Degree Requirements
Candidates must complete an approved 45-credit program, which includes at least 33 core credits in courses with the Stat or MTH prefix. In addition, students must satisfy Other Requirements (see below).
A student must have a minimum 3.00 GPA on the courses applied to the program of study, as well as a minimum 3.00 GPA in all graduate-level courses taken at PSU. Although grades of C+, C, and C- are below the graduate standard, they may be counted as credit toward a master’s degree with the specific written approval of the department if taken at PSU after the term of formal admission to the graduate program.
Students are responsible for knowing University-level graduate policies and procedures for obtaining the degree. These policies and procedures are in the Graduate School section of the PSU Bulletin. Several of the most frequently asked about University-level graduate policies and procedures can also be found on the Graduate School website..
Core Requirements (33 credits)
Statistics Core (12 credits):
- Stat 561 Mathematical Statistics I
- Stat 562 Mathematical Statistics II
- Stat 563 Mathematical Statistics III
- Stat 564 Applied Regression Analysis
Data Science Core (15 credits):
- Stat 531 Ethics and Practice of Data Science
- Stat 587 Data Science I
- Stat 588 Data Science II
- Mth 563 Computational Methods for Data Science
- Mth 566 Optimization for Data Science
Training and Validation Core (6 credits):
- Stat 501 Literature and Research
- Stat 570 Consulting Rotation
Electives (12 credits)
A total of 12 elective credit hours must be completed. The following list of courses is pre-approved for elective credit.
- Stat 501 Statistical Literature and Problems
- Stat 565 Experimental design I
- Stat 566 Experimental design II
- Stat 567 Applied Probability I
- Stat 568 Applied Probability II
- Stat 571 Applied Multivariate Statistical Analysis
- Stat 572 Bayesian Statistics
- Stat 573 Computer Intensive Methods in Statistics
- Stat 576 Sampling Theory and Methods
- Stat 577 Categorical Data Analysis
- Stat 578 Survival Analysis
- Stat 580 Nonparametric Methods
- Stat 661 Advanced Mathematical Statistics I
- Stat 662 Advanced Mathematical Statistics II
- Stat 663 Advanced Mathematical Statistics III
- Stat 664 Theory of Linear Models I
- Stat 665 Theory of Linear Models II
- Stat 666 Theory of Linear Models III
- Mth 667 Stochastic Processes and Probability Theory I
- Mth 668 Stochastic Processes and Probability Theory II
- Mth 669 Stochastic Processes and Probability Theory III
- Stat 671 Statistical Learning I
- Stat 672 Statistical Learning II
- Stat 673 Statistical Learning III
- Cs 541 Artificial Intelligence
- Cs 542 Adv. Art. Intelligence: Comb. Games
- Cs 543 Adv. Art. Intelligence: Comb. Search
- Cs 545 Machine Learning
- Cs 546 Advanced Topics in Machine Learning
- Ec 572 Time Series Analysis and Forecasts
- USP 655 Advanced Data Analysis: Structural Equation Modeling
- Ohsu CS 623 Deep Learning
- Ohsu CS 562 Natural Language Processing
- Ohsu 5/692 Ethics in AI and Mach. Learning Research
- Ee 516 Math. Found. of Machine Learning
- Ee 515 Computer Vision
- Ee 518 Machine Learning Theory & Algorithms
- Ee 519 Deep Learning Theory & Practice
- Ee 525 Spectral Estimation
- Ec 572 Time Series Analysis and Forecasts
- Usp 655 Adv. Data Analysis: Str. Eqn. Modeling
- Geog 518 Landscape Ecology
- Esm 565 Managing Climate Risks and Vulnerabilities: Adaptation and Mitigation
- Esm 566 Environmental Data Analysis
- Esm 567 Multivariate Analysis of Environmental & Biological Data
- Esm 585 Ecology & Management of Bio-Invasions
- Geog 512 Global Climate Change Science and Socio-environmental Impact Assessmen
- Geog 514 Hydrology
- Esr 525 Watershed Hydrology
- Geog 572 Critical GIS
- Geog 588 Introduction to Geographic Information Systems
- Geog 592 Advanced Geographic Information Systems
- Geog 594 GIS for Water Resources
- Geog 596 Introduction to Spatial Quantitative Analysis
- Geog 597 Advanced Spatial Quantitative Analysis
- SySc 514 System Dynamics
- SySc 525 Agent-Based Simulation
- SySc 527 Discrete System Simulation
- SySc 531 Data Mining with Information Theory
- SySc 535 Modeling & Simulation with R and Python
- SySc 540 Introduction to Network Science
- SySc 552 Game Theory
- SySc 575 AI: Neural Networks I
- Bsta 517 Statistical Methods in Clinical Trials
- Bsta 519 Applied Longitudinal Data Analysis
- Phe 513 Introduction to Public Health
- Epi 525 Biostatistics 1
- Epi 512 Epidemiology I
- Epi 513 Epidemiology II (Methods)
- Epi 514 Epidemiology III (Causal Inference)
- EPI 536 Epidemiologic Data Analysis & Interpretation
- Phe 513 Introduction to Public Health
- Bmi 550 Computational Biology I
- Bmi 551 Computational Biology II
Other courses outside the Department and other mathematics courses may be considered, but must be approved as electives by the Statistics + Data Science graduate program adviser. "Approved as elective" means that it is approved inside the 12 elective credit hours but not inside the core requirements. A course or sequence cannot be counted both within the core and as an elective course or sequence.
Other Requirements
The Training and Validation component of the program consists of one of the following options:
- Option 1: 3 credits of Stat 501 and 3 credits of Stat 570
- Option 2: 6 credits of Stat 570
- Option 3: 6 credits of Stat 501