Statistics, PhD
Contact Info
| Contact: | José E. Figueroa-López |
| Email: | sdsphddirector@wustl.edu |
| Website: | https://sds.wustl.edu/ |
Doctoral Candidacy
To earn a PhD at Washington University, a student must complete all courses required by their department; maintain satisfactory academic progress; pass certain examinations; fulfill residence and Mentored Experience Requirements; write, defend, and submit a dissertation; and apply to graduate via Workday Student. For the details of doctoral degree general requirements in Arts & Sciences, including an explanation of Satisfactory Academic Progress, students should review the Doctoral Degree Academic Information page of the Arts & Sciences Bulletin.
Program Requirements
- Total Units Required: 45 (Note: Remission applies for a maximum of 72 graduate-level units.)
- Degree Length: 5 years
- Note: Students must be enrolled in 9 graduate credits each semester to retain full-time status. As students complete their coursework, if enrolled in fewer than 9 graduate credits, they must enroll in a specific Arts & Sciences graduate course that will show 0 units but does count as full-time status. Students should connect with their department to ensure proper enrollment prior to Add/Drop.
- Grade Requirement: A GPA of B (3.0) or better is required in graduate coursework.
PhD in Statistics
Degree requirements summary:
A total of 45 graduate units are required, consisting of the following:
- 18 required coursework units in fundamental topics
- 18 elective coursework units
- Two qualifying exams in statistics
- 1.5 coursework units of the Teaching Seminar Course
- 1.5 coursework units of the Professional Development Course
- 6 coursework units of Statistics and Data Science Seminars
- Teaching Requirement for PhD Students from the Office of Graduate Studies, Arts & Sciences
- Oral presentation
- Dissertation research, thesis preparation, and defense
General requirements: Completion of the PhD requires four to five years of graduate study. The student must spend at least one academic year as a full-time student; this requirement cannot be met wholly by summer sessions or part-time study. The student may, with departmental permission, transfer a maximum of 15 graduate credits from other universities. The typical course load is 9 credit units per semester. A GPA of B (3.0) or better is required in graduate coursework.
Teaching experience is an increasingly important component of graduate education for students who seek academic employment. The PhD in Statistics program provides the opportunity for students to work as Assistants in Instruction and to learn how to teach technical topics to students with a wide range of backgrounds.
For the well-prepared student, "normal progress" usually means the following:
- At the end of the first year, the student should have taken qualifying exams in any two of the three sequences in fundamental topics (see "Specific Course Requirements" below for details).
- At the end of the second year, the student has successfully completed the three sequences of fundamental topics.
- At the end of the third year, the student has completed the candidacy requirement.
- At the end of the fourth year, the student has completed the 45-unit course requirement and is making substantial progress on a thesis.
Students must also complete the Teaching Seminar Course (SDS 5501), which prepares them for both Assistant in Instruction work and academic teaching duties, which are integral to all scholarly activities. For a typical PhD student, the course is taken in the spring of the first year. Each student will have departmental duties (e.g., grading, proctoring) of no more than 15 hours per week as Assistants in Instruction. Students must also complete a Professional Development Course (SDS 5502). PhD students must also register for SDS 5503 (Statistics and Data Science Seminar) for six semesters during their PhD. This is a 1-credit-hour course for a total of 6 credit hours.
Please note that the sequence outlined above is for "well-prepared" students. The exact point at which any student enters the sequence depends on their ability and background. When warranted, deviation from the normal sequence is permissible, and a tailored program that fits the student's ability and background will be followed.
Specific course requirements:
Students will have to complete the following three sequences in fundamental topics for a total of 18 required coursework units:
Pick one of the following two course pairs:
| Code | Title | Units |
|---|---|---|
| SDS 5061 | Theory of Statistics I | 6 |
and SDS 5062 | Theory of Statistics II | |
| SDS 5010 | Probability | 6 |
and SDS 5020 | Mathematical Statistics |
In addition to completing one of the two course pairings above, students must also complete the following two sequences:
| Code | Title | Units |
|---|---|---|
| SDS 5071 | Advanced Linear Models I | 6 |
and SDS 5072 | Advanced Linear Models II | |
| SDS 5531 | Advanced Statistical Computing I | 6 |
and SDS 5532 | Advanced Statistical Computing II |
In exceptional circumstances, departmental permission may be requested to replace one of these sequences with a suitable alternative. The student may also petition the department to waive one or more of these sequences because of work completed previously.
