ucsd data science phd deadline

UC San Diego Launches New Data Science Graduate Degree Programs

Xochitl Rojas-Rocha

Published Date

Programs designed for access and success

HDSI’s new graduate degree programs provide fresh pathways for expanding and training a talent pool drawn from diverse academic backgrounds. Upon entry, HDSI offers incoming students individual counseling and a graduate experience designed to meet their specific needs.

The programs place students into categories depending on their academic preparation, including (a) those with computing backgrounds (degree and/or work experience in computer science, electrical engineering, or other engineering areas); (b) those with a mathematics or statistics background (degree and/or work experience); and (c) those with a background or work experience in applied data science, such as economics, computational social sciences, natural or biological sciences. Each student can look forward to a course pathway that meets them where they are, regardless of their background.

HDSI staff have also designed the new programs with the express goal of broadening participation by offering students the option to learn the basics of data science areas at their own pace through online courses.

HDSI advances the field of data science by exploring principles, methods, and tools that enable us to understand the nature of digital data and the interactions of this new field with existing disciplines of human inquiry. The Institute seeks to catalyze new research and create a thriving, self-identifying community of data science researchers, practitioners, and industry partners. New research is enabled and supported through a number of research projects across UC San Diego and the HDSI Industry Partner Alliance member companies and other stakeholders who seek to advance methods and tools on projects where data science is having a direct impact.

To learn more about UC San Diego Data Science graduate programs, including admission deadlines and requirements, visit https://datascience.ucsd.edu/academics/graduate/ .

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Computer Science & Engineering

UCSD CSE Graduate Admissions

Updated July 12th,2024

We appreciate your interest in the graduate program in the Department of Computer Science & Engineering at UC San Diego. We offer PhD and MS degrees in both computer science and computer engineering. We also have a streamlined 5-year BS/MS program for highly qualified UCSD CSE undergraduates. Undocumented students are welcome to apply.

Department Overview

The UCSD Application for Graduate Admission opens for Fall 2025 admission from September 4, 2024 until December 18, 2024 at 11:59 p.m. PST. Admission is given for Fall quarter only . We do not admit new students in Spring quarters. All application materials must be submitted through the online application.

The GRE is not required for PhD and MS applications for Fall 2025. Applicants may still submit valid scores if they have already taken the test.

The TOEFL iBT Special Home Edition will be accepted for admission at both the Master's and PhD levels. However, the TOEFL ITP Plus for China Solution cannot be used to substitute for a standard TOEFL iBT test. The Department will also now accept IELTS scores to verify English language proficiency.

For information about the online application and required supplemental documents, please refer to: Application Checklist and Guidelines.

About the Admissions Process

Admission to the CSE graduate program is very selective. Our department receives over 6,000 applications annually. (For Grad Data, please click here ).

We seek applicants with a strong academic background in computer science and engineering and/or a related field and a demonstrated potential for success in graduate school. Each application undergoes a comprehensive review by the CSE Admissions Committee, which examines every component of the application and supplemental materials when considering an applicant for admission. (Standardized test scores and grade point averages (GPA) are an integral part of the evaluation process, but admissions decisions are NOT made based on these components alone.)

The MS and PhD degree programs are mutually independent. You may only apply for one program or the other ; applications for both the MS and PhD programs will not be accepted. ( An MS degree is not required for admission to the PhD program.)

Current or former UCSD students in the MS program who wish to continue/transfer to the PhD program, will complete the UCSD Application for Graduate Admission by the general application deadline for the year they are seeking admission ( Admission is ONLY given in the Fall quarters. ) The application will be reviewed along with those of the external PhD applicants for that admissions cycle. MS to PhD students must complete their application online, but will NOT be able to "submit" the application. Once you have completed your online application, please email [email protected] to notify the team, so we can track your application. When sending an email, please include "MS to PhD Transfer Application Submission", in the subject line. For a complete list of all of the supplemental materials required for the graduate application, please visit our Graduate Application Checklist .

If you are interested in enrolling in Computer Science and Engineering classes without being matriculated in a degree program you may do so through the Concurrent Enrollment Program .

The CSE department has a limited number of application fee waivers available for PhD international students. Our department will be granting these waivers to students based on factors that include: contributions to diversity; social justice experience; overcoming adversity. Unfortunately, we are not able to grant application fee waivers on the basis of financial need alone. If you would like to request a CSE department fee waiver, please fill out the Fee Waiver Application. (Form will be updated by mid September, please note if applying for a fee waiver you must submit your application early at least 2 weeks early. )

Graduate Admission Requirements

To be considered for admission, applicants must meet the minimum university and departmental requirements outlined below. All application documents are submitted to the Graduate Division Online application at UCSD Application for Graduate Admission . For a complete list of all of the supplemental materials required for the graduate application, please visit our Graduate Application Checklist .

Additional questions about admission into our graduate program may be answered on our Graduate Admissions FAQ page. Please also see the Prospective Student web pages on the Graduate Education and Postdoctoral Affairs (GEPA) .

Undocumented Students : Undocumented students are welcome to apply. For additional Information: Undocumented Student Services

Prerequisites

A bachelor's degree in computer science, computer engineering, electrical engineering, or mathematics is preferred, but not required. Applicants with a degree in another discipline will be considered for admission if they have completed the minimum required CSE courses or their equivalent. However, it is recommended that applicants without a CSE background take courses beyond the minimum to demonstrate an ability to understand more advanced concepts in computer science and engineering: My bachelors degree isn't in CS. What kind of background should I have before applying to the program?

Academics and GPA

Applicants must hold a bachelor's degree or the equivalent from an accredited institution in the United States or from a recognized university-level academic institution abroad. At least a B average (3.0 GPA) or its equivalent is required for admission. Satisfaction of minimal standards does not, however, guarantee admission, since the number of qualified applicants far exceeds the number of places available. International and U.S. Applicants should refer to the UCSD Academic Policies - Admissions .

Test Scores

GRE: The GRE is not required for PhD and MS applications for Fall 2025. Applicants may still submit valid scores if they have already taken the test.

Applicants with valid GRE test scores should request that ETS submit the scores directly to the UCSD institution code 4836. The department codes are not necessary . Information about the GRE is available from the Educational Testing Service (ETS) website.

TOEFL/IELTS: The Test of English as a Foreign Language ( TOEFL ) OR the International English Language Testing System ( IELTS ) is required for international applicants whose native language is not English and who have not studied full-time at a university-level institution where the sole language of instruction is in English.

The UCSD TOEFL Institution Code is 4836. No department code is required. The university minimum TOEFL score required for admission is 550 for the paper-and-pencil version, 213 for the computer-based test or 85 for the internet-based test (iBT). The TOEFL iBT Special Home Edition will be accepted for admission at both the Master's and PhD levels. However, the TOEFL ITP Plus for China Solution cannot be used to substitute for a standard TOEFL iBT test. For more information on the TOEFL, visit the TOEFL website . An official ETS-reported score must be submitted for an applicant to be admitted into the graduate program. The Test of Spoken English (TSE) is not required.

The university minimum IELTS score required for admission is Band Score 7 . To submit official test scores to UC San Diego Graduate Division, applicants must contact the IELTS test center where they took the test to request their official test results to be sent to the address below. UC San Diego does not accept unofficial copies of IELTS Test Report Forms from students. An institution code is not required.

University of California, San Diego Graduate Admissions 9500 Gilman Drive #0003 La Jolla, CA 92093-0003

Duplication of Degrees

NOTICE - Normally, UCSD does not permit the duplication of advanced degrees. (Previous professional degrees, however, are not included in this restriction.) Holders of a master's degree in one field may be considered under certain circumstances for admission into a master's degree program in another field (after the admissions faculty committee reviews all application files in any given admissions cycle). Holders of a PhD, in any field, are advised not to apply for admission to the CSE department's PhD program. For more information, please refer to here .

