The Graduate Certificate in Applied Statistics + Data Science provides students with essential training in statistical methods and data science tools to address real-world challenges across diverse fields. Designed for accessibility and flexibility, the program is suitable for undergraduate students from math-intensive fields seeking advanced graduate-level training to enhance job readiness, as well as for graduate students from other less math-intensive disciplines looking to complement their expertise with data science and statistical skills. The program's curriculum enables students to build foundational statistical and data science competencies during the program, rather than requiring mastery beforehand, and fosters proficiency in specific fields of application through a wide array of electives. The program also serves as an effective stepping stone for students interested in the revised MS in Statistics + Data Science program.
Admission:
Program prerequisites:
Prospective students must have basic preparation in mathematics and statistics, including an introductory statistical methods course (such as Stat 244, Stat 364 or equivalent), Calculus II (Mth 252Z), and Linear Algebra (Mth 261).
In addition to the program prerequisites, applicants must meet the university's minimum admission requirements including English language proficiency.
Program Goals, Objectives:
Many graduate programs include a research methods component that requires the student to acquire some exposure to statistical methods as the basis for the design of experiments and analysis of data. The Graduate Certificate in Applied Statistics + Data Science goes well beyond those requirements -- the student develops both a depth of understanding of methods and a breadth of application across disciplines. It is expected that a student who earns this certificate would be capable of performing sophisticated statistical analysis and modeling for problems within his or her particular discipline. They would also be expected to be able to access and understand consultation with professional statisticians and provide consultation in the application of statistical methods for research purposes and in the solution of practical problems. The goal of the program is a coordinated plan for which students will be assured of exposure to statistical techniques needed in most applications.
Core Requirements:
This Graduate Certificate credential may be completed with a minimum of 23 credit hours: including foundational statistics and data science graduate coursework, plus 8 credits on a field of application chosen by the student among the pre-approved electives, or otherwise approved as electives by the Statistics + Data Science graduate program adviser.
Graduate certificate students must have a minimum 3.00 GPA on all 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 graduate certificate with the specific written approval of the program.
Students are responsible for knowing University-level graduate policies and procedures for obtaining the certificate. These policies and procedures are in the Graduate School section of the PSU Bulletin. Several of the most frequently asked questions about University-level graduate policies and procedures can also be found on the Graduate School website.
Course of Study
The program of study leading to a Graduate Certificate in Applied Statistics + Data Science requires the successful completion of a minimum of 23 graduate credit hours of coursework distributed as three components:
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Statistics and Data Science core: The goal of this component is to introduce students to foundational concepts in statistics, data science and their application to solve real-world problems. This four core course module includes: Stat 531 Ethics and Practice of Data Science (3 credits), Stat 564 Applied Regression Analysis (3 credits), Stat 551 Applied Statistics for Engineers and Scientists (4 credits) and Stat 587 Data Science I (3 credits).
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Area of Specialization: The objective of this component is to help the student either 1) develop proficiency in a field of application, or 2) further strengthen their statistical and data science toolkit. A minimum of 8 additional hours chosen from the list of interdisciplinary courses below. Please note that 510/610 courses are not acceptable toward the certificate.
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Statistical consulting: To provide experience in dealing with real-world data-driven problems Stat 570 Statistical Consulting (3 credits). Please note that this course is only offered during winter and spring terms.
All courses applied to the certificate program must have a B- or better grade. To continue in the program, students are required to maintain regular graduate student status, requiring a cumulative 3.00 GPA for all coursework and a term GPA of at least 2.67.
