Information and Policies
Introduction
The Department of Statistics offers a Statistical Science Bachelor’s/Master's pathway. Undergraduate students in certain majors can apply to the pathway in order to earn a B.S. degree in their own major together with an M.S. degree in Statistical Science. Approved programs are:
- B.S. in Applied Physics, Astrophysics, or Physics
- B.S. in Computer Science
- B.S. in Economics/Mathematics
- B.A. in Mathematics (Pure Math and Computational)
Sample planners for the pathway are available through the Baskin Engineering Graduate Affairs Office website. Qualified undergraduates from other undergraduate majors may also apply to the pathway and their applications will be considered on a case by case basis.
Depending on the student’s progress, the B.S./M.S. pathway can be completed in five years.
Admission Requirements and Process
The requirements for admission into the 4+1 pathway are: (1) a cumulative GPA of 3.0 or more; (2) to have taken, or to have a plan to take, foundational coursework in univariate and multivariate calculus, linear algebra, introductory statistics, and programming; and (3) to have completed both STAT 131 and STAT 132 with an A- or above. (Please see Course Requirements section below for details.)
Interested students should schedule a meeting with the STAT graduate director to discuss their curriculum plan and fill the application form. The ultimate deadline for application to the pathway is the end of their junior year (ninth quarter of study at UC Santa Cruz), although students are encouraged to apply significantly earlier, ideally at the same time as their major declaration.
All students who apply to the pathway will be admitted into it, provided they satisfy the requirements. However, approval of an undergraduate student into a five-year bachelor’s/master’s path does not automatically guarantee admission into the one-year master’s program.
Students apply for admission into the M.S. program during their senior year using the same procedure and timeline as all other applicants to the program, and they will be considered for the M.S. program along with regular applicants. The pathway assists qualified enrolled students with a simplified graduate application process that does not require students to submit Graduate Record Examination (GRE) scores. This will be made clear to students admitted into the pathway, as early as possible.
Course Requirements
Students pursuing the contiguous pathway are expected to have a strong quantitative background. At the time they apply to the pathways they will be expected to have completed the following coursework:
Basic Requirements
To be admitted into the contiguous pathway, students must have completed this series of courses with an average GPA of 3.0 or above.
Univariate Calculus
Either both MATH 19A and MATH 19B, or both MATH 20A and MATH 20B.
| MATH 19A | Calculus for Science, Engineering, and Mathematics | 5 |
| MATH 19B | Calculus for Science, Engineering, and Mathematics | 5 |
| MATH 20A | Honors Calculus | 5 |
| MATH 20B | Honors Calculus | 5 |
Multivariate Calculus
Linear Algebra
| AM 10 | Mathematical Methods for Engineers I | 5 |
| | OR | |
| MATH 21 | Linear Algebra | 5 |
| | OR | |
| PHYS 116A | Mathematical Methods in Physics | 5 |
Introductory Statistics
| STAT 5 | Statistics | 5 |
| | OR | |
| STAT 7 | Statistical Methods for the Biological, Environmental, and Health Sciences | 5 |
| | OR | |
| STAT 17 | Statistical Methods for Business and Economics | 5 |
| | OR | |
| PHYS 133 | Intermediate Laboratory | 5 |
Programming
| CSE 20 | Beginning Programming in Python | 5 |
or higher level programming course; or equivalent
Advanced Requirements
During their junior year (or before, if appropriate), students in the pathway will be expected to take two courses:
| STAT 131 | Introduction to Probability Theory | 5 |
| STAT 132 | Classical and Bayesian Inference | 5 |
Students must obtain a grade no lower than A- in both courses to remain eligible for the pathway. A grade of A- or higher in STAT 131 will waive the M.S. requirement of taking STAT 203, Introduction to Probability Theory. Instead of STAT 203, students in the pathway will need to take an additional elective course to satisfy the credit requirements for the M.S. program (see sample plan below).
Students who pass STAT 131 and STAT 132 with a grade of C or higher but lower than A-, although not eligible to continue in the contiguous pathway to the M.S., will still have the opportunity to complete a minor in statistics or to apply to the M.S. through the regular admission process.
Senior Year Requirements
Students apply for admission into the M.S. program during their senior year using the same procedure and timeline as all other applicants to the program. In addition, during the senior year, students in the pathway will be required to take two graduate-level courses:
| STAT 204 | Introduction to Statistical Data Analysis | 5 |
| STAT 206B | Intermediate Bayesian Inference | 5 |
Note that the two courses satisfy requirements for the Statistical Science M.S. program and therefore cannot be counted as part of the minimum 40 credits of upper-division courses required by the undergraduate major.
