Computer Science

Students in the Data Science, M.S. program complete 30 units of graduate work, including a 6-unit thesis. The core of the program comprises advanced courses in computational data science covering topics in statistical inference, machine learning for analysis of big data, data visualization, and big data design and management. The electives may be chosen to form a concentration in an area of specialization or to provide a broadly based program of study, whichever is more consistent with the selected thesis.

A. Requirements for Admission

For admission to the Master of Science program in Data Science, applicants must meet the requirements of the University as listed in this Catalog. A baccalaureate degree from an accredited institution in a quantitative field including but not limited to computer science, data science, other computing disciplines, statistics, mathematics, engineering, physics, economics or relevant fields is required.

Applicants must have a GPA of at least 3.0 on a 4.0 scale in an earned baccalaureate degree. Applicants with a GPA between 2.5 and 3.0 will be considered for admission on a case-by-case basis and must provide general GRE scores.

Applicants with a baccalaureate degree in computer science, data science, other computing disciplines, statistics, mathematics, engineering, physics, or economics from an accredited institution in the U.S. or Canada with a GPA of at least 3.0 are not required to provide general GRE scores. The waiver of general GRE scores will be considered on a case-by-case basis for applicants from disciplines other than stated above from an accredited institution in the U.S. or Canada.

Applicants with a baccalaureate degree from an institution outside the U.S. or Canada must provide general GRE scores.

Additional requirements for international applicants can be found in the section for International Admissions.

Requirements for Classified Status

To attain fully classified graduate status in the program, students must complete any required prerequisite undergraduate material and have a 3.0 GPA for all work completed as a conditionally classified student. Information about the prerequisite material can be obtained from the graduate coordinator or academic lead.

B. Degree Requirements

All courses in the student’s graduate program must be completed with a grade of “C” or better. No course taken more than 7 years prior to the date of which all requirements for the degree are completed may be counted as part of the 30 units in the degree program. No time limit applies to courses taken to satisfy Data Science M.S. prerequisite requirements.

State-Support Program

1. Required Courses (21 units)

a. Breadth Requirement (15 units)

b. Culminating Experience (6 units)

Thesis

COMP 696 Directed Graduate Research (3)
COMP 698C Thesis (3)

Each Data Science M.S. candidate must submit a proposal for a thesis to be done under the supervision of a faculty member. When the thesis is approved by that faculty member, the graduate coordinator and the department chair, the proposal becomes a contract between the student and the department as to the work to be done for the thesis. A three-member thesis committee is formed with that faculty member as its chair. When the work is done, the student must prepare a report and defend or present the results of the thesis before the committee. The report and presentation must be approved by the student’s thesis committee.

2. Electives (9 units)

a. Data Science Electives A (3 units)

Select one of the following courses:

COMP 502 Programming for Data Science and Analytics (3)*
COMP 541 Data Mining (3)*
COMP 640 Database System Design (3)
COMP 642 Advanced Databases and Data Visualization (3)
COMP 643 Deep Learning (3)

*If not chosen in 1.a. above.

b. Data Science Electives B (3 units)

Select one of the following courses:

COMP 502 Programming for Data Science and Analytics (3)**
COMP 535/L Parallel and Distributed Computing (2/1)
COMP 541 Data Mining (3)**
COMP 545 Cloud Computing (3)
COMP 569 Artificial Intelligence (3)
COMP 640 Database System Design (3)***
COMP 642 Advanced Databases and Data Visualization (3)***
COMP 643 Deep Learning (3)***

**If not chosen in 1.a. or 2.a. above.
***If not chosen in 2.a. above.

c. Other Electives (3 units)

Computer Science courses at the 400- (that are available for graduate credit), 500- or 600-level including Data Science electives in 2.a or 2.b above (excluding COMP 440, COMP 442, COMP 482, COMP 490/L, COMP 491/L, COMP 492, COMP 494, COMP 499, COMP 696, COMP 698, COMP 698DS, and COMP 699).

Requests for elective courses that do not meet the requirements stated above must be approved by the student’s thesis committee chair, the department graduate coordinator and the department chair prior to course enrollment. The student’s thesis committee chair may require that specific elective courses be taken prior to enrollment in COMP 696 and COMP 698. Students should seek approval from their committee chair prior to enrolling in elective courses. At least 6 units must be at the 500-level or above.

Total Units Required for the M.S. Degree: 30

Self-Support Program

Note: Due to the cohort nature of the program, electives are prescribed for students enrolling through The Tseng College.

1. Required Courses (27 units)

COMP 502 Programming for Data Science and Analytics (3)
COMP 541 Data Mining (3)
COMP 542 Machine Learning (3)
COMP 639 Probability and Statistics for Data Science (3)
COMP 640 Database System Design (3)
COMP 641 Fundamentals of Data Science (3)
COMP 642 Advanced Databases and Data Visualization (3)
COMP 643 Deep Learning (3)
COMP 644 Big Data (3)

2. Culminating Experience (3 units)

COMP 698DS Graduate Project (3)

Total Units Required for the M.S. Degree: 30