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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:

  1. A residential degree program in master’s of science in data science, MS-DS
  2. A residential degree program in doctor of philosophy in data science, PhD-DS
  3. An online degree program in master’s of data science, MDS (pending WSCUC approval)

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 annually. 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 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, or 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.
  • Evidence of proficiency for international students: three English proficiency examinations are accepted for graduate study at UC San Diego:
    • 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, to ensure a successful and timely completion of the graduate degree program does require 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-level 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-level 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:

  1. DSC 200. Data Science Programming
  2. DSC 202. Data Management for Data Science
  3. DSC 210. Numerical Linear Algebra
  4. DSC 211. Introduction to Optimization
  5. 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:

  1. DSC 240. Machine Learning
  2. DSC 260. Data Ethics and Fairness
  3. (*)DSC 241. Statistical Models (or MATH 282B)
  4. (*)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:

  1. DSC 203. Data Visualization and Scalable Visual Analytics
  2. DSC 204B. Big Data Analytics and Applications
  3. DSC 242. High-dimensional Probability and Statistics
  4. DSC 243. Advanced Optimization
  5. DSC 244. Large-Scale Statistical Analysis
  6. DSC 245. Introduction to Causal Inference
  7. DSC 250. Advanced Data Mining
  8. 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

  1. DSC 599. TA/Tutor Training
  2. DSC 293. Faculty Research Seminar
  3. DSC 294. Research Rotation
  4. 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 in data science 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 in data science 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 coadviser 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’s of Science in Data Science (MS-DS) Degree Program

While the masters 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 MS 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’s 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’s 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.

Admission into the Program

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
  • Evidence of proficiency for international students: three English proficiency examinations are accepted for graduate study at UC San Diego:
    • 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 and Course Planning for Students Entering the MS-DS 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, to ensure a successful and timely completion of the graduate degree program does require 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 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.

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.

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.

Course Requirements

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’s of science in the data science 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 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.

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 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 298 (Independent Research) as a part of Group C courses. All courses must be completed for a letter grade, except the DSC 298 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.

Student with Disabilities

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/students/obtainaccommodations.html. Distance learning sites must confirm their ability to support students with disabilities.

Master’s of Data Science Online Program (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

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

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’s 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.

  • MDS 200R. Python for Data Science
  • MDS 210R. Probability and Statistics in Data Science
  • MDS 220R. 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.

  • MDS 230R. Big Data Analytics Using Spark
  • MDS 240R. Data Mining on the Web
  • MDS 250R. Data Management for Analytics

Electives (choose any three courses, twelve units total)

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

  • MDS 260R. Advanced Unsupervised Learning
  • MDS 261R. Natural Language Processing
  • MDS 262R. Data Visualization
  • MDS 263R. Data Preprocessing
  • MDS 264R. Interaction Design
  • MDS 265R. Introduction to Information-Theoretic Data Processing
  • MDS 266R. Human-Centered AI
  • MDS 267R. Data Fairness and Ethics
  • MDS 268R. Practice and Applications

Capstone (one course)

MDS 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.

Student with Disabilities

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 UCOP. 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/students/obtainaccommodations.html.