Prerequisites, if needed, are advanced undergraduate courses in linear algebra and real analysis. Such courses would count as 0 credits toward the PhD degree if taken during the PhD.
At the end of the first year, the PhD student should have taken the qualifying exams in any two of the above three sequences. In each of the qualifying exams taken, students can obtain either a pass, a conditional pass, or a no pass. These denominations will be determined by the instructor, the DGS, and the graduate committee. "Pass" reflects successful performance in both the course sequence and the additional exam related to the sequence. "Conditional pass" indicates that the student has not performed satisfactorily in either 1) a portion of the course sequence or 2) the exam related to the course sequence. A conditional pass might result, for example, from a B or higher grade for the sequence but underperformance on the related additional exam. "No Pass" will be assigned to a student who underperforms in both the sequence (two courses) as well as on the additional related exam, effectively constituting extreme underperformance. See "Qualifying examinations and candidacy requirements" below for more information about qualifying examinations.
Students must maintain a 3.0 or better grade average in qual courses. This is separate from the general graduate school requirement of a 3.0 GPA in all graduate courses. Conceivably, a student could receive a low grade in a qual course and as long as they pass an exam given later and keep their average qual grade point average at or above 3.0, then they would be satisfying the requirements.
PhD students are also required to complete SDS 5501 Teaching Seminar and SDS 5502 Professional Development. Each of these two courses is 1.5 credit hours. PhD students must also register for SDS 5503 Statistics and Data Science Seminar for six semesters during their PhD. This is a 1-credit-hour course for a total of 6 credit hours. SDS 5501 is typically taken in the spring semester of the student’s first year. SDS 5502 is typically taken in the fall semester of the student’s second year. Departmental permission may be requested to replace one of these sequences with a suitable alternative. In exceptional circumstances, departmental permission may be requested to postpone or waive one or more of these courses.
Prior to finding a research advisor, students are welcome to take any of the Department of Statistics and Data Science 5000-level statistics electives, and they may also take reading courses with statistics faculty members (SDS 5000/SDS 5900 Research). Statistics electives offered by the department include the following:
| Code | Title | Units |
|---|---|---|
| SDS 5070 | Stochastic Processes | 3 |
| SDS 5120 | Survival Analysis | 3 |
| SDS 5155 | Time Series Analysis | 3 |
| SDS 5210 | Statistical Computation | 3 |
| SDS 5310 | Bayesian Statistics | 3 |
| SDS 5430 | Multivariate Statistical Analysis | 3 |
| SDS 5440 | Mathematical Foundations of Big Data | 3 |
| SDS 5480 | Topics in Statistics | 3 |
| SDS 5531 | Advanced Statistical Computing I | 3 |
| SDS 5532 | Advanced Statistical Computing II | 3 |
| SDS 5595 | Topics in Statistics:Spatial Statistics | 3 |
| SDS 5800 | Topics in Statistics | 3 |
Once a PhD student reaches the minimum of 45 units, they can directly choose to register for ASGS 9000. Students can take a maximum of 72 credit hours without incurring tuition costs. In other words, once students are past their foundational coursework and have met the 45-credit hour requirement, they can still enroll in advanced topics courses or independent study courses in consultation with and approval of the student's research advisor. These courses are important for broadening and deepening the base knowledge of our graduate students. A PhD in statistics is not simply a technical degree and these courses allow for students to learn about areas not directly related to their dissertation research as well as deepen their knowledge in their area of research.
Elective courses taken in other departments allow students to supplement their statistics coursework with other topics that may be helpful for their research and professional development. Some popular elective courses offered by other departments include the following:
| Code | Title | Units |
|---|---|---|
| CSE 5101 | Introduction to Artificial Intelligence | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5109 | Advanced Machine Learning | 3 |
| CSE 5401 | Advanced Algorithms | 3 |
| ECON 8151 | Advanced Theoretical Econometrics | 3 |
| ESE 4050 | Reliability and Quality Control | 3 |
| ESE 4150 | Optimization | 3 |
| ESE 5200 | Probability and Stochastic Processes | 3 |
| ESE 5230 | Information Theory | 3 |
| MATH 5056 | Topics in Financial Mathematics | 3 |
| MATH 5151 | Measure Theory and Functional Analysis | 3 |
| MATH 5152 | Measure Theory and Functional Analysis II | 3 |
| MATH 5160 | Complex Variables | 3 |
| MATH 5501 | Numerical Applied Mathematics | 3 |
| PHS 5140 | Randomized Controlled Trials | 3 |
Language requirement: All students must demonstrate proficiency in English.