To learn more about UC San Diego Graduate Student-related data or data about a specific department before applying, please visit the Division of Graduate Education and Postdoctoral Affairs website linked here .

Future Events

If you are interested in attending future recruitment events and fairs where you are able to connect with Computer Science and Engineering MS and PhD advisors, please complete the following interest form . By submitting this form, you will receive information from UC San Diego.

Here are some upcoming events where the Graduate Division will speak with prospective students and offer guidance on everything from admissions to graduate student life.

Contact Information

If you still have questions for which you could not find an answer on our website, please email either one of the following:

PhD Admissions at [email protected]

MS Admissions at [email protected]

The University of California, San Diego does not discriminate on the basis of race, color, national origin, religion, sex, disability, age or sexual orientation in any of its policies, procedures, or practices, including but not limited to academic admission, financial aid, educational services, and student employment.

Key Features

2-year, in-person technical program taught by ucsd faculty experts, friday and saturday class schedule, alternating weekends, 38 units in 24 month program, tuition is $1,035 per unit, next class starts fall 2024 .

The Data Science and Engineering program combines the skills of a software programmer, database manager, and statistician to create mathematical models of the data, identify trends/deviations, then present them in effective visual ways that can be understood by others.

Data scientists unlock new sources of economic value, provide fresh insights into science, and inform decision makers by analyzing large, diverse, complex, longitudinal, and distributed data sets generated from instruments, sensors, internet transactions, email, video, and other digital sources.

Students entering the MAS program for a degree in Data Science and Engineering will undertake courses in programming, analysis, and applications management and visualization. This program requires three foundational courses and six core courses totaling 34 units, plus a capstone team project course of four units, for a total of 38 units.

Courses in the Data Science & Engineering program are taught by world class faculty from the Computer Science & Engineering Department and the San Diego Supercomputer Center.

Courses and descriptions can be viewed on the Course Catalog .

This program is considered part-time as enrollment each quarter is 8 units or less.

2024-2025 Academic Calendar

View sample capstones from previous years.

Prospective students often ask for links to resources that would be helpful to review in preparation for the DSE program. In response, the faculty have created a page of "brush up" materials covering math, programming and databases.

The Masters of Advanced Study degree programs at Jacobs School of Engineering are entirely self-supporting and receive no state funding. The cost of the master's program is all inclusive - it includes all instruction costs, administrative costs, textbooks, course materials, on-campus parking and meals on all class days.

The tuition for 2024 - 2026 is $1,035 per unit (38 units).

The per unit tuition does not include mandatory student fees which are determined annually by UCSD. It also assumes students wave UCSD student health insurance. Additional information about tuition and fees is available on the UCSD Website .

Military-affiliated students can find additional information here .

Students needing Financial Aid can view this.

In addition, many employers have benefits programs that help pay for additional education. Contact your HR department to see if your company offers these benefits.

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At the University of California, San Diego, diversity is a core component of excellence that further enhances our quality and achievement. We seek a diverse graduate student body to ensure that all of our students gain the educational benefits that result from being exposed to a broad spectrum of ideas and perspectives. These include the variety of personal experiences, values, and worldviews that arise from differences of culture and circumstance. Such differences include race, ethnicity, gender, age, religion, language, abilities/disabilities, sexual orientation, socioeconomic status, geographic region and more. We wish to broaden and deepen both the educational experience and the scholarly environment, as students and faculty learn to interact effectively with each other, preparing them to participate in an increasingly complex and pluralistic society. We also want all of our students to contribute to the campus community in a manner that enhances campus diversity and inclusiveness, consistent with the University of California Principles of Community .

The UC San Diego Graduate Application can be completed on-line.

The Admissions Committee of the program considers all elements of the student's file. Top applicants are usually invited to UCSD to visit the campus and participate in faculty interviews in February and March. Arrangements are made by telephone or e-mail from the program office. It is therefore essential that you provide a correct telephone number (home and work) and email address where you can be reached during the winter and spring months.

We recognize that the graduate school admissions process can be opaque and intimidating. To address this problem, we have established the Biomedical Application Assistance Program (BMAAP), a student-run program that aims to demystify the admissions process for applicants. For more information, please visit the BMAAP Program page.

Requirements

The The Division of Graduate Education and Postdoctoral Affairs at UCSD requires a minimum GPA of 3.0 for admission to graduate school. The Biomedical Sciences Admissions Committee looks specifically at an applicant’s cumulative and science GPAs, and at the types of courses taken. Recommended courses include calculus, biochemistry, organic and physical chemistry, biology, and, preferably, cell and molecular biology and mammalian physiology.

As of 2018, applicants are no longer required to submit scores for either the GRE General or Subject Tests. Applicants can optionally submit scores for the GRE General Test (verbal, quantitative, and analytical sections) and/or an applicable GRE Subject Test. If an applicant wishes to take the GRE tests, it is advised to take the GRE in the fall prior to the fall term for which admission is sought. Applicants may self-report scores at the time of application submission. When ordering your GRE score reports, use UCSD's institution code 4836. No department codes are necessary. GRE score reports are typically received electronically within 5-7 business days from the order date. More information about the GRE may be obtained from the Educational Testing Service (ETS) website.

The UCSD Graduate Application fee is $135 ($155 for international applicants). This fee may be waived for applicants who demonstrate financial hardship, US Military service, or who have participated in certain graduate preparatory programs. Please see this page for more information. For international students requesting a fee waiver, please visit this link .

Preliminary Application (International applicants)

The preliminary application is no longer required. All student applications are given equal consideration in the application review process.

Apply for Fall 2025

Official Graduate Application Opens - early September 2024

Official Graduate Application Deadline - approximately late November 2024

Interviews and Recruitment (In-Person) Weekends - to be announced, most likely in February 2025

Begin Your Application

More information.

Frequently Asked Questions

Please contact the Biomedical Sciences Graduate Program offfice for more information or questions: [email protected].

Statement of Purpose

Focus your Statement of Purpose on the reasons you are interested in attending the UC San Diego Biomedical Sciences Graduate Program. The statement has a 1500 word limit and should be well organized, concise, and completely free of grammar, punctuation, and spelling errors.

Include responses to the following as part of your statement:

Summarize the long term goals of research in which you participated.
Describe one research problem, project or area for graduate study that excites you. Have you thought about what you want to accomplish in graduate school and how is UC San Diego a good match for these goals?

Letters of Recommendation

These are of great value in assessing a student’s qualifications for a career in research. It is advantageous to have letters submitted by well-known faculty who can critically evaluate a student’s academic performance, undergraduate research experience, and potential for a career in biomedical sciences.

Research Experience

Undergraduate and/or post-college research experience is considered an important part of a student’s preparation for graduate work. It gives the student the opportunity to experience laboratory work and thus make a knowledgeable decision to pursue a career in basic biomedical sciences.

Official Application

Screening of applications will begin in late November. It is recommended that applicants submit their application well before the deadline to be considered for on-campus interviews. Typical causes of delayed consideration include missing letters of recommendation and transcripts.

PLEASE NOTE: To expedite the processing of your application, the Biomedical Sciences Admissions Committee requires that you upload PDF versions of your official or unofficial transcripts directly into the UCSD on-line application system. More information on this process is available in the on-line application system.

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The University of California, in compliance with Title VI of the Civil Rights Act of 1964, Title IX of the Education Amendments of 1972, Section 504 of the Rehabilitation Act of 1973, and the Age Discrimination Act of 1975, and the Americans with Disabilities Act of 1990, does not discriminate on the basis of race, color, national origin, religion, sex, disability, or age in any of its policies, procedures, or practices; nor does the university discriminate on the basis of sexual orientation. This nondiscrimination policy covers admission and access to, and treatment and employment in, university programs and activities, including but not limited to, academic admission, financial aid, educational services, and student employment.

Inquiries regarding the university's equal opportunity policies may be directed to the campus compliance coordinator, (858) 534-0195.

