Program Details | Applied Statistics + Data Science Graduate Certificate

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.

Students are encouraged to meet with their advisor at the beginning of their program and consult with them regularly throughout their program. Please contact the GCAS program adviser during the term prior to the term of anticipated graduation to confirm that all program requirements have been completed.

Core Requirements

This Graduate Certificate credential may be completed with a minimum of 23 credit hours of statistical graduate coursework with no comprehensive exam, while the MS in Statistics requires more extensive coursework and examinations.

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.

The program of study leading to a GCASDS requires the successful completion of a minimum of 23 graduate credit hours of coursework distributed as three components:

(i) Statistics and Data Science core sequence: The goal of this component is to introduce students to foundational concepts 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) and
  • Stat 551 Applied Statistics for Engineers and Scientists (3 credits) and
  • Stat 564 Applied Regression Analysis (3 credits) and 
  • Stat 587 Data Science I (3 credits)

(ii) Area of Specialization: The objective of this component is to help the student either 1) develop proficiency in 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.

(iii) Consulting: To provide experience in dealing with real world data-driven problems Stat 570 Consulting Rotation (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 (23 CREDITS)

The 23 credit requirements must include courses distributed as follows:

Statistics and Data Science Core (12 credits):

  • Stat 551 Applied Statistics for Engineers and Scientists I
  • Stat 564 Applied Regression Analysis
  • Stat 531 Ethics and Practice of Data Science
  • Stat 587 Data Science I

Area of Specialization (at least 8 credits):

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
  • Stat 561 Mathematical Statistics I
  • Stat 562 Mathematical Statistics II
  • Stat 563 Mathematical Statistics III
  • Stat 565 Experimental Design: Theory and Methods I
  • Stat 566 Experimental Design: Theory and Methods II
  • Stat 588 Data Science II
  • Mth 563 Computational Methods for Data Science
  • Mth 566 Optimization for Data Science
  • 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
  • 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 522 Discrete Time Processing
  • 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 Assessment
  • 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

Consulting (3 credits):

  • Stat 570 Consulting Rotation


Please contact the GCASDS program adviser during the term prior to the term of anticipated graduation to confirm that all program requirements have been completed