If English is not the student's native language, they must pass an oral English proficiency exam with a grade of 3 or better. If the student does not score a 3 the first time they take the exam, the director of English Language Programs for Arts & Sciences will recommend that the student take one or more classes to improve reading, writing, pronunciation, listening, or speaking skills. After the recommended classes have been completed, the student is required to retake the English proficiency exam. Once the student has demonstrated the ability to handle teaching a class (by scoring a 3 or better on the exam), they will qualify for Assistant in Instruction or Course Instructor duties.
Qualifying examinations and candidacy requirements: The qualifying exam and candidacy requirements constitute two separate requirements. The qualifying exam is a series of two written tests that cover a range of topics; the candidacy requirement is an oral presentation and thesis proposal.
The written tests cover the material in two of the following three sequences:
- Pick one of the following two course pairs:
- SDS 5061 Theory of Statistics I and SDS 5062 Theory of Statistics II
- SDS 5010 Probability and SDS 5020 Mathematical Statistics
- SDS 5071 Advanced Linear Models I and SDS 5072 Advanced Linear Models II
- SDS 5531 Advanced Statistical Computing I and SDS 5532 Advanced Statistical Computing II
Each spring, at the end of the SDS 5020 (or SDS 5062), SDS 5072, and SDS 5532, all students enrolled in these courses take a two-hour final exam; this exam usually covers the second half of the sequence. Doctoral candidates take an additional one-hour exam that covers the two courses of each sequence. To pass the qualifying exam of the sequences, the student must pass the three-hour combined exam.
In each of the qualifying exams taken, students can obtain either a pass, a conditional pass, or a no pass. These denominations will be determined by the instructor, the DGS, and the graduate committee. "Pass" reflects successful performance in both the course sequence and the additional exam related to the sequence. "Conditional pass" indicates that the student has not performed satisfactorily in either 1) a portion of the course sequence or 2) the exam related to the course sequence. A conditional pass might result, for example, from a B or higher grade for the sequence but underperformance on the related additional exam. "No Pass" will be assigned to a student who underperforms in both the sequence (two courses) as well as on the additional related exam, effectively constituting extreme underperformance.
In the case of a conditional pass, the student is placed on probation, per the OGS Policy on Probation and Dismissal for Academic Reasons. The DGS in consultation with the graduate committee will determine what additional work is required for the student to pass the corresponding qualifier and return to good standing. This may include an independent exam or an independent studies course with a faculty related to the subject within one year. Such work must be detailed in writing for the student, and the department will keep this probation letter, along with the outcome, as part of the student's record.
Students who obtain a “no pass” grade in one of the qualifiers, reflecting both underperformance in both courses of the sequence and on the related exam, will be considered to be in extreme academic underperformance and dismissed from the doctoral program, consistent with OGS policy. The department will follow the OGS Policy on Probation and Dismissal for Academic Reasons. If desired, the student may continue to pursue the Statistics MA degree with an additional year of coursework. During this last year, the student will not receive graduate funding and will pay tuition as a regular MA student.
Because each sequence varies somewhat in content from year to year, it is recommended that the student take each set of exams at the conclusion of the sequence in which they are enrolled. No advantage is gained by delaying the exam for a year. It is desirable to make every effort to finish all three exams by the end of the second year of study.
Some students will enter the PhD program with previously acquired expertise in one or more of the three basic sequences. This situation sometimes happens with students who transfer from other PhD programs or who come from certain foreign countries. Such students may formally petition the Director of Graduate Studies to be exempted from the appropriate course and its qualifying exam. The petition must be accompanied by hard evidence (e.g., published research, written testimony from experts, records of equivalent courses, examinations and the grades achieved on them). The graduate committee will make the final judgment on all exemption requests.
Once the written phase of the qualifying process is complete, the student is ready to begin specialized study. By the third year of study, the student must complete the candidacy requirement. The student must form a preliminary thesis committee called a Research Advisory Committee that includes their advisor and at least two other faculty members. In discussion with the advisor and the preliminary thesis committee, the student will select a topic, and a body of literature related to this topic. The student will prepare a one-hour oral presentation related to the topic and a two-page thesis proposal that demonstrates mastery of the selected topic. The oral presentation is designed to expedite specialized study and to provide guidance toward the thesis. The preparatory work for the thesis proposal often becomes the foundation on which the thesis is constructed.