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Electrical and Computer Engineering

Graduate Admissions

Online application , (fall 2025 applications open in early september) m.s. & ph.d. fall 2025 deadlines:, monday december 16th.

by 11:59pm (California time)

Sean Jones Email: [email protected] Phone: (858) 534-3213

Thank you for your interest in UC San Diego's Electrical and Computer Engineering graduate programs! ECE has long been a powerhouse at UC San Diego with its innovative and impactful research led by top-notch faculty and outstanding graduate and undergraduate students. When our Chancellor challenged us to "break from the ordinary" to make the world a better place, ECE accepted it with a conviction that the work we can do can change our future. For more information, we invite you to review our webpages. If you have any questions, please contact us at [email protected] .

Ph.D.: December 16th, 2024 M.S.: December 16th, 2024

The ECE Department admits students once per year, for the fall term only

Apply Here!: UCSD Online Admissions Application (Fall applications open in early September)

You will need:

Statement of Purpose (PDF format no larger than 2 MB; uploaded to online application)
Three Letters of Recommendation (Uploaded to online application)
Scanned unofficial copies of transcripts from all higher education institutions attended (Uploaded to online application)
Official GRE General Test scores (submitted to UCSD via ETS; see "Admission Requirements" section below. (Submitting scores are required for M.S. applications. For Ph.D. applications, scores are not required, but recommended ).
Demonstration of English proficiency (International applicants only; see "Admission Requirements" section below)
Resume/CV (recommended; uploaded to online application)
Pay the non-refundable application fee

For our impacted majors/research areas, you will not have the opportunity to switch into them if you are admitted to a different major.

Our impacted majors are:

Computer Engineering (EC79)
Electronic Circuits and Systems (EC78)
Intelligent Systems Robotics and Control (EC80)
Machine Learning and Data Science (EC93)

Therefore if you are interested in any of these majors, be sure to apply to them when filling out your application.

You may seek approval to switch into one of our non-impacted majors/research areas after your first quarter in your program.

A B.S. and/or M.S. degree in engineering, physical sciences, or mathematics from an accredited college or university
Minimum cumulative GPA of 3.0 (on a 4.0 scale or its equivalent)
GRE General Test (Submitting scores are required for M.S. applications. For Ph.D. applications, scores are not required, but recommended ). Use institution code 4836 to report scores to UCSD.
Option 1: TOEFL with a minimum score of 550 (paper based test-PBT) or 85 (internet based test-iBT). Use institution code 4836 to report scores to UCSD.
Option 2 : IELTS with a minimum Band Score of 7.
Option 3: Pearson Test of English (PTE) Academic with a minimum overall score of 65.
Option 4: Earned or will be earning a bachelor's, master's, or doctoral degree with grades of B (3.0) or better from a university which provides instruction solely in English. You may verify whether your institution meets this requirement by looking up your institution in the IAU World Higher Education Database (WHED) . If English is not the sole language of instruction listed, if no language is listed at all, or if the institution does not appear on the WHED website, you are required to submit English proficiency exam scores. No other documentation (e.g. letters, language certificates, school websites) may be used in place of WHED as a means to obtain an exemption from the English proficiency requirement.

Checking your Application Status

Applicants may check the status of their application via their online application portal.

A completed online application will have the status "Submitted". The ECE Faculty Admissions Committee commences their review of applications in December, following the application deadline.

Please do not contact the ECE Graduate Affairs Office to obtain the status of your application. Each applicant will receive notification when an admission decision has been made.

Application Timeline

We appreciate your patience in awaiting a decision. Our application review process will not commence until after the application deadlines. MS and PhD decisions will be released on a rolling basis starting at the end of January with the goal of making the majority of the decisions by early April. As soon as a decision is made on your file, you will receive an email notification. If you have any doubts, please visit your application portal for the most up to date application status.

Admissions Frequently Asked Questions Page

The Ph.D. in Mathematics, with a Specialization in Statistics is designed to provide a student with solid training in statistical theory and methodology that find broad application in various areas of scientific research including natural, biomedical and social sciences, as well as engineering, finance, business management and government regulations. It aims to produce future researchers in contemporary statistics, both in academia and industry, who will contribute to satisfy the tremendous need for new statistics theory and methodology following the rapid growth of computing power, high throughput technology, and the explosion of digital data acquisition technologies.

Prospective students must apply to the Ph.D. program in Mathematics and select "Statistics" in the "Current Area of Interest" section of their on line application (this means the person is applying the Specialization in Statistics degree). Demonstration of computer literacy is highly desired; knowledge of a programming language such as Perl or C, and knowledge of a statistical computing package such as SAS, R, S-PLUS or STATA are also desirable. The program may admit students without this level of preparation with the understanding that the student will promptly make up any deficiencies by taking additional courses upon entering the program.

Program Requirements for the Specialization in Statistics

The specialization requires completion of 72 units before advancement to Ph.D. candidacy.
Full-time students are required to register for a minimum of twelve (12) units every quarter, eight (8) of which must be graduate-level mathematics courses taken for a letter grade only.
MATH 280A-B-C (Probability Theory)
MATH 281A-B-C (Mathematical Statistics)
MATH 282A-B (Applied Statistics)
MATH 287A (Time Series Analysis)
MATH 287B (Multivariate Analysis)
MATH 287C (Advanced Time Series Analysis)
MATH 287D (Statistical Learning)
MATH 202A (Applied Algebra I)
MATH 240A-B-C (Real Analysis)
MATH 241A-B (Functional Analysis)
MATH 261A-B-C (Probabilistic Combinatorics and Algorithms)
MATH 270A-B (Numerical Analysis)
MATH 271A-B-C (Numerical Optimization)
MATH 283 (Statistical Methods in Bioinformatics)
MATH 285 (Stochastic Processes)
MATH 289A-B (Topics in Probability and Statistics)
MATH 294 (The Mathematics of Finance)
Candidates must acquire experience in statistical consulting and the practical analysis of data. To meet this requirement, students must participate in the MathStorm graduate student consulting seminar for one year. A project outside the consulting seminar can be substituted only if prior approval is obtained from the director of the consulting seminar and the student's advisor. Students should complete at least five quarters of coursework before taking the consulting seminar and are encouraged to fulfill the requirement in their third or fourth year.
Students must pass two written qualifying exams. One of the required exams is in Mathematical Statistics (MATH 281A-B-C) the other is recommended to be in Real Analysis (MATH 240A-B-C). At least one of the exams should be passed at the Ph.D. level, and both exams should be passed at the provisional doctoral level or better.
At least one of the exams should be passed at the provisional doctoral level before the start of the second year and both passed before the start of the third year, for the student to remain in the Ph.D. program.
Before the start of the third year, the student is required to take Applied Statistics (MATH 282A-B) and pass the comprehensive exam in this subject.
No foreign language requirement.

Advancement to Candidacy

It is expected that by the end of the third year (9 quarters), students should have a field of research chosen and a faculty member willing to direct and guide them. A student will advance to candidacy after successfully passing the oral qualifying examination, which deals primarily with the area of research proposed but may include the project itself. This examination is conducted by the student's appointed doctoral committee. Based on their recommendation, a student advances to candidacy and is awarded the C. Phil. degree.

Dissertation and Final Defense

Students participating in the Ph.D. in Mathematics with a Specialization in Statistics must complete a dissertation and final defense that meets all requirements for the regular Ph.D. in mathematics.

Students who wish to switch between the regular Ph.D. program in Mathematics and the Specialization in Statistics must submit a written request to the graduate vice chair for consideration. Approval is not automatic, however.

$ucsd data science phd deadline$

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PhD in Data Science, UCSD Fall 2023

Hello everybody, I had applied to the PhD in Computer science program at UCSD. A couple of months post the deadline, I receive an email from the dept offering me the opportunity to apply to the Data science program with an application fee waiver. The deadline for admission offers to be given out as per the website is March 15th and I haven't heard from the department yet ( not even regarding the interview ). Neither from the Computer science dept nor from the data science dept. Is anybody else also in the same boat? Have the admission decisions been rolled out? Not much on grad cafe with respect to the Data Science program either. This is scary!

