Data Science, Computational

Subject abbreviation: CPDS The Marlan and Rosemary Bourns College of Engineering

Vassilis Tsotras, Ph.D., Director datascience.ucr.edu/graduate/ computational-data-science

Program Faculty

Salman Asif, Ph.D. (Electrical and Computer Engineering) Bir Bhanu, Ph.D. (Electrical and Computer Engineering) Jia Chen, Ph.D. (Electrical and Computer Engineering) Evangelos Christidis, Ph.D. (Computer Science and Engineering) Yue Dong, Ph.D. (Computer Science and Engineering) Ahmed Eldawy, Ph.D. (Computer Science and Engineering) Basak Guler, Ph.D. (Electrical and Computer Engineering) Eamonn Keogh, Ph.D. (Computer Science and Engineering) Paea LePendu, Ph.D. (Computer Science and Engineering) Amr Magdy, Ph.D. (Computer Science and Engineering) Evangelos Papalexakis, Ph.D. (Computer Science and Engineering) Chinya Ravishankar, Ph.D. (Computer Science and Engineering) Amit Roy-Chowdhury, Ph.D. (Electrical and Computer Engineering) Elaheh Sadredini, Ph.D. (Computer Science and Engineering) Mariam Salloum, Ph.D. (Computer Science and Engineering) Christian Shelton, Ph.D. (Computer Science and Engineering) Vassilis Tsotras, Ph.D. (Computer Science and Engineering) Ertem Tuncel, Ph.D. (Electrical and Computer Engineering) Greg Ver Steeg, Ph.D. (Computer Science and Engineering) Neftali Watkinson, Ph.D. (Computer Science and Engineering) Nanpeng Yu, Ph.D. (Electrical and Computer Engineering)

M.S. in Computational Data Science The Marlan and Rosemary Bourns College of Engineering offers an M.S. program in Computational Data Science.

Admission All applicants to this program must have completed a bachelor’s degree or its approved equivalent from an accredited institution and to have attained undergraduate record that satisfies the standards established by the Graduate Division and University Graduate Council. Applicants must supply GRE General Test scores. Applicants whose first language is not English are required to submit acceptable scores from the TEST of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS) unless they have a degree from an institution where English is the exclusive language of instruction. Additionally, each applicant must submit letters of recommendation, as per the admission requirements. All other application requirements are specified in the graduate application.

Prerequisite Material Applicants need experience in programming, software engineering, algorithms, and background in statistics. Competence in these areas is defined by the following UCR undergraduate courses (or equivalents):

CS 141, CS 100, MATH 010A, MATH 031, as well a course covering foundations of probability and statistics (like STAT 155 or EE 114)

Applicants who fail to meet this criterion may sometimes be admitted with course deficiencies, provided they take remedial steps to cover the deficiencies. A student who is deficient in a competency area may be asked to complete the corresponding UCR course with a letter grade of at least B, or to pass a challenge examination based on that course’s final exam with a grade of at least B. All such remedial work cannot be counted towards the MS degree requirements and should be completed within the first year of graduate study, and in all cases the deficiency(s) must be corrected BEFORE a student can enroll in any graduate course from the same specialty area. The details will be decided by the Graduate Advisor of the program in consultation with the student. Course Work The M.S. in Computational Data Science requires the completion of 49 units of coursework, including a capstone project. There are no thesis or comprehensive exam options.

The coursework consists of 5 core courses, 6 elective courses, a professional development course and the capstone course. Elective courses are selected by the student from a list of possible courses; students can petition to select a course not on the list. Students who have completed similar courses elsewhere may petition for waiver of a required course or for substitution of an alternative course.

Core Courses (20 units): All students must complete the same core courses.

• CS 252A/EE 251A – Data Analytics and

Exploration

• CS 252B/EE 251B – Fundamentals of Data

Science or CS 224 – Fundamentals of Machine Learning

• CS 226 – Big-Data Management or

CS 236 – Database Management Systems

• CS 235 – Data Mining Techniques

• CS 108/STAT 108 – Data Science Ethics

Elective Courses (24 units): The six electives must be selected from the following two lists of elective courses where at least four courses must be from list A. Students may petition for other elective courses; such petitions require approval from the program’s graduate advisor. Courses used to satisfy the Core Courses requirements may not be used to satisfy the Elective Courses requirements.

Elective List A: CS 205, CS 222, CS 225, CS 226 or CS 236, CS 227, CS/EE 228, CS 229, CS 242, CS/EE 248, EE 227/ CS 258, EE 231, EE 236, EE 240, EE 244

Elective List B: CS 210, CS 211, CS/EE 217, CS 234, EE 241, EE 243, EE 250

Professional Development Requirement Students will satisfy the professional development requirement by enrolling in one of the following courses: one quarter of CS 287, or GDIV 403, or at least one unit of CS 298I.

Capstone Experience Students must complete CS/EE 279 under the guidance of the capstone instructor member.

Normative Time to Degree Six quarters (2 years).