After the student completes the oral presentation, work on the thesis begins.
The dissertation and thesis defense: The student's dissertation is the single most important requirement for the PhD degree; it must be an original contribution to the knowledge of data science, statistics, probability, and/or applied probability and is the student's opportunity to conduct significant independent research.
It is the student's responsibility to find a thesis advisor who is willing to guide their research. Since the advisor should be part of the oral presentation committee, the student should have engaged an advisor by the beginning of their second year of study.
Once the department has accepted the dissertation (on the recommendation of the thesis advisor), the student is required to defend their thesis through a presentation accompanied by a question-and-answer period.
For information about preparing the thesis and its abstract as well as the deadlines involved, including the creation of the Research Advisory Committee and the Dissertation Defense Committee, please consult the Office of Graduate Studies, Arts & Sciences. Please use these additional relevant resources: the Doctoral Dissertation Guide, the Forms page, and the Policies and Procedures page.
Qualifying Examinations
Progress toward the PhD is contingent upon the student passing examinations that are variously called preliminary, qualifying, general, comprehensive, or major field exams. The qualifying process varies according to the program. In some programs, it consists of a series of incremental, sequential, and cumulative exams over a considerable time. In others, the exams are held during a relatively short period of time. Exams may be replaced by one or more papers. The program, which determines the structure and schedule of the required examinations, is responsible for notifying the Office of Graduate Studies, Arts & Sciences, of the student’s outcome, whether successful or unsuccessful.
Mentored Experience Requirements
Doctoral students at Washington University must complete a department-defined Mentored Experience. The Mentored Experience Requirement is a doctoral degree requirement that is notated on the student’s transcript when complete. Each department has an established Mentored Experience Implementation Plan in which the number of units that a student must earn through Mentored Teaching Experience(s) and/or Mentored Professional Experience(s) is defined. The Mentored Experience Implementation Plans outline how doctoral students within the discipline will be mentored to achieve competencies in teaching at basic and advanced levels. Some departments may elect to include Mentored Professional Experiences as an avenue for completing some units of the Mentored Experience Requirement. Doctoral students will enroll in ASGS 8005, 8010, or 8015 Mentored Teaching Experience - Assistant in Instruction; ASGS 8020 Mentored Teaching Experience - Mentored Independent Teaching; or ASGS 8120 Mentored Professional Experience to signify their progression toward completing the overall Mentored Experience Requirement for the degree.
The Doctoral Dissertation
A Research Advisory Committee (RAC) must be created no later than the end of the student's third year; departments may set shorter timelines (e.g., by the end of the student's second year) for this requirement. As evidence of the mastery of a specific field of knowledge and of the capacity for original scholarly work, each candidate must complete a dissertation that is approved by their RAC.
A Title, Scope & Procedure Form for the dissertation must be signed by the committee members and by the program chair. It must be submitted to the Office of Graduate Studies, Arts & Sciences, at least six months before the degree is expected to be conferred or before the beginning of the fifth year of full-time enrollment, whichever is earlier.
A Doctoral Dissertation Guide and a Dissertation Template that give instructions regarding the format of the dissertation are available on the website of the Office of Graduate Studies, Arts & Sciences. Both should be read carefully at every stage of dissertation preparation.
The Office of Graduate Studies, Arts & Sciences, requires each student to make the full text of the dissertation available to the committee members for their review at least one week before the defense. Most degree programs require two or more weeks for the review period; students should check with their faculty.
The Dissertation Defense
Approval of the written dissertation by the Research Advisory Committee (RAC) is strongly recommended before the student can orally defend the dissertation. The Doctoral Dissertation Committee that examines the student during the defense consists of at least five members. Normally, the members of the RAC also serve on the Doctoral Dissertation Committee. The dissertation committee is then additionally augmented to ensure that the following criteria are met:
- Three of the five members (or a similar proportion of a larger committee) must be full-time Washington University in St. Louis faculty members or, for programs involving Washington University in St. Louis-affiliated partners, full-time members of a Washington University in St. Louis-affiliated partner institution. All members must be authorized to supervise PhD students and have appropriate expertise in the proposed field of study. One of these three members must be the PhD student's primary thesis advisor, and one may be a member of the emeritus faculty.