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Staff Research Associate 1 - 131558

Job description, #131558 staff research associate 1.

UCSD Layoff from Career Appointment : Apply by 08/16/2024 for consideration with preference for rehire. All layoff applicants should contact their Employment Advisor.

Special Selection Applicants : Apply by 08/28/2024. Eligible Special Selection clients should contact their Disability Counselor for assistance.

DESCRIPTION

Under supervision, incumbent will assist with experiments making technical determinations and/or observations related to research in rheumatoid arthritis.

Key Responsibilities: -Primary Cell Culture: Set up and maintain primary cell cultures. -Laboratory Techniques: Conduct experiments such as qPCR, cryosection, immunofluorescence, tissue disaggregation, and histology on human and mouse tissues, blood and tissue processing, as well as other experiments as needed. -Equipment Management: Install, operate, and maintain laboratory instruments and specialized equipment. -In Vivo Studies: Keep compliance records for animal use and manage the mouse colony. Adhere to animal use regulations. -Under supervision: Conduct arthritis models in mice; Execute acute inflammation models in mice. -Research Coordination: Organize research activities in the lab, including scheduling studies, and locating appropriate protocols and regulations for experiments. -Training and Communication: Notify lab members about relevant training courses in biosafety, animal welfare (if applicable), and new techniques. -Inventory Management: Maintain and record all reagents in freezers, refrigerators, and the liquid nitrogen tank. -Student Supervision: Train and direct students in their work. -Safety Compliance: Oversee laboratory environmental health and safety activities, including the disposal of biohazard waste.

Other duties assigned as needed.

MINIMUM QUALIFICATIONS

Theoretical knowledge of molecular biology, immunology, and biochemistry.

Willingness to learn new techniques and tasks and share knowledge to inform and teach students and paraprofessionals new techniques.

Experience with the operation of a personal computer and software application, such as Access, MS Word, Excel and R. Knowledge to work with and learn new software for analysis

Good organizational skills to order and maintain lab equipment and supplies.

Knowledge of biosafety techniques and practices. Experience with handling of chemicals and biological hazards.

Experience and ability to perform standard and specialized techniques required in research protocols. Skill to troubleshoot adaptations of experimental protocols.

Skill in accurate record keeping and ability to organize, analyze, and calculate data. Knowledge in basic math for maintaining ledgers, income and expenditures reports.

Experience in the use of common lab equipment such as balance, vortex, pH meter, autoclave, micropipette and centrifuge.

Interpersonal communication skills, and ability to supervise and interact with varied personalities.

PREFERRED QUALIFICATIONS

Bachelor's degree required in a related field related.

SPECIAL CONDITIONS

Occasional evenings and weekends as experiments require.

Must have access to reliable transportation to local hospitals for sample collection.

Employment is subject to a criminal background check.

Pay Transparency Act

Annual Full Pay Range: $55,374 - $66,064 (will be prorated if the appointment percentage is less than 100%)

Hourly Equivalent: $26.52 - $31.64

Factors in determining the appropriate compensation for a role include experience, skills, knowledge, abilities, education, licensure and certifications, and other business and organizational needs. The Hiring Pay Scale referenced in the job posting is the budgeted salary or hourly range that the University reasonably expects to pay for this position. The Annual Full Pay Range may be broader than what the University anticipates to pay for this position, based on internal equity, budget, and collective bargaining agreements (when applicable).

If employed by the University of California, you will be required to comply with our Policy on Vaccination Programs, which may be amended or revised from time to time. Federal, state, or local public health directives may impose additional requirements. If applicable, life-support certifications (BLS, NRP, ACLS, etc.) must include hands-on practice and in-person skills assessment; online-only certification is not acceptable.

UC San Diego Health Sciences is comprised of our School of Medicine, Skaggs School of Pharmacy and Pharmaceutical Sciences, The Herbert Wertheim School of Public Health and Human Longevity Science, and our Student Health and Well-Being Department. We have long been at the forefront of translational - or "bench-to-bedside" - research, transforming patient care through discovery and innovation leading to new drugs and technologies. Translational research is carried out every day in the hundreds of clinical trials of promising new therapies offered through UC San Diego Health, and in the drive of our researchers and clinician-scientists who are committed to having a significant impact on patient care. We invite you to join our team!

Applications/Resumes are accepted for current job openings only. For full consideration on any job, applications must be received prior to the initial closing date. If a job has an extended deadline, applications/resumes will be considered during the extension period; however, a job may be filled before the extended date is reached.

To foster the best possible working and learning environment, UC San Diego strives to cultivate a rich and diverse environment, inclusive and supportive of all students, faculty, staff and visitors. For more information, please visit UC San Diego Principles of Community .

UC San Diego is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.

For the University of California’s Affirmative Action Policy please visit: https://policy.ucop.edu/doc/4010393/PPSM-20 For the University of California’s Anti-Discrimination Policy, please visit: https://policy.ucop.edu/doc/1001004/Anti-Discrimination

UC San Diego is a smoke and tobacco free environment. Please visit smokefree.ucsd.edu for more information.

UC San Diego Health maintains a marijuana and drug free environment. Employees may be subject to drug screening.

Application Instructions

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Posted : 8/14/2024

Job Reference # : 131558

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Data Science

[ undergraduate program | courses | faculty ]

All courses, faculty listings, and curricular and degree requirements described herein are subject to change or deletion without notice.

The field of data science spans mathematical models, computational methods, and analysis tools for navigating and understanding data and applying these skills to a broad and emerging range of application domains. A whole range of industries—from drug discovery to healthcare management, from manufacturing to enterprise business processes as well as government organizations—are creating demand for data scientists with a skill set that enables them to create mathematical models of data, identify trends and patterns using suitable algorithms, and present the results in effective manners. The target systems can be, for example, biological (e.g., clinical data from cancer patients), physical (e.g., transportation networks), social (e.g., social networks), or cyber-physical (e.g., smart grids). In all these cases, there is a combination of core knowledge in information processing coupled with the skills to abstract, build, and test predictive and descriptive models that must be taught and learned in the context of an application domain. These application areas are in many domains served by engineering, physical sciences, social sciences, health and life sciences, and arts and humanities.

The Halıcıoğlu Data Science Institute’s (HDSI) data science programs are structured to provide access to education in data science for students drawn from diverse backgrounds. As a fundamentally quantitative discipline, an undergraduate education in quantitative disciplines is assumed. These include bachelor’s and/or master’s degrees in a quantitative field such as engineering, computer science, mathematics, statistics, cognitive science, disciplines in physical or life sciences, as well as quantitative social sciences such as econometrics, economics, or computational social sciences. Other degree options are acceptable with demonstrated course work or experience in programming, calculus, probability, and statistics.

For students who do not have any background in quantitative disciplines as mentioned above, we encourage signing up for any available introductory classes in our online MDS program or independent classes on introductory programming and introductory data science courses via EdX or Coursera platforms to assess interest and suitability.

For a listing of current participating faculty, please visit: https://datascience.ucsd.edu/about/faculty/

Overview of Graduate Degree Programs in Data Science

The Halıcıoğlu Data Science Institute (HDSI) offers the following three graduate degree programs in the data science area:

A residential degree program in master of science in data science, MS/DS
A residential degree program in doctor of philosophy in data science, PhD/DS
An online degree program in master of data science (online), MDS (pending WSCUC accreditation )

The online and residential degree programs have different admission requirements and processes that are described separately. Admission into one degree program does not automatically imply admission into any other degree program. For students enrolled in the residential MS/DS program, an independent application is needed for admission into the PhD/DS degree program. For students in the PhD/DS program, a residential MS/DS degree can be earned by following prescribed review and approval processes.

