The Department of Statistics offers a fee-based Master of Science (MS) Track "Statistics - Advanced Methods and Data Analysis." The track is intended for full-time international and domestic students who wish to obtain a rigorous training in Statistics. Most students will complete the Masters degree in the Winter of their second year.
The goal of the new Master of Science track is to offer students the ability to focus on methodology, take an applied statistics capstone class in the winter of the second year and complete the MS in the winter of the second year, as a cohort. There is also the option for students to specialize in areas of emphasis (such as genetics. For more information, see the Specialized Course Sequences section below).
The admissions criteria for this track will be a strong mathematical background and a statement of purpose that indicates a desire to obtain a strong methodological statistics training in a full-time program. Specifically, students should have a background in mathematics, statistics, or a quantitative field, with 30 or more quarter credits in mathematics and statistics, to include a year of advanced (second-year) calculus, one course in linear algebra, and one course in probability theory; Graduate Record Examination scores (the Advanced Mathematics subject test is strongly encouraged but not required); a statement of purpose; and three letters of recommendation from appropriate former or current faculty.
The minimum requirements for the new MS track are a minimum of 49 credits including the required sequence of courses described below. Students may enroll in additional elective courses.
Graduation Requirements
- Theory: Pass the first year MS Theory Exam. If this exam is failed, it must be passed in the following year.
- Applied: Pass the Applied Statistics Capstone class.
- Satisfactory progress in approved courses and electives with a total of at least 49 credits.
- Seminar: Attendance in at least one quarter of the statistics seminar.
Course Requirements and Sequence
In the table below the subscripts are the number of credits. The course titles are:
- 502 Design and Analysis of Experiments
- 504 Applied Regression
- 512 Statistical Inference
- 513 Statistical Inference
- 528 Capstone Data Analysis
- 534 Statistical Computing
- 536 Analysis of Categorical and Count Data
- 570 Advanced Regression Methods for Independent Data
- 571 Advanced Regression Methods for Dependent Data
Required Courses:
First Year
FALL | WINTER | SPRING | |
---|---|---|---|
502_{4} | 504_{4} | 534_{3} | |
512_{4} | 513_{4} | Theory Exam |
Second Year
FALL | WINTER | SPRING |
570_{3} | 571_{3} | |
536_{3} | 528_{3} | |
Graduating Quarter |
- In the first year, there are 5 required classes, accounting for 19 credits.
- In the second year, there are 4 required classes, accounting for 12 credits.
- Enrollment in Electives typically begins in Spring quarter of the first year.
- A small number of STAT 600 credits may be taken. For example, in the autumn and winter of the first year, 2 credits of STAT 600 are typically taken. Mostly, these STAT 600 obligations are satisfied by student attendance at one or two of the various departmental or working group seminars. At least one such Stat seminar is required by this program prior to graduation. Other than seminar, STAT 600 credits can be earned through research or consulting. Up to 2 credits of research per quarter are allowed, up to a maximum of 6 credits over the course of the degree. These research opportunities should be discovered through student initiative. We also allow 1 or 2 credits of consulting per quarter, up to a maximum 4 credits over the course of the degree. The director of consulting can be approached to see if projects are available. Note that these consulting credits are distinct from the STAT 599 consulting course, which does not form a part of this program.
- The above requirement gives a total of 32 (that is, 19+12+1) credits so that the student will need to take 17 further credits. Some of these 17 credits are the STAT 600 credits mentioned above. Obvious possible electives are listed below.
- International students will typically take classes for 4 quarters, with J1 and F1 visa requirements requiring a minimum of 10 credits per quarters, and in the Winter of their second year (the graduating quarter) will typically register for 9 credits.
- The degree will be Master of Science: Advanced Methods and Data Analysis with the possibility for the students to take a particular sequence of courses (see the Specialized Course Sequences section below) with a "Letter of Recognition," which names and describes the group of courses taken.
- Each Required Course and each graded Elective used in any context toward the 49 required credits for the degree requires a minimum grade of 2.7. Moreover, the cumulative GPA of all graded courses taken for any reason must be at least 3.0.
- See also the University requirements:
- Instructions, Policies & Procedures for Graduate Students
- Doctoral Degree Requirements
Balance of the Program:
- The Theory component is catered for specifically by 512-513 and the Theory Exam.
- The Methods component is satisfied by 502-504-536-570-571.
- The Applied component is based on methods covered in 502-504-536-570-571 and 528 Applied Statistics Capstone.
- The Computing component is catered for by 534.
Course Fees
The course is being run through Professional and Continuing Education. Starting Autumn 2019 the instructional fees are $950 per credit for non-resident and $550 for resident, with a minimum of 49 credits being required. This cost does not include additional fees or the cost of textbooks and materials.
Departmental aid is available, with preference given to United States and, in particular, Washington State, residents.
We will provide students with a letter of recognition describing the sequence taken by the student. The specializations available may vary from year to year. They are:
- Demography:
- Social Statistics: Any three of
- Statistical Genetics:
- Statistical Learning:
Obvious Possible Electives Usually Available
- Stochastic Modeling of Scientific Data: STAT 516_{4}, STAT 517_{4}
- Statistical Learning: The second year courses STAT 535_{3}, STAT 538_{3}, STAT 548_{3}
- Advanced Theory of Statistical Inference: STAT 581_{3}, STAT 582_{3}, STAT 583_{3}
- Probability: STAT/MATH 521_{3}, STAT/MATH 522_{3}, STAT/MATH 523_{3 }(All taught biannually)
- Time series: STAT 519_{3}, STAT 520_{4}, STAT 530_{3} (Not all taught every year)
- Nonparametric regression and classification: STAT/BIOSTAT 527_{3}
- Sample Survey Techniques: STAT/BIOSTAT/CS&SS 529_{3}
- Theory of Linear Models: BIOSTAT/STAT 533_{3}
- Multivariate Analysis: STAT 542_{3}
- Statistical Methods for Spatial Epidemiology: BIOSTAT 555
- Structural Equation Models: CS&SS 526
- Event History Analysis for the Social Sciences: CS&SS 544
- Multivariate Data Analysis for the Social Sciences: CS&SS 589