Requirements
Statistics and Data Science Core
| Stat 551 | Applied Statistics for Engineers and Scientists I | 4 |
| Stat 564 | Applied Regression Analysis | 3 |
| Stat 531 | Ethics and Practice of Data Science | 3 |
| Stat 587 | Data Science I | 3 |
Consulting
Area of Specialization
A minimum of 8 elective credit hours must be completed. The following list of courses is pre-approved for elective credit.
| Stat 552 | Applied Statistics for Engineers and Scientists II | 3 |
| Stat 561 | Mathematical Statistics I | 3 |
| Stat 562 | Mathematical Statistics II | 3 |
| Stat 563 | Mathematical Statistics III | 3 |
| Stat 565 | Experimental Design: Theory and Methods I | 3 |
| Stat 566 | Experimental Design: Theory and Methods II | 3 |
| Stat 588 | Data Science II | 3 |
| Mth 563 | Computational Methods for Data Science | 3 |
| Mth 566 | Optimization for Data Science | 3 |
| Stat 567 | Applied Probability I | 3 |
| Stat 568 | Applied Probability II | 3 |
| Stat 571 | Applied Multivariate Statistical Analysis | 3 |
| Stat 572 | Bayesian Statistics | 3 |
| Stat 573 | Computer Intensive Methods in Statistics | 3 |
| Stat 576 | Sampling Theory and Methods | 3 |
| CS 541 | Artificial Intelligence | 3 |
| CS 542 | Advanced Artificial Intelligence: Combinatorial Games | 3 |
| CS 543 | Advanced Artificial Intelligence: Combinatorial Search | 3 |
| CS 545 | Machine Learning | 3 |
| CS 546 | Reinforcement Learning | 3 |
| Ec 572 | Time Series Analysis and Forecasts | 4 |
| USP 655 | Advanced Data Analysis: Structural Equation Modeling | 3 |
| EE 516 | Mathematical Foundations of Machine Learning | 4 |
| EE 515 | Computer Vision | 4 |
| EE 518 | Machine Learning Theory and Algorithms | 4 |
| EE 519 | Deep Learning Theory and Fundamentals | 4 |
| EE 522 | Discrete Time Processing | 4 |
| EE 525 | Spectral Estimation | 4 |
| Ec 572 | Time Series Analysis and Forecasts | 4 |
| USP 655 | Advanced Data Analysis: Structural Equation Modeling | 3 |
| Geog 518/ESM 518 | Landscape Ecology | 4 |
| ESM 565 | Investigating Ecological and Social Issues in Urban Parks and Natural Areas | 4 |
| ESM 566/CE 566 | Environmental Data Analysis | 4 |
| ESM 567 | Multivariate Analysis of Environmental Data | 4 |
| ESM 585 | Ecology and Management of Bio-Invasions | 4 |
| Geog 512 | Global Climate Change Science and Socio-environmental Impact Assessment | 4 |
| Geog 514 | Hydrology | 4 |
| ESM 525/CE 565 | Watershed Hydrology | 4 |
| Geog 572 | Critical GIS | 2 |
| Geog 588/USP 591 | Geographic Information Systems I: Introduction | 4 |
| Geog 592/USP 592 | Geographic Information Systems II: Advanced GIS | 4 |
| Geog 594 | GIS for Water Resources | 4 |
| Geog 596 | Introduction to Spatial Quantitative Analysis | 4 |
| Geog 597 | Advanced Spatial Quantitative Analysis | 4 |
| SySc 514 | System Dynamics | 4 |
| SySc 525 | Agent Based Simulation | 4 |
| SySc 527 | Discrete System Simulation | 4 |
| SySc 531 | Data Mining with Information Theory | 4 |
| SySc 535 | Modeling & Simulation with R and Python | 4 |
| SySc 540 | Introduction to Network Science | 4 |
| SySc 552 | Game Theory | 4 |
| SySc 575 | AI: Neural Networks I | 4 |
| BSTA 517 | Statistical Methods in Clinical Trials | 3 |
| BSTA 519 | Applied Longitudinal Data Analysis | 3 |
| PHE 513 | Introduction to Public Health | 3 |
| Epi 525 | Biostatistics I | 4 |
| Epi 512 | Epidemiology I | 4 |
| Epi 513 | Epidemiology II | 4 |
| Epi 514 | Epidemiology III | 4 |
| Epi 536 | Epidemiological Data Analysis & Interpretation | 4 |
Students students should consult with the department chair if they would like to use courses from OHSU to fulfill electives.