DATA 298B  is the second part of the master’s project course in which each student completes an in-depth written project to achieve the program outcomes and satisfy the program’s culminating experience requirement.