Students who are accepted into the M.S. program but fail any of the two senior-year graduate courses will revert to the regular two-year M.S. timeline and will be allowed to retake them
M.S. Year Course Requirements
Students will complete the remaining required courses (STAT 200, STAT 280B, STAT 205 and STAT 208) and electives (four, one of them in lieu of STAT 203) for the M.S. program, as well as their capstone project, during their fifth year in the program. At least two of the four electives must be chosen from List A (courses offered by the Statistics Department).
A student enrolled in the 4+1 contiguous program will complete a total of 45 credits in classroom courses, just like students in the standard M.S. program, with 15 of those credits having been completed as part of their undergraduate degree.
If for any reason a student cannot complete the M.S. requirements during their fifth year, they will revert to the standard M.S. timeline.
Core Courses
| STAT 200 | Research and Teaching in Statistics | 3 |
| STAT 205 | Introduction to Classical Statistical Learning | 5 |
| STAT 208 | Linear Statistical Models | 5 |
| STAT 280B | Seminars in Statistics | 2 |
List A Electives
Electives offered by the Statistics Department and open to M.S. students.
| STAT 207 | Intermediate Bayesian Statistical Modeling | 5 |
| STAT 209 | Generalized Linear Models | 5 |
| STAT 221 | Statistical Machine Learning | 5 |
| STAT 222 | Bayesian Nonparametric Methods | 5 |
| STAT 223 | Time Series Analysis | 5 |
| STAT 224 | Bayesian Survival Analysis and Clinical Design | 5 |
| STAT 225 | Multivariate Statistical Methods | 5 |
| STAT 226 | Spatial Statistics | 5 |
| STAT 227 | Statistical Learning and High Dimensional Data Analysis | 5 |
| STAT 229 | Advanced Bayesian Computation | 5 |
| STAT 243 | Stochastic Processes | 5 |
| STAT 244 | Bayesian Decision Theory | 5 |
| STAT 246 | Probability Theory with Markov Chains | 5 |
List B Electives
Electives offered outside the Statistics Department and open to STAT M.S. students.
| AM 212A | Applied Partial Differential Equations | 5 |
| AM 216 | Stochastic Differential Equations | 5 |
| AM 230 | Numerical Optimization | 5 |
| AM 250 | An Introduction to High Performance Computing | 5 |
| CSE 242 | Machine Learning | 5 |
| CSE 243 | Data Mining | 5 |
| CSE 249 | Large-Scale Web Analytics and Machine Learning | 5 |
| CSE 261 | Advanced Visualization | 5 |
| CSE 263 | Data Driven Discovery and Visualization | 5 |
| CSE 272 | Information Retrieval | 5 |
| CSE 277 | Random Process Models in Engineering | 5 |
| ECE 253
/CSE 208
| Introduction to Information Theory | 5 |
| ECE 256 | Statistical Signal Processing | 5 |
| ECON 211A | Advanced Econometrics I | 5 |
| ECON 211B | Advanced Econometrics II | 5 |
| ENVS 215A | Geographic Information Systems and Environmental Applications | 5 |
| ENVS 215L | Exercises in Geographic Information Systems | 2 |
ENVS 215L is the concurrent lab to ENVS 215A. The lecture/lab combination counts as one course.
Capstone Requirement
This is a Plan II Capstone. For the M.S. degree, students conduct an individual capstone research project (up to three quarters) and in the spring participate in a seminar in which results from their project are presented. Examples of capstone research projects include: review and synthesis of the literature on a topical area of statistical science, application and comparison of different models and/or computational techniques from a particular area of study in statistics, and comprehensive analysis of a data set from a particular application area.
Students must submit a proposal to the potential faculty sponsor no later than the end of their first academic quarter. If the proposal is accepted, the faculty member becomes the sponsor and supervises the research and writing of the project. When the project is completed and written, it must be submitted to and accepted by a committee of two individuals, consisting of the faculty advisor and one additional reader. The additional reader will be chosen appropriately from within the graduate program faculty or outside of it. Either the advisor or the additional reader must be from within the graduate program faculty.
Planner
STAT 131 and STAT 132 are requirements to qualify for the program, but do not satisfy any M.S. requirement (except, in the case of STAT 131, waiving the requirement of taking STAT 203). An M.S. elective is required in lieu of STAT 203.
Sample Planner for Bachelor's/Master's Pathway