- All other committee members must be active in research/scholarship and have appropriate expertise in the proposed field of study whether at Washington University in St. Louis, at another university, in government, or in industry.
- At least one of the five members must bring expertise outside of the student's field of study to the committee, as judged by the relevant department/program and approved by the Office of Graduate Studies, Arts & Sciences.
The approval processes outlined in the RAC section of the Doctoral Council bylaws also apply to the doctoral dissertation committee, including approval of each dissertation committee by the Office of Graduate Studies, Arts & Sciences.
The student is responsible for making the full text of the dissertation accessible to their committee members for their review in advance of the defense according to program rules. Washington University in St. Louis community members and guests of the student who are interested in the subject of the dissertation are normally welcome to attend all or part of the defense but may ask questions only at the discretion of the committee chair. Although there is some variation among degree programs, the defense ordinarily focuses on the dissertation itself and its relation to the student's field of expertise.
Attendance by a minimum of four members of the Doctoral Dissertation Committee, including the committee chair and an outside member, is required for the defense to take place. This provision is designed to permit the student's defense to proceed in case of a situation that unexpectedly prevents one of the five members from attending. Students should not plan in advance to only have four members in attendance. If four members cannot attend, the defense must be rescheduled. The absence of all outside members or of the committee chair also requires rescheduling the defense.
Students, with the support of their Doctoral Dissertation Committee chair, may opt to hold their dissertation defense in person or by utilizing a virtual or hybrid format.
Submission of the Dissertation
After the defense, the student must submit an electronic copy of the dissertation online to the Office of Graduate Studies, Arts & Sciences. The submission website requires students to choose among publishing and copyrighting services offered by ProQuest’s ETD Administrator. Students are asked to submit the Survey of Earned Doctorates separately. The degree program is responsible for delivering the final approval form, signed by the committee members at the defense and then by the program chair or director, to the Office of Graduate Studies, Arts & Sciences. Students who defend their dissertations successfully have not yet completed their PhD requirements; they finish earning their degree only when their electronic dissertation submission has been accepted by the Office of Graduate Studies, Arts & Sciences.
Master's Degree Along the Way/
In Lieu of a PhD
Students typically spend their first two years (four semesters) taking graduate courses. At the end of this time, they will have completed requirements for the master's degree.
As part of their degree requirements, PhD students must complete a program-defined Mentored Experience Requirement (MER) as per these guidelines. The Mentored Experience Implementation Plan (MEIP) is the written articulation of a program-defined degree requirement for PhD students to engage in mentored teaching activities and/or mentored professional activities, collectively referred to as MERs.
Mentored Experience Requirements (MERs)
Philosophy of Teaching
Statistics and Data Science (SDS) are interdisciplinary subjects in the interplay of mathematics, computer science, and even philosophy. Effective instruction of these disciplines is essential for the professional development of students regardless of whether they choose to go to academia or industry upon completion of the PhD program. Teaching is widely recognized not only as an effective method to develop communication and organizational skills but also as a practice conducive to deeper understanding of concepts, tools, and theories and eventually the creation of breakthroughs and new knowledge. Furthermore, SDS plays a fundamental role in many other disciplines that analyze data to infer valid conclusions and, hence, produce better decision making.
Our program wants PhD students to receive adequate preparation in teaching and instructional methodology so that they will be effective in the classroom at WashU as well as be able to start their new positions with success upon completion of their PhD.
Preparatory Engagement
Preparatory Engagement activities are those that represent an introduction to the foundational skills associated with teaching or communication. Pedagogical preparation engagement activities are normally completed before students are permitted to engage in assisting or teaching in a classroom.
SDS requires two preparatory engagements:
- Students must complete one 1.5-credit semester course during the spring of their first year: SDS 5501 Teaching Seminar. This course is taught by exceptionally experienced and talented teaching faculty in the department from a detailed syllabus of topics. Topics covered during the course include group discussion of issues and methods in teaching and learning, creating classroom activities, giving teaching demonstrations, and writing teaching statements. Students will learn that teaching others provides both excellent communication skills and a deep understanding of statistical thinking. Direct mentoring is provided to the students when serving in the role of Assistant in Instruction (AI).
- Students must register and attend the SDS seminar series: SDS 5503 Statistics and Data Science Seminar. Students learn more effective communication and presentation skills by observing the presentations of experienced researchers and scientist.
