Courses

Penn State's 12-credit online Graduate Certificate in Applied Statistics consists of required and elective courses that can deepen your knowledge of statistical analysis and provide you with:

training on SAS and Minitab software packages
a blend of practical and theoretical data-analysis skills
sophisticated tools and knowledge to handle and analyze data

The graduate certificate allows you to simultaneously gain graduate credit and a highly valued skill set. The applied statistics program is designed as a "stand-alone" certificate or can serve as a "step-up" program into a master's degree — including the Master of Applied Statistics degree.

To earn the Graduate Certificate in Applied Statistics, you will take 6 credits of required courses and choose 6 credits of electives based on your professional goals. At least 6 of the 12 credits must come from courses at the 500 level or above. A minimum 3.0 GPA is required to obtain the certificate.

Most courses within the Applied Statistics program are also available as individual courses for those looking to fulfill continuing professional development requirements. Read the instructions for how to register for Penn State World Campus courses to learn how you can enroll in any of the upcoming classes on an individual basis.

Course List - Graduate Certificate in Applied Statistics

Required Courses (6 Credits)
Title	Abbreviation	Description	Credits
Applied Statistics	STAT 500	Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics. Prerequisite: 3 credits in statistics	3 credits
Regression Methods	STAT 501	Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression. Prerequisites: 6 credits of statistics or STAT 500; matrix algebra	3 credits

Elective Courses (6 Credits)
Title	Abbreviation	Description	Credits
Introduction to Probability Theory	STAT 414	Probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems. Prerequisites: Statistics — STAT 500 and STAT 501 strongly recommended. Math — A standard three-course calculus sequence (for example, MATH 140, MATH 141 and MATH 230) and knowledge of matrix algebra and linear algebra (similar to MATH 220). If taken more than a few years ago, students are strongly encouraged to review their calculus knowledge.	3 credits
Introduction to Mathematical Statistics	STAT 415	A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests. Prerequisites: STAT 414	3 credits
Introduction to SAS	STAT 480	Selection and evaluation of statistical computer packages. Prerequisite: 3 credits in statistics	1 credit
Intermediate SAS for Data Management	STAT 481	Intermediate SAS for data management. Prerequisite: STAT 480	1 credit
Advanced Statistical Procedures in SAS	STAT 482	This course covers advanced statistical procedures in SAS, including ANOVA, GIM, CORR, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, MIXED, GRAPH, EXPORT, and SQL. Prerequisites: STAT 480, STAT 481	1 credit
Statistical Analysis System Programming	STAT 483	Introduction, intermediate, and advanced topics in SAS. Note: Credit can not be received for both STAT 483 and STAT 480/481/482. Prerequisites: 3 credits in statistics	3 credits
The R Statistical Programing Language	STAT 484	Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics.	1 credit
Intermediate R Statistical Programming Language	STAT 485	Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics. R is a popular tool for statistical analysis and research used by a growing number of data analysts inside corporations and academia. Enforced Concurrent at Enrollment: STAT 484	1 credit
Introduction to Statistical Analysis with Python	STAT 487	Due to the pervasiveness of Python as a statistical analysis tool, there is a demand for statisticians to learn Python to perform descriptive and inferential data analysis. The course will take a case study approach to introduce students to Python. Students will learn to work with complex data using Python and will get hands-on experience on how to use Python to conduct statistical analyses. Enforced Prerequisite at Enrollment: STAT 300 or STAT 460 or STAT 461 or STAT 462 or STAT 500	2 credits
Analysis of Variance and Design of Experiments	STAT 502	Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs. Prerequisites: STAT 462 or STAT 501	3 credits
Design of Experiments	STAT 503	Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs. Prerequisites: STAT 462 or STAT 501; STAT 502	3 credits
Analysis of Discrete Data	STAT 504	Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher-way tables; latent and logistics models. Prerequisites: STAT 500, 501, and 502; matrix algebra	3 credits
Applied Multivariate Statistical Analysis	STAT 505	Analysis of multivariate data; T-squared tests; partial correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations. Prerequisites: STAT 501 and 502; matrix algebra	3 credits
Sampling Theory and Methods	STAT 506	Theory and application of sampling from finite populations. Prerequisites: calculus, 3 credits in statistics (STAT 500 is recommended)	3 credits
Epidemiologic Research Methods	STAT 507	Develops research and quantitative methods related to the design and analysis of epidemiological (mostly observational) studies. Such studies assess the health and disease status of one or more human populations or identify factors associated with health and disease status. To a lesser degree, the course also covers non-randomized, intervention (experimental) studies that may be designed and analyzed with epidemiological methods. Prerequisite: STAT 500	3 credits
Applied Data Mining and Statistical Learning	STAT 508	Data Mining tools are exploring data with regression, PCA, discriminate analysis, cluster analysis, classification and regression trees (CART). Prerequisite: STAT 501 or a similar course that covers analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression	3 credits
Design and Analysis of Clinical Trials	STAT 509	The objective of the course is to introduce students to the various design and statistical analysis issues in biomedical research. This is intended as a survey course covering a wide variety of topics in clinical trials, bioequivalence trials, toxicological experiments, and epidemiological studies. Prerequisite: STAT 500	3 credits
Applied Time Series Analysis	STAT 510	Identification of models for empirical data collected over time. Use of models in forecasting. Prerequisite: STAT 462, 501, or 511	3 credits

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