Undergraduate Advising

Academic advising is a collaborative educational process in which students and their advisors are partners in meeting the essential learning outcomes that support student success. This partnership requires participation and involvement of both the advisor and the student and spans the student’s whole educational experience.

Portland State University advising system has two types of academic advisors, the pathway advisors and the faculty advisors. Pathway advisors assist with university general education, BA/BS degree requirements, academic standing issues and petitions.  They also provide referrals to various campus services.  

Mathematics and Data Science majors belong to the Engineering, Computer Science, Mathematics, and Physics Pathway.

Faculty advisors or department advisors support students in the Mathematics and Data Science majors and in the Mathematics minor. Meeting with a faculty advisor before registering for courses is an important part of your academic success. Faculty advisors can help you develop an academic plan that supports your interests and career aspirations, navigate degree requirements, and make informed course selections to stay on track for graduation.
 

Faculty Advising Guide for Mathematics & Data Science Majors

Meet with a Faculty Advisor Early 
We strongly encourage all Mathematics and Data Science majors to meet with a department faculty advisor as early as possible in their academic program. Faculty advisors can help you select appropriate electives, develop a course plan, explore career and graduate school options, and align your coursework with your academic and professional goals.
Students interested in undergraduate research are encouraged to explore the Departmental Honors Track and discuss research opportunities with a faculty advisor.
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MTH 300: Introduction to Mathematical Reasoning

Students transitioning to upper-division mathematics coursework should strongly consider taking MTH 300: Introduction to Mathematical Reasoning during the Fall term.
MTH 300 serves as an important bridge between the computational focus of many 200-level mathematics courses and the proof-based nature of 300- and 400-level courses. The course develops the logical reasoning and proof-writing skills that are essential for success in advanced mathematics.  The proof techniques learned in MTH 300 will help prepare students for subsequent upper-division courses and contribute to stronger performance throughout the major.

Students can take MTH 344 or MTH 346 concurrently with MTH 300 in Fall term. 

MTH 300 also provides valuable preparation for MTH 311: Introduction to Mathematical Analysis I. We recommend taking MTH 311 in Winter term, followed by MTH 312 in Spring term.
Please note: MTH 300 is offered only during Fall terms.
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STAT 451: Applied Statistics for Engineers and Scientists I and STAT 452: Applied Statistics for Engineers and Scientists II do not satisfy the 400-level approved sequence requirement for the mathematics major.

However, both courses are approved 400-level elective courses for the mathematics major and approved Mathematics/Statistics electives for the Data Science major.
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MTH 484: Algebra and Geometry Connections for Teachers is not an approved 400-level elective for the mathematics major.
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STAT 399, Introduction to Statistical Methods II will be offered for the first time in Winter 2027. The course is an approved Mathematics/Statistics elective for the Data Science major.  
Course Description
A continuation of Introduction to Statistical Methods.  Hypothesis testing and confidence intervals for two-sample proportions and means from two samples (paired and unpaired).  Family-wise error rate control using Bonferroni and sample size planning with power considerations. Chi-square tests of homogeneity, goodness of fit, and independence. Includes and introduction to one-way ANOVA and simple linear regression. Non-parametric testing options including Fisher’s Exact Test. More with bootstrapping and simulation methods relating to inference.

Prerequisites: STAT 361, Introduction to Statistical Methods.