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Applied Statistics Certificate

Description of Certificate

The Graduate Certificate in Applied Statistics is designed to teach students the fundamental principles of statistics and the skills necessary to use statistical computer programs for data analysis. Students will learn how to select the appropriate statistical model for a research project, prepare raw data sets for analysis, and utilize the applicable computer software program to analyze the data sets. The program covers topics such as statistical inference, modeling, and computer programs (e.g., R Suite, SAS). Graduates will be prepared to use statistical methods and software programs to analyze data sets and summarize the results.

Target Audience

The certificate will have two target audiences:

  1. Graduate students currently enrolled in a graduate degree program related to applied statistics (e.g., Statistics, Higher Education, Educational Leadership and Policy Studies) and
  2. Current data analysis professionals in organizations that gather and analyze data, such as marketing companies and non-profit agencies.

Time to Complete

Full-time and part-time students are eligible to enroll in the certificate program. Degree-seeking students may take courses in conjunction with their regular course load. Students attending full-time can complete the certificate in a minimum of one academic year (two semesters) and a maximum of three academic years (six semesters). Degree-seeking students attending part-time can complete the certificate in approximately two academic years (four semesters) and a maximum of four academic years (eight semesters).

Non-degree seeking, full-time students can complete the certificate in a minimum of one academic year (two semesters). Non-degree-seeking, part-time students taking one course per semester can complete the certificate in two academic years (four semesters).

Admission

All students will be required to apply to the certificate program. The admission requirements will be based on enrollment status at the institution.

Eligibility

Current VT graduate students only

Complete the Application for Graduate Certificate Program form.

Degree-seeking students will:

  • Submit a Graduate School Application for Admission and pay the fee
  • Possess a bachelor's degree from an accredited institution with a GPA of 3.0 or better.

Non-degree seeking students will:

  • Submit a Graduate School Application for Admission and pay the fee
  • Possess a bachelor's degree from an accredited institution with a GPA of 3.0 or better
  •  Submit official undergraduate transcripts demonstrating bachelor's degree conferral
  • Undergraduate coursework in statistics and computer programming is recommended

Students who have not earned a degree in the United States must submit:

  • Test of English as a Foreign Language (TOEFL) minimum score of 90 on the internet-based test (iBT) or the International English Language Testing System (IELTS) with a minimum score of 6.5 TOEFL scores of 20 or greater in the Listening, Writing, Speaking, and Reading subsections.

Curriculum Requirements

The curriculum requires coursework to develop students' knowledge of statistical modeling (e.g., multiple linear regression, analysis of variance) and modern statistical programs (e.g., R Suite, SAS, JMP, SPSS). Students will learn to analyze, summarize, and interpret the results of data analyses. Students will gain an understanding of how to prepare raw data sets for analysis and how to select the most appropriate statistical model for a given research goal or project (e.g., hypothesis testing, interval estimation). Students will learn how to use statistical software programs to analyze a data set.

Program Requirements

Number of Credit Hours: 12 credit hours of graduate-level courses.

  • Choose one of the two 9-credit core course tracks: Statistics Track or Biometry Track. Then, select a 3-credit course from the restricted electives list. 

Core Courses — 9 credit hours

Statistics Track

  • STAT 5615 – Statistics in Research I (3 credits)
  • STAT 5616 – Statistics in Research II (3 credits)
  • STAT 5024 – Communication in Statistical Collaborations (3 credits)

Biometry Track

  • STAT 5605 – Biometry I (3 credits)
  • STAT 5606 – Biometry II (3 credits)
  • STAT 5024 – Communication in Statistical Collaborations (3 credits)

Restricted Electives — 3 credit hours

  • STAT 5204G: Experimental Design: Concepts and Applications (3 credits)
  • STAT 5214G: Advanced Methods of Regression Analysis (3 credits)
  • STAT 5105G: Theoretical Statistics I (3 credits)
  • ADS/STAT 5154: Statistical Computing for Data Science (3 credits)
  • STAT 5234: Experimental Design for Data Science (3 credits)
  • STAT 5504G: Advanced Applied Multivariate Methods (3 credits)
  • STAT 5514G: Advanced Introduction to Categorical Data Analysis (3 credits)
  • STAT 5364G: Advanced Statistical Genomics (3 credits)
  • STAT 5444G: Advanced Applied Bayesian Statistics (3 credits)
  • STAT 5524G: Sample Survey Methods (3 credits)
  • STAT 5525 (ADS 5525): Statistical Learning (3 credits)
  • STAT 5474: Statistical Theory of Quality Control (3 credits)
  • STAT 5664: Applied Time Series (3 credits)
  • STAT 5374: Statistical Epidemiology and Observational Studies (3 credits)

Course Descriptions

Core Courses — 9 credit hours

Statistics Track

STAT 5615: Statistics in Research (3 credits)

Concepts in statistical inference, including basic probability, estimation, and tests of hypothesis, point and interval estimation, and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of covariance. One year of Calculus. CMS.

STAT 5616: Statistics in Research (3 credits)

Concepts in statistical inference, including basic probability, estimation, and tests of hypothesis, point and interval estimation, and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 5616: Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of covariance. One year of Calculus and knowledge of CMS are required.

STAT 5024: Effective Communication in Statistical Consulting (3 credits)

Communication skills are necessary to be effective interdisciplinary statistical collaborators. Explaining and presenting statistical concepts to a non-statistical audience, helping scientists answer their research questions, and managing an effective statistical collaboration meeting.

Prerequisites: (STAT 5034 and STAT 5044) or STAT 5615

Corequisite: 5204 or 5616.

Biometry Track

STAT 5605: Biometry (3 credits)

Descriptive statistics, the normal distribution, estimation, hypothesis testing, simple linear regression, and one-way analysis of variance and the use of JMP® software (a product of SAS) with applications to the biological sciences.

STAT 5606: Biometry (3 credits)

Experimental design, nested and factorial analysis of variance, linear regression and correlation, multiple regression, and the use of JMP® software (a product of SAS), with applications to the biological sciences.

5606: Pre: 5605 or 5615. (3H,3C). 5605: I; 5606: II.

STAT 5024: Effective Communication in Statistical Consulting (3 credits)

Communication skills are necessary to be effective interdisciplinary statistical collaborators. Explaining and presenting statistical concepts to a non-statistical audience, helping scientists answer their research questions, and managing an effective statistical collaboration meeting.

Prerequisites: (STAT 5034 and STAT 5044) or STAT 5615

Corequisite: 5204 or 5616.

Restricted Electives — 3 credit hours

STAT 5204G: Experimental Design: Concepts and Applications (3 credits)

Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Completely randomized, randomized complete block and Latin square designs, analysis of covariance, split-plot designs, factorial and fractional factorial designs, incomplete block designs, repeated measures, power and sample size, mean separation procedures.

Prerequisite: STAT 5605 or STAT 5615

STAT 5214G: Advanced Methods of Regression Analysis (3 credits)

Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS.

Prerequisite: STAT 5605 or STAT 5615

STAT 5105G: Theoretical Statistics I (3 credits)

Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 5106G: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance.

Prerequisite: 5105G: Graduate Standing; 5106G: 5105G.

ADS/STAT 5154: Statistical Computing for Data Analytics (3 credits)

Computational techniques for advanced applied statistical analyses and machine learning methods. Project management for larger data projects, including computational constraints, pitfalls, and strategies related to different data types. Advanced report generation across various media, efficient R programming, advanced statistical function writing, parallel statistical computing with R, handling missing data, numerical optimization methods, the EM algorithm, and Monte Carlo methods.