Residential Graduate Degree Programs

Admission to the residential degree programs in data science is done through the Graduate Division at UC San Diego. The application deadline is December for admissions effective the following fall quarter. For admission deadline and requirements, please refer to the departmental web page: http://datascience.ucsd.edu .

Admission decisions for the MS and PhD programs are made separately. A current MS student who wishes to enter the PhD program must submit a petition, including a new statement of purpose and three new letters of recommendation, to the HDSI graduate admissions committee.

Online Graduate Degree Program

The online master’s of data science (MDS) is a new degree program of its kind at the University of California, San Diego (pending WSCUC approval). The program is designed with the express goal of broadening participation into the growing field of data science by attracting talent from very different fields that are likely to benefit from advances in data science. The MDS program is also designed for working professionals who are able to pace their learning in view of their work-life balance. While the curriculum features the same level of rigor and learning goals as the residential MS/DS program, it provides flexibility to test and try different pathway courses online and devise a graduation plan before committing to a terminal degree program. Starting fall 2023, the program will also offer two opportunities annually for the students to apply and enter into the degree program starting fall and spring quarters respectively. Regardless of the pace of completion, the program requires a minimum of three quarter registration into the program to comply with UC Academic Senate rules for master’s degree programs.

Doctor of Philosophy (PhD) in Data Science

The goal of the doctoral program is to create leaders in the field of data science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them the knowledge, skills, and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in data science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models, and statistical analysis, to applications in data science.

Admission into the Program

A PhD degree in data science is an advanced degree that prepares students for leadership in data science research in academia, industry, and civic organizations. To be successful in this program, the students must have a background in quantitative analysis typically seen in degree programs with substantial mathematical preparation and programming skills. Admissions requirements for the PhD program are:

Bachelor’s and/or master’s degree in a quantitative field such as engineering, computer science, mathematics, statistics, cognitive science, scientific disciplines, or quantitative social sciences such as economics or computational social science. Other degree options are acceptable with demonstrated course work or experience in programming, calculus, probability, and statistics.
Undergraduate GPA of at least 3.0 on a 4.0 scale.
College transcripts.
Three letters of recommendation.
Optional GRE requirements as per the latest guidance from the Graduate Division at UC San Diego.
A statement of purpose that clearly outlines the motivation, background preparation, any relevant work experience in data science related areas, and topical interests for a degree in data science. Prospective students would be asked to identify any faculty members that they would like to seek as a research adviser.
The Test of English as a Foreign Language (TOEFL): The minimum TOEFL score for admission is 85 for the internet-based test and 64 for the paper-based test. Please note the paper-based test does not have a speaking component.
The International English Language Testing System (IELTS) Academic Training exam: The minimum IELTS score is band 7.0.
The Pearson Test of English Academic (PTE Academic). The minimum PTE academic score required for graduate admission is an overall score of 65.

Academic Preparation of Students Entering the PhD Program

Given the novelty of the degree programs in data science at the undergraduate level, we anticipate entering students to the graduate program with undergraduate training in areas outside of data science. In fact, the graduate program is designed to enable maximum participation of interested students from diverse educational backgrounds. However, ensuring a successful and timely completion of the graduate degree program requires academic preparation in five key areas of data science at the undergraduate level: algorithmic and programming skills, data organization methods and skills, numerical linear algebra, multivariate calculus, probability, and statistics.

While students with an undergraduate degree from a data science major or data science minor would have taken courses in all the five areas mentioned, we expect that students graduating from other quantitative undergraduate programs would have knowledge in the majority of the five areas mentioned above. There will be incoming students who would be lacking requisite knowledge and skills in some of these areas. To fill this gap, the program offers a set of foundational courses described in the next section.

These courses are designed to serve the needs of three classes of incoming students: (a) students with preparation in computing and/or information sciences at a level to master algorithmic programming and cloud computing skills; (b) students with preparation in mathematical subjects at a level to master statistical analysis and probability necessary for meaningful data analysis; (c) students who enter the program from other areas of science that rely upon collecting and analyzing observational or experimental data in order to advance scientific understanding. These are students with a degree in natural sciences such as physics, chemistry, biology, environmental sciences, etc., or coming from a social science background such as economics, political science, psychology, etc. Application examples may be causal inference in economics, assessing statistical significance of a pharmaceutical experiment or psychological treatment, the study of social networks in political science, etc.

We note that these are broad and overlapping categories. Even when students come prepared in both advanced computing and mathematics/statistics, data science education challenges them to apply these skills meaningfully in diverse applications, as well as improve their visualization/presentation skills. To do this successfully, students may need a working knowledge of the topics they may have already studied. As a result, Group A courses normalize background preparation of all our students with options that enable them to skip courses as appropriate but under careful supervision and advising discussed next.

Our graduate admissions process uses text analysis methods to automatically sort and bin admitted students into three pools as above, and thus drive the subsequent advising process; this will also include prior communication with the students regarding their preparation options using online and other offers by UC San Diego and other organizations.

Within the first week of arrival, each student will be scheduled for a one-on-one meeting with a faculty adviser and/or graduate program academic coordinator. After meeting with their faculty adviser, newly admitted students may be directed to take specific upper-division undergraduate courses from different areas, in order to solidify their backgrounds when or if there is some perceived weakness; up to two such courses may count towards their PhD degree units. The faculty adviser will also determine if an incoming student has strength in a particular area, and can thus avoid taking the area-associated course(s) among the five foundational courses of Group A.

The institute also offers preterm summer boot camp programs to help entering students with background preparation.

Course Requirements

There are foundation, core, and elective and research requirements for the graduate program. These course requirements are intended to ensure that students are exposed to (1) fundamental concepts and tools (foundation), (2) advanced, up-to-date views in topics central to data science for all students (core requirement), and (3) a deep, current view of their research or application (elective requirement). Courses may not fulfill more than one requirement.

The doctoral program is structured as a total of fifty-two units in courses grouped into foundational, core, professional preparation, and research experience areas as described below. Successful completion of the program requires successful and timely completion of three examinations and completion of a doctoral dissertation . Out of the fifty-two units, forty-eight units (or twelve courses) must be taken for letter grade and at least forty units must be using graduate-level courses.

The remaining four (= 52–48) units are for professional preparation , consisting of one unit of faculty research seminar, two units of TA/tutor training, and one unit of a survival skills course taken for a passing (satisfactory) grade. Finally, as mentioned earlier, out of the twelve regular courses, at least ten must be graduate-level courses; at most two can be upper-division undergraduate courses. Thirty-six units or nine courses must be completed within six quarters from the start of the degree program.

Group A, Group B, and Group C. Group A courses are introductory-level graduate courses in the foundational areas of data science. Group B are core graduate-level courses with prerequisites from Group A courses. Group C are advanced, specialized, and free-standing courses, often part of the required courses in the data science specialization of the graduate program in other departments. In all three groups, required courses are indicated as such; they cannot be substituted by other courses without exception approval from the graduate program committee.

Group A: Preparatory Courses

There are five important knowledge and skills areas necessary for understanding (and advancing) core data science. It is, therefore, important that all our entering students either have background preparation or courses available in the program to ensure a successful completion of the stipulated doctoral degree program. A student can receive credit towards the PhD degree for a maximum of three courses from the list of courses below:

DSC 200. Data Science Programming
DSC 202. Data Management for Data Science
DSC 210. Numerical Linear Algebra
DSC 211. Introduction to Optimization
DSC 212. Probability and Statistics for Data Science

Group B: Core Courses

Four core courses are required for all PhD students, including those with a bachelor’s degree in data science. The four required courses are:

DSC 240. Machine Learning
DSC 260. Data Ethics and Fairness
(*)DSC 241. Statistical Models (or MATH 282B)
(*)DSC 204A. Scalable Data Systems (or CSE 202)

(*) Depending on academic preparation, a PhD student can take an advanced course on applied statistics, such as MATH 282B instead of DSC 241. Similarly, instead of DSC 204A, a student can take a course on algorithms, such as CSE 202, Design and Analysis of Algorithms.