Campus resources such as the Center for Teaching and Learning (CTL) are utilized for additional information about effective teaching methodology.
Mentored Teaching Experiences (MTEs)
Assistant in Instruction (AI)
An Assistant in Instruction (AI) is a PhD student who is directly engaged in the organization, instruction, and/or support of a semester-long course primarily taught by a faculty member. An AI receives mentorship from a faculty member related to best practices in classroom engagement, instruction in the field, interpersonal engagement, and other relevant skills. Students and mentors complete a mentorship plan prior to the start of each AI experience. To complete each AI assignment and to ensure that it applies toward their degree requirements, students must register for the appropriate course number for each semester of engagement. Refer to the "Required Pathways for Completion" section below for course numbers and details.
SDS provides a planned sequence of teaching opportunities. During the students' second year of studies, they begin service in the SDS help room mentoring undergraduate students with questions in their introductory statistics classes. They may also take on a more active teaching opportunity by serving as an AI for those introductory statistics courses. PhD students are assigned two to four sections of introductory statistics or data science courses to assist with when serving as an AI. This involves preparing jointly with the instructor weekly recitations or labs and leading one of such 50-minute recitations per week. Other PhD students may be assigned to AI in the capacity of graders for more advanced courses. Students are mentored on how to assess valid arguments or conclusions from fundamental axioms and principles.
SDS PhD students will be required to observe faculty members’ classes and write a report and give short teaching presentations of their own. Student presentations are recorded so they can be critiqued, and further comments can be provided.
Mentored Independent Teaching (MIT)
MIT is a semester-long experience for PhD students who engage as the primary instructor or co-instructor of a course under the mentorship of a faculty member as part of the MER. Students and mentors complete a mentorship plan prior to the start of each MIT experience. To complete each MIT assignment and to ensure that it applies toward their degree requirements, students must register for the appropriate course number (ASGS 8020) for each semester of engagement. Refer to the "Required Pathways for Completion" section below for more details.
SDS does not require MIT, although opportunities may be available to engage in MIT to obtain 20 to 60 MER units. For graduate students further along and better prepared, as judged by the Graduate Committee and the Teaching Center representative, they may be assigned an MIT. Feedback on teaching and instruction is provided by the instructor of the course through observations of the graduate student and through meetings for the course.
In order to qualify to engage in MIT, students must have completed at least 20 MER units as an AI.
Required Pathways for Completion
Students work with their faculty mentor and their Director of Graduate Studies to plan how and when they will complete their MERs. Students register during the normal registration period for courses in accordance with one of these approved pathways.
Several different pathways are possible to complete the MER. The possible pathways are listed below. When a student is an AI, they register for ASGS 8005, ASGS 8010, or ASGS 8015 given their role and number of hours per week required (5, 10, or 15) during the semester of engagement. MITs register for ASGS 8020 and are expected to spend 20 hours per week during the assignment.
Pathway #1
| ASGS 8005 | Take two times |
| ASGS 8010 | Take three times |
| ASGS 8015 | Take two times |
Pathway #2
| ASGS 8010 | Take two times |
| ASGS 8015 | Take two times |
Pathway #3
| ASGS 8005 | Take one time |
| ASGS 8010 | Take two times |
| ASGS 8015 | Take three times |
| ASGS 8020 | Take one time |
Pathway #4
| ASGS 8010 | Take three times |
| ASGS 8015 | Take two times |
| ASGS 8020 | Take one time |
Pathway #5
| ASGS 8010 | Take six times |
| ASGS 8020 | Take two times |
Pathway #6
| ASGS 8005 | Take six times |
| ASGS 8020 | Take three times |
Pathway #7
| ASGS 8005 | Take four times |
| ASGS 8010 | Take one time |
| ASGS 8020 | Take three times |
Optional Activity: Teaching Intensive Pathway (TIP)
The TIP is an optional pathway for those students whose career interests lie in academia or another field that would benefit from extended teaching experiences. This immersive experience allows students to further explore the breadth and depth of teaching best practices and pedagogy related to their respective field. Students who are interested in participating in this elective experience must formally request to participate, which is subject to program approval. Due to this experience being an elective, unpaid experience, students who participate in the TIP will not receive compensation.
SDS allows a TIP for 20 total MER units (two AIs at 10 MER units each, one engagement per semester).
Optional Pathway
| ASGS 8010 | Take two times |