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Graduate Certificate in
Applied Statistics
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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.

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.

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. Please note only 3 credits of statistical programming will count towards your certificate. A minimum 3.0 GPA is required to obtain the certificate.

Required Courses (6 credits)

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

    • Prerequisite

      one undergraduate course in statistics

  • 3
    credits

    Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.

Elective Courses (select 6 credits)

  • 3
    credits

    Probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems.

    • Prerequisite

      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

    A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests.

    • Prerequisite

      STAT 414

    • 3
      credits

      Introduction, intermediate, and advanced topics in SAS.

      • Prerequisite

        3 credits in statistics

      • Note

        Credit cannot be received for both STAT 483 and STAT 480/481/482.

      • 1
        credit

        Selection and evaluation of statistical computer packages.

        • Prerequisite

          3 credits in statistics

      • 1
        credit

        Intermediate SAS for data management.

        • Prerequisite

          STAT 480

      • 1
        credit

        This course covers advanced statistical procedures in SAS, including ANOVA, GIM, CORR, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, MIXED, GRAPH, EXPORT, and SQL.

        • Prerequisite

          STAT 480 and STAT 481

  • 1
    credit

    Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics.

  • 1
    credit

    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

  • 2
    credits

    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

  • 3
    credits

    Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.

    • Prerequisite

      STAT 462 or STAT 501

  • 3
    credits

    Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.

    • Prerequisite

      STAT 462 or STAT 501; STAT 502

  • 3
    credits

    Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher-way tables; latent and logistics models.

  • 3
    credits

    Analysis of multivariate data; T-squared tests; partial correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations.

  • 3
    credits

    Theory and application of sampling from finite populations.

    • Prerequisite

      calculus, 3 credits in statistics (STAT 500 is recommended)

  • 3
    credits

    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

    Data mining tools are exploring data with regression, PCA, discriminate analysis, cluster analysis, and 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

    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

    Identification of models for empirical data collected over time. Use of models in forecasting.

    • Prerequisite

      STAT 462 or STAT 501 or STAT 511

Course Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

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