In addition, a doctoral student must select at least two out of the following eight core courses:

DSC 203. Data Visualization and Scalable Visual Analytics
DSC 204B. Big Data Analytics and Applications
DSC 242. High-Dimensional Probability and Statistics
DSC 243. Advanced Optimization
DSC 244. Large-Scale Statistical Analysis
DSC 245. Introduction to Causal Inference
DSC 250. Advanced Data Mining
DSC 261. Responsible Data Science

Thus, doctoral students are required to take a minimum of six courses for letter-grade credit from Group B courses. Students can take more than six courses from this group to satisfy letter-grade course requirements except (satisfactory completion of professional preparation) teaching, survival skills, and research seminar courses. Students who satisfy all letter-grade course requirements are expected to enroll in individual research (DSC 298) in a section offered by the faculty adviser to meet residency requirements and maintain graduate student standing during the period of dissertation research.

Group C: Professional Preparation and Elective Courses

Group C courses aim to provide either practical experiences in chosen specialization areas or advanced training for students preparing for doctoral programs. The courses include required professional preparation courses: two-unit TA/tutor training (DSC 599), one unit of academic survival skills (DSC 295), and one-unit faculty research seminar (DSC 293), all of which must be completed with a Satisfactory (S) grade using the S/U option.

Professional Preparation Courses

DSC 599. TA/Tutor Training
DSC 293. Faculty Research Seminar
DSC 294. Research Rotation
DSC 295. Academia Survival Skills

General Elective and Specialization Courses

Courses here aim to provide advanced training for students in the doctoral programs, or practical experiences in chosen specialization areas. Students can choose from the following elective or specialization tracks. Additional elective courses will be offered based on faculty interest and availability.

DSC 205, DSC 231, DSC 251, DSC 252, DSC 253, DSC 254, DSC 213, DSC 214.

CSE 234, MATH 181 A-B-C, MATH 284, MATH 285, MATH 287A-B, COGS 243.

Research Rotation Program

Research rotations provide the opportunity for first-year PhD students to obtain research experience under the guidance of HDSI faculty members. Through the rotations, students can identify a faculty member under whose supervision their dissertation research will be completed.

A research rotation is a guided research experience lasting one quarter (ten weeks) obtained by registering for DSC 294 with an instructor. All PhD students will participate in a minimum of two research rotations during their first year , and with a minimum of two different faculty members and as many as four rotations including summer quarter. A student may rotate twice under the same faculty member as long as they rotate with at least two faculty members. The goal is to help the student identify and develop their research interests and to expose students to new methodological approaches or domain knowledge that may be outside the scope of their eventual thesis.

Research rotations must be completed before the start of the second year with a signed commitment form from a faculty adviser. Those who fail to identify a research adviser shall be advised to leave the doctoral program with an optional assessment for completion of a terminal MS/DS degree.

Preliminary Assessment Examination

The preliminary assessment is an advisory examination. It consists of an oral examination in an area selected by the student with the goal to assess the student’s preparation for the proposed area, including several relevant topics, and identify any courses that are required or recommended for the candidate based on knowledge shown and critical missing background revealed.

The preliminary examination must be completed before the start of the second year in the doctoral degree program. The examination dates are announced no later than the start of the winter quarter along with the logistical details of the preliminary examination conducted by the graduate committee of HDSI. A failing grade in the preliminary examination would include a recommendation for the opportunity to receive a terminal MS/DS degree, provided the student can meet the degree requirements in no more than one extra quarter over the standard time for the MS program. Students who fail the preliminary examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time.

After a student successfully completes the preliminary assessment examination, in the next annual review of the student (conducted in the fall quarter), the departmental committee on graduate affairs of the HDSI faculty council assigns the academic adviser to provide necessary updates to the departmental committee on graduate affairs and helps set up the doctoral dissertation committee.

Research Qualifying Examination (UQE)

A research qualifying examination (UQE) is conducted by the dissertation committee. One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25 percent or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the Graduate Division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or twelve quarters from the start of the degree program; the UQE is tantamount to advancement to the PhD candidacy exam.

A petition to the graduate committee is required for students who take UQE after the required twelve quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS/DS track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense presentation and examination to the doctoral committee. One senate faculty member must have a primary appointment in the department outside of HDSI. As explained earlier, partially appointed faculty in HDSI (at 25 percent or less) are acceptable in meeting this outside department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of data science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715 requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

Special Requirements: Professional Training and Communications

All graduate students in the doctoral program are required to complete at least one quarter of experience in the classroom as teaching assistants regardless of their eventual career goals. Effective communications and ability to explain deep technical subjects is considered a key measure of a well-rounded doctoral education. Thus, PhD students are also required to take a one-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.

Special Requirements: Generalization, Reproducibility, and Responsibility (GRR)

A candidate for the doctoral degree in data science is expected to demonstrate evidence of generalization skills and reproducibility in research results, as well as the ability to responsibly conduct and use data science in light of potential ethical and societal implications of the research results.

Evidence of generalization skills may be in the form of—but not limited to—the generalization of results arrived at across domains or across applications within a domain, the generalization of applicability of method(s) proposed, or the generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirements may be satisfied by supplying additional supplementary material consisting of code, data repository along with evidence of independent external use, or adoption.

Evidence of the responsible use of data science includes the ability to collaboratively identify and respond to ethical and societal opportunities and risks and adhering to “best practices” in terms of ethical consequences (for example, obtaining appropriate consent for data collection about humans, documenting design, and modeling choices, etc.).

The GRR requirements will necessarily require a PhD student to be exposed to one or more application domains since understanding data upon which method advances are tried must be understood well by the researchers so that the objects of generalization, reproducibility, and responsible use are indeed supported by the experimental data. Normally this would be through an adviser or co-adviser who works in an application domain area, or through the rotation program. The institute provides software and services to help graduate students discover and meet relevant domain and method experts.

Relation to the Master of Science in Data Science (MS/DS) Degree Program

While the master’s and PhD programs are two independent programs, the PhD program provides students the ability to fulfill all requirements for the MS degree on their way to completion of the PhD program. This enables a doctoral student to apply for and receive an master’s degree in data science before the conferral of the PhD degree.

Student with Disabilities

In order for the program to respond, a student requiring accommodation for disability may make a request for accommodation upon submission of the student’s intent to apply to the graduate program. Declaration of any disability information is not part of the admissions review process and will not be a factor in admissions. Information concerning accommodation requests is available at: https://disabilities.ucsd.edu/ . Distance learning sites must confirm their ability to support students with disabilities.

Master of Science (MS) in Data Science

The goal of the master’s program is to teach students knowledge and skills required to be successful at performing data driven tasks, and lay the foundation for future researchers who can expand the boundaries of knowledge in data science itself. To meet its goals, the master of science in data science (MS/DS) program consists of two components: formal courses, as well as a terminating thesis or a course-directed comprehensive examination.

Admissions requirements for the MS/DS program are:

Bachelor’s degree in a quantitative field such as engineering, computer science, mathematics, statistics, cognitive science, scientific disciplines, or quantitative social sciences such as economics or computational social science. Other degree options are acceptable with demonstrated course work or experience in programming, calculus, probability, and statistics.
Undergraduate GPA of at least 3.0 on a 4.0 scale
College transcripts
Three letters of recommendation
Optional GRE requirements as per the latest guidance from the Graduate Division at UC San Diego

Academic Preparation and Course Planning for Students Entering the MS/DS Program

While students with an undergraduate degree from a data science major or data science minor would have taken courses in all five areas mentioned, we expect that students graduating from other quantitative undergraduate programs may be lacking requisite knowledge and skills in some of these areas. To fill this gap, the program offers a set of foundational courses described in the next section.

In case a student has to take all five foundational courses in Group A, the student should be prepared to spend one extra quarter in the degree program. It is possible, however, for the students who are trained in an application area of data science to save some time from elective courses and devise a graduation schedule within six quarters by exercising the thesis option that enables them to apply data science techniques to the applied field of their original expertise, thus reducing the course load in the elective series.

There are introductory, core, and elective and research requirements (Group A, B, and C courses below) for the master’s program. These course requirements are intended to ensure that students are exposed to (1) fundamental concepts and tools (foundation), (2) advanced, up-to-date views in topics central to data science for all students (core requirement), and (3) a deep, current view of their research or application (elective requirement). Courses may not fulfill more than one requirement.

The master of science in the data science (MS/DS) program is structured as a total of twelve (12) four-unit courses grouped into foundational, core, and specialization areas as described below. Successful completion of the program requires completion of a thesis or a course-based comprehensive examination that tests integrative knowledge across multiple courses. Out of the forty-eight units, at least forty units must be using graduate-level courses. In addition, two out of ten graduate courses can be in areas not directly related to data science but a domain specialization such as economics, biology, medicine, etc., upon approval of the student’s faculty adviser.

Group A: Introductory Courses: Maximum of Four Course Credit

These courses seek to provide five critical foundational knowledge and skills areas that each student graduating from the master’s program is expected to receive at a graduate level: programming skills, data organization and methods skills, numerical linear algebra, multivariate calculus, probability, and statistics.

The program is designed so that students lacking in any (and all) of these foundational knowledge and skills can take credit for a maximum of four courses from the following five courses: DSC 200, DSC 202, DSC 210, DSC 211, and DSC 212.

Group B: Core Courses: Three Required Courses, Minimum of Six Courses

These courses build upon foundational courses. All students must take three required core courses: DSC 240, DSC 241 (*), and DSC 260. In addition, students can select at least three out of the following core courses: DSC 203, DSC 204A (*), DSC 204B, DSC 242, DSC 243, DSC 244, DSC 245, DSC 250, DSC 261.

(*) Depending on academic preparation, a master’s student can take an advanced course on applied statistics, such as MATH 282B instead of DSC 241. Similarly, instead of DSC 204A, a student can take a course on algorithms, such as CSE 202, Design and Analysis of Algorithms.

Group C: Elective and Specialization Courses: Remaining Course Credit Requirements

The MS/DS students can take advantage of electives to complete their course of study. These courses can be advanced courses in core data science subjects listed under Group B as research topics (DSC 291) courses, or they can be graduate (or upper-division undergraduate) courses in other departments subject to approval by the student’s HDSI faculty adviser.

As a matter of guidance, students can choose from the following elective or specialization tracks to complete course requirements.

General Elective Courses:

DSC 205, DSC 231, DSC 251, DSC 252, DSC 253, DSC 254, DSC 213, DSC 214

CSE 234, MATH 181 A-B-C, MATH 284, MATH 285, MATH 287 A-B, COGS 243.

Specialization Areas: minimum of three courses required

Upon prior approval from a graduate adviser, students can sign up for an available specialization area. A specialization requires a minimum of three courses in a specialization area.

Specialization: Bioengineering

BENG 203, BENG 211, BENG 213, BENG 218, BENG 221, BENG 230A-B, BENG 276, COGS 278, PHYS 278, FMPH 223, FMPH 226.

Specialization: Business (marketing)

MGT 475, MGT 477, MGT 489, MGTA 455, MGTA 479.

Specialization: Business (supply chain and technology)

MGT 450, MGT 451, MGTA 456, MGTA 463.

Specialization: Business (finance)

MGT 407, MGTF 402, MGTF 404, MGTF 405, MGTF 406, MGTF 415.

Specialization: Machine Vision and Interaction Design

COGS 202, COGS 220, COGS 225, COGS 283.

Specialization: Computational Neuroscience

BGGN 246, BGGN 260, COGS 260 (or NEU 282), COGS 280.

Specialization: Networks

MATH 261A, MATH 277A, MATH 289A-B, DSC 205, BNFO 286, POLI 287, SIOB 276, ECE 227, MAE 247.

Availability of all specializations is not guaranteed. Additional specialization areas may be added by student petition.

Thesis or Comprehensive Exam Requirements

The MS/DS degree can be pursued under either the thesis option (Plan I) or the comprehensive examination option (Plan II). The comprehensive examination option follows a course-based comprehensive examination plan under the supervision of a comprehensive examination committee. For full-time students, all the requirements can be completed within one to two years. Students must register for a minimum of three quarters for residency requirements. To maintain good academic standing, students must be making timely and satisfactory progress toward completion of degree requirements and must maintain a minimum overall GPA of 3.0 at UC San Diego.

Approved Elective Courses and Research Credits

The number of elective and research units required varies by degree (see below). Electives are chosen from graduate courses in DSC, CSE, cognitive science, ECE, mathematics, or from other departments as approved. Please refer to the HDSI website for a list of approved electives. Courses must be completed for a letter grade, except for research units that are taken on a Satisfactory/Unsatisfactory basis. Seminar and teaching units may not count toward the electives and research requirement, although both are encouraged.

● Plan I: Thesis Option

The student must sign up for a minimum of eight and maximum of twelve units of DSC 299 (Independent Research) as a part of Group C courses. All courses must be completed for a letter grade, except the DSC 299 units which are taken only on a Satisfactory/Unsatisfactory basis. The student will perform thesis research under the guidance of a thesis adviser and a thesis committee consisting of at least three members. It is required that at least two members of the committee are members of the HDSI faculty council and one of the three committee members can be an industry fellow with an adjunct appointment or a faculty member drawn from another department or division. The chair of the committee shall be approved by the MS program committee. Alternatively, an HDSI industry fellow may be requested to serve as the fourth member of the committee. The committee must be approved by the Graduate Division by the end of the third quarter in the MS program. Students opting for Plan I are required to file an approved thesis to satisfy requirements for completion of the program.

● Plan II: Course-Based Comprehensive Examination Option

Under this plan, the student must complete a practical course-based comprehensive examination designed to evaluate the student’s ability to integrate knowledge and understanding. In this format of the comprehensive examination, the students must answer comprehensive questions in their chosen domain in each of the three selected courses . The comprehensive examination is integrated into the host courses, and in most cases, the associated work serves dual purposes, contributing independently to the student’s course grade and comprehensive examination score.

The comprehensive examination typically consists of a specific class assignment or examination, or a portion thereof, that has been explicitly approved by the MS program committee. Determination of the outcome on the comprehensive exam is separate from the grade in the host course. The students are required to successfully pass the comprehensive examination in three courses drawn from each of the three areas: computing , math/statistics , systems .

Students are permitted up to five attempts, that is, five different courses. No more than three course-hosted comprehensive examinations can be taken in a single quarter, and no comprehensive examination can be repeated in a single quarter. The courses marked for comprehensive examination can be taken only for a letter grade. Course-hosted examinations are registered at the beginning of each quarter and students must register in advance by the specified deadline for the examination. The examination is supervised by a faculty committee responsible for the content, evaluation, and administration of the examination which is separate from the course instructor who is responsible for the course grade but not success in the comprehensive examination.

For more and the latest information regarding the comprehensive examination, please check the HDSI website under graduate programs .

In order for the program to respond, a student requiring accommodation for a disability must make a request for accommodation upon submission of the student’s intent to apply to the graduate program.

Information concerning accommodation requests is available at: http://disabilities.ucsd.edu/ . Distance learning sites must confirm their ability to support students with disabilities.

Master of Data Science Online (MDS) (pending WSCUC approval)

The Halıcıoğlu Data Science Institute (HDSI), in cooperation with the Department of Computer Science and Engineering (CSE), offers a master’s degree in data science to working professionals who are seeking to expand their skill set in data science. MDS is a formally recognized degree (and pending approval by the Western Association of Schools and Colleges, WSCUC) by the University of California that is delivered in a fully online learning format.

The MDS program combines concepts from statistics, computer science, and applications where data is at the forefront. The goal of the MDS program is to teach students the skills required to be successful at performing data-driven tasks. This includes the ability to: (1) collect raw data from various sources and convert this raw data into a curated form amenable to algorithmic analysis, (2) understand machine learning algorithms and how to run them on large data sets, (3) interpret the results of these algorithms, iteratively drill down into the data, and perform more analysis to answer questions about the data.

Admissions requirements are as follows:

Bachelor’s degree with an undergraduate GPA of at least 3.0; preferably in a field of study that provides a strong mathematical background, such as: computer science, mathematics, engineering, quantitative social sciences, computational life sciences, and computational health sciences.
Students whose undergraduate degree is in a nontechnical or nonquantitative field may have the opportunity to pursue a four-course sequence as part of a MicroMasters program which includes the foundational courses in the MDS curriculum. Satisfactory performance in the MicroMasters program can then be considered alongside other admissions criteria.
Two (2) years prior work experience or current employment in a data science related role.
Three (3) letters of recommendation, one (1) of which is recommended to be from the applicant’s current employer.
TOEFL or TSE (international applicants only).

Course requirements are broken down into three categories: foundation, core, and elective. The program also includes a capstone requirement. The course requirements are intended to ensure that students are exposed to (1) fundamental concepts and tools (foundation), (2) advanced, up-to-date views in topics central to data science (core), and (3) a deep, current view of areas for the application of data science tools and techniques (elective). Courses may not fulfill more than one requirement.

The master of data science program is structured as a total of ten four-unit courses inclusive of the final capstone project course.

Foundations (take all three courses, twelve units total)

The foundation courses provide critical foundational knowledge and skills needed in the remainder of the program.

DSC 207R. Python for Data Science
DSC 215R. Probability and Statistics in Data Science
DSC 255R. Machine Learning Fundamentals

Core (take all three courses, twelve units total)

The core courses build upon foundational courses and cover the central topics of the program.

DSC 208R. Data Management for Analytics
DSC 232R. Big Data Analytics Using Spark
DSC 256R. Data Mining on the Web

Electives (choose any three courses, twelve units total)

Students will be able to customize their experience in the program by taking three elective courses.

DSC 209R. Data Visualization
DSC 246R. Introduction to Information-Theoretic Data Processing
DSC 257R. Advanced Unsupervised Learning
DSC 258R. Natural Language Processing
DSC 259R. Practice and Applications
DSC 266R. Human-Centered AI
DSC 267R. Data Fairness and Ethics

Capstone (one course)

DSC 298R. Capstone Project in Data Science . This course consists of a quarter-long project which requires application of the data science knowledge and skills acquired through the MDS curriculum. Students will pick one project out of several available options, each project from a different application domain. Projects are individually completed and graded based on a ten-step process translated into executable notebook-based reports throughout.

As a fully online program, collaboration with the instructional designers in the Teaching and Learning Commons ensures that online courses in the MDS program meet the electronic accessibility standards established by the UC Office of the President. Such considerations include:

All videos will have captions.
All videos will be accessible for screen readers for students who are visually impaired.
For students who need additional accommodation, voice navigation and voice dictation will be available upon request.
Care has been taken to avoid using colors to signify or promote particular actions, in order to accommodate students with color blindness.
All online materials will have the ability to have the font sizes increased.
Course text (pdfs, other documents) will also be accessible.

In order for the program to respond, a student requiring accommodation for disability must make a request for accommodation upon submission of the student’s intent to apply to the graduate program.

Information concerning accommodation requests is available at: http://disabilities.ucsd.edu/ .

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UC San Diego to Launch New Master’s Program in Data Science and Engineering
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COMMENTS

MS & PhD Admissions
In order to provide the tools and resources needed to achieve academic, professional, and personal development goals, the Data Science program has a committed staff available to support students and their needs. If you have any questions or concerns regarding admission, please reach out to our team via [email protected].
PhD Program
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Data Science
Admission to the residential degree programs in data science is done through the Graduate Division at UC San Diego. The application deadline is December for admissions effective the following fall quarter. ... A PhD degree in data science is an advanced degree that prepares students for leadership in data science research in academia, industry ...
Halıcıoğlu Data Science Institute
Graduate. MS & PhD Admissions; MS Program. MSDS Course Requirements; Degree Questions; PhD Program. PhD Students; ... HALICIOĞLU DATA SCIENCE INSTITUTE TUESDAY, AUGUST 27TH FROM 3:00-4:00PM REGISTRATION ... UC San Diego 9500 Gilman Dr. La Jolla, CA 92093 (858) 534-2230 ...
PhD Course Requirements
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MS Program
Welcome to the HDSI MSDS Graduate Program! ... and lay the foundation for future researchers who can expand the boundaries of knowledge in Data Science itself. To meet its goals, the Master of Science in Data Science ... UC San Diego 9500 Gilman Dr. La Jolla, CA 92093 (858) 534-2230
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Contact Us. Give us a call or drop by anytime, we endeavor to answer all inquiries within 24 hours. Find us. PO Box 16122 Collins Street West Victoria, Australia
UC San Diego Launches New Data Science Graduate Degree Programs
Article Content. UC San Diego has launched new master's and doctoral degree programs at the Halıcıoğlu Data Science Institute (HDSI), the university's hub for all things data science.The new programs join UC San Diego's popular undergraduate data science programs for an integrated slate of courses and degrees targeting individuals at all educational levels, including working ...
Graduate Admissions FAQ
The UCSD Application for Graduate Admission opens for Fall 2025 from September 4th, 2023 until December 18th, 2024 at 11:59 p.m. PST. Admission is given for Fall quarter only. We do not admit new students in Spring quarters. All application materials must be submitted through the online application.
Admissions
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UCSD CSE Graduate Admissions
The UCSD Application for Graduate Admission opens for Fall 2025 admission from September 4, 2024 until December 18, 2024 at 11:59 p.m. PST. Admission is given for Fall quarter only. We do not admit new students in Spring quarters. All application materials must be submitted through the online application. The GRE is not required for PhD and MS ...
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These surveys provide valuable information that can be used to improve the graduate student experience at UC San Diego. To learn more the surveys administered, click the link below. View Survey Information. All Graduate Student Data including: Time to Degree, Admissions, Enrollment, and placement.
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Courses in the Data Science & Engineering program are taught by world class faculty from the Computer Science & Engineering Department and the San Diego Supercomputer Center. Courses and descriptions can be viewed on the Course Catalog. This program is considered part-time as enrollment each quarter is 8 units or less. 2024-2025 Academic Calendar
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Email: [email protected]. Phone: (858) 534-3213. Thank you for your interest in UC San Diego's Electrical and Computer Engineering graduate programs! ECE has long been a powerhouse at UC San Diego with its innovative and impactful research led by top-notch faculty and outstanding graduate and undergraduate students.
Selective Major Application
2023-2024 Selective Major Application Timeline. SPRING 2024: Open to Students to Apply: Wednesday, March 27. Deadline to Apply or Make Changes: Sunday, April 7. Offers of Admission Sent: Monday, April 15 - Friday, April 19. Deadline to Accept/Deny: Friday, May 3.
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Program Requirements for the Specialization in Statistics. The specialization requires completion of 72 units before advancement to Ph.D. candidacy. Full-time students are required to register for a minimum of twelve (12) units every quarter, eight (8) of which must be graduate-level mathematics courses taken for a letter grade only.
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Data Science
Admission to the residential degree programs in data science is done through the Graduate Division at UC San Diego. The application deadline is December for admissions effective the following fall quarter. ... A PhD degree in data science is an advanced degree that prepares students for leadership in data science research in academia, industry ...

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