Halıcıoğlu Data Science Institute

Data Science (MS) (DS75)

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. Industries 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 Master of Science in Data Science program offers a combination of core knowledge in information processing coupled with the skills to abstract, build and test predictive and descriptive models in the context of an application domain. Students gain the 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.

Program Info

Area of Study Data Science
Program Overview The Master of Science in Data Science (MS-DS) program is structured as a total of twelve (12) 4-unit courses grouped into foundational, core and specialization areas. 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 48 units, at least 40 units must be using graduate-level courses. In addition, 2 out of 10 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 advisor.

Group A: Introductory courses: maximum of four courses (16 units) credit
These courses provide five critical foundational knowledge and skills 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: 3 required courses, maximum of 6 courses (24 units) credit
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 Ph.D. student can take an advanced course on Applied Statistics, such as MATH 282B instead of DSC 241. Similarly, instead of DSC204A, 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 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 advisor.
As a matter of guidance, students can choose from the following general electives or by selecting three electives in a given 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 287A-B, COGS 243.
Specializations

Specialization Areas: minimum of 3 courses required
Upon prior approval from a graduate advisor, students can sign up for an available specialization area for an “Master of Science in Data Science with specialization in specialization-area” degree. A specialization requires a minimum of three courses in a specialization area.

Specialization: Bioengineering
BENG 218, BENG 203, BENG 211, BENG 213, 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
COGS 260, BGGN 246, BGGN 260, COGS 260 (or NEU 282), COGS 280

Specialization: Networks
MATH 261A, MATH 277A, MATH 289A, MATH 289B, 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.

Department Halıcıoğlu Data Science Institute
Major Data Science (MS)
Degree Master of Science
Admissions Terms Fall
Application Deadlines  

Basic Requirement

Admissions Tests

GRE General is recommended.

English Proficiency Exams

(international applicants only)

A test of English language proficiency is required for international applicants whose native language is not English and who have not studied full-time for one uninterrupted academic year at a university-level institution in which English is the language of instruction and in a country where English is a dominant language.

The following test(s) are accepted by this department:

TOEFL (Test of English as a Foreign Language)
IELTS (International English Language Testing System)

Letters of Recommendation

Minimum of 3 recommendations required.

Three (3) letters of recommendation are required. Additional letters may be submitted.

Statement of Purpose

Required

Resume/CV

Required

Contact Info

Website https://datascience.ucsd.edu/academics/graduate/admissions/
Email DSCgradinfo@ucsd.edu
Campus Office Halıcıoğlu Data Science Institute
University of California San Diego
10100 Hopkins Drive
La Jolla, CA 92093
Mailing Address
Halıcıoğlu Data Science Institute
University of California San Diego
9500 Gilman Drive MC 0555
La Jolla, CA 92093

Data Science (PhD) (DS76)

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. Industries 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 PhD in Data Science degree aims to provide a research-oriented education; teaching knowledge, skills and awareness required to perform data-driven research, and enabling students to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. This doctoral program spans foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Program Info

Area of Study Data Science
Program Overview The doctoral program is structured as a total of 52 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 52 units, 48 units (or 12 courses) must be taken for letter grade and at least 40 units must be graduate-level courses.
The remaining 4 units are for professional preparation, consisting of 1 unit of faculty research seminar, 2 units of TA/tutor training and 1 unit of survival skills course taken for a passing (satisfactory) grade.

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 can not be substituted by other courses without exception approval from the graduate program committee.

Group A: Preparatory Courses
There are five important knowledge and skills necessary for understanding (and advancing) core data science. It is, therefore, important that all our entering students either have background preparation or have courses available in the program to ensure a successful completion of the stipulated doctoral degree program. A student can receive credit towards the Ph.D. 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 Ph.D. students, including those with a Bachelors 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 Ph.D. student can take an advanced course on Applied Statistics, such as MATH 282B instead of DSC 241. Similarly, instead of DSC204A, 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 2 out of the following 8 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 6 courses for letter-grade credit from Group B courses. Students can take more than 6 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 into individual research (DSC 298) in a section offered by the faculty advisor 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: 2 unit TA/tutor training (DSC 599), 1 unit of academic survival skills (DSC 295) and 1 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 (10 weeks) obtained by registering for DSC 294 with an instructor. All Ph.D. students will participate in a minimum of 2 research rotations during their first year, and with a minimum of two different faculty members, and as much 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 advisor. Those who fail to identify a research advisor 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.

Research Qualifying Examination (UQE)
A research qualifying examination (UQE) is conducted by the dissertation committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% 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 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

Dissertation Defense Examination and Thesis requirements
Students must successfully complete a final dissertation defense presentation and examination to the doctoral committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). 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% 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(D) 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, Ph.D. students are also required to take a 1-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.
Department Halıcıoğlu Data Science Institute
Major Data Science (PhD)
Degree Doctor of Philosophy
Admissions Terms Fall
Application Deadlines  

Basic Requirement

Admissions Tests

GRE General is recommended.

English Proficiency Exams

(international applicants only)

A test of English language proficiency is required for international applicants whose native language is not English and who have not studied full-time for one uninterrupted academic year at a university-level institution in which English is the language of instruction and in a country where English is a dominant language.

The following test(s) are accepted by this department:

TOEFL (Test of English as a Foreign Language)
IELTS (International English Language Testing System)

Letters of Recommendation

Minimum of 3 recommendations required.

Three (3) letters of recommendation are required. Additional letters may be submitted.

Statement of Purpose

Required

Resume/CV

Required

Contact Info

Website https://datascience.ucsd.edu/academics/graduate/admissions/
Email DSCgradinfo@ucsd.edu
Campus Office Halıcıoğlu Data Science Institute
University of California San Diego
10100 Hopkins Drive
La Jolla, CA 92093
Mailing Address
Halıcıoğlu Data Science Institute
University of California San Diego
9500 Gilman Drive MC 0555
La Jolla, CA 92093

Departments