Master of Science in Data Science and Artificial Intelligence

The Master of Science in Data Science and Artificial Intelligence (DS&AI) program prepares students for high-demand industry positions in DS&AI and for doctoral programs that require a solid knowledge base in computer science, statistics, machine learning, and artificial intelligence. The program combines a broad and in-depth core curriculum in theory and applications. Students will receive rigorous and hands-on training through high-quality research activities to gain software system-building experiences in interdisciplinary settings. Through this program students with and without a Computer Science background will advance their careers by acquiring computational, statistical and system-building skills in this increasingly data-driven society.

Admission requirements 

Successful applicants to the program must

  • Hold a baccalaureate degree from a regionally accredited 4-year institution or shall have completed equivalent academic preparation as determined by the appropriate campus authority in a quantitative/computing field, including but not limited to computer science, mathematics, physics, statistics, engineering or relevant fields. Successful applicants are expected to demonstrate knowledge by having satisfactorily completed courses or worked in the information technology industry in the following areas: data structures, programming, analysis of algorithms, database systems, software engineering,  calculus, probability, and/or statistics. However, an applicant who is deficient in these areas may be admitted conditionally on passing a set of undergraduate courses during the first calendar year of study.
  • Have a 3.0 GPA or higher in their earned undergraduate degree or have earned a post-baccalaureate degree.
  • Submit a general GRE score that has been taken within the last 3 years.
  • Submit a TOEFL score (minimum 550/80) or IELTS score (minimum 7.0) obtained within the past two years if their undergraduate degree is from a country where the official language is not English.
  • Furnish 2 Letters of Recommendation.
  • Submit a Statement of Purpose
  • Submit a copy of CV or resume.

Culminating Experience Requirements 

Each student in this program is required to complete a culminating experience project/thesis by enrolling in either CSC 895 Applied Research Project or CSC 898 Master's Thesis. Under the supervision of a tenured/tenure-track faculty member, a student employs concepts and methods learned in this program to solve a problem of significant importance from a practical or research standpoint. Through this culminating experience project, a student will synthesize and apply concepts and methods learned in more than one course, analyze and compare existing work in the area of study, create a software solution, evaluate this software, and present the major findings in the forms of an oral defense and written thesis/report. CSC 895 and CSC 898 are equivalent options. The project or thesis will also add a prominent component to a student's portfolio and lay the foundation for a future career in data science and AI, be it a Ph.D. program or a leadership position in the industry.

Each student will work with a tenured/tenure-track faculty member to decide whether to register for CSC 895 or CSC 898. Before enrolling in CSC 895 or CSC 898, the student and his/her faculty advisor will be required to assemble a committee of multiple faculty members to maximize a student’s learning outcomes through working on his/her culminating experience project.

A culminating experience project in this program will produce the following three major deliverables: (1) a software system that utilizes relevant data to address an important problem; (2) a written report or Master’s thesis; and (3) an oral presentation to argue/explain/demonstrate the software solution and convince the committee of the solution’s efficacy and effectiveness.

Level 1 and Level 2 Writing Proficiency

Level 1 writing proficiency requirement: A candidate is considered to have fulfilled the Level 1 Writing Proficiency requirement if this candidate has earned a score of 4.0 or above in their recent GRE’s analytic writing assessment.  In the case a candidate scored below 4.0, this candidate may be conditionally admitted. In such a case, this student is required to successfully finish SCI 614: Graduate Writing Skills (3 units) within one year after starting their studies in this program. 

Level 2 writing proficiency requirement: The written report or the Master's thesis resulted from a student's culminating experience project will be utilized to determine a student's Level 2 writing proficiency. Each student in this Master's program is required to carry out a culminating experience (CE) project by enrolling in either CSC 895 or CSC 898. To successfully complete one's CE project, each student is in turn required, among several other mandatory requirements, to write a written project report for CSC 895 or a written Master's thesis for CSC 898.  

Master of Science in Data Science and Artificial Intelligence – 30-33 units

Students may count a maximum of three paired courses (9 units) towards these degree requirements. A paired course is a graduate course paired with an undergraduate course covering similar content and are identified in the course description, e.g., "CSC 865/665 is a paired course offering."

Algorithms, AI & Machine Learning (6-9 units)

Select one:

CSC 849Search Engines3
CSC 865Artificial Intelligence3
CSC 869Data Mining3
CSC 872Pattern Analysis and Machine Intelligence3

Select one or two:

CSC 810Analysis of Algorithms II3
CSC 849Search Engines3
CSC 865Artificial Intelligence3
CSC 869Data Mining3
CSC 872Pattern Analysis and Machine Intelligence3
CSC 876Soft Computing and Decision Support Systems3
Deep learning/neural networks (new course)3

Big Data Platforms & Systems (3 units)

Select one:

CSC 848Software Engineering3
CSC 864Multimedia Systems3
CSC 874Topics in Big Data Analysis3
CSC 875Advanced Topics in Database Systems3
Big data platforms and systems (new course)3

Probability, Statistics, and Statistical Learning (6 units)

MATH 440Probability and Statistics I3
MATH 448Introduction to Statistical Learning and Data Mining3

Data Visualization and Visual Data Analytics (3 units)

Data visualization: concepts, tools, techniques, and paradigms (new course)3

Applications and Best Practices (3-6 units)

Select one or two:

CSC 820Natural Language Technologies3
CSC 821Biomedical Imaging and Analysis3
CSC 842Human-Computer Interaction3
CSC 847Cloud and Distributed Computing: Concepts and Applications3
CSC 857Bioinformatics Computing3
CSC 859AI Explainability and Ethics3
CSC 890Graduate Seminar3
CSC 897Research3-6
CSC 899Independent Study1-3
ISYS 850Seminar in Business Intelligence3
MATH 741Probability and Statistics II3
MGMT 850Ethics and Compliance in Business3
MKTG 886Seminar in Marketing Analytics3
Big data: privacy and cybersecurity (new course)3

Supervised Research and Culminating Experience (6 units) 

CSC 895Applied Research Project3
or CSC 898 Master's Thesis
CSC 897Research3
or CSC 899 Independent Study

Optional: Supervised Industrial Research (1-3 units)

CSC 893Supervised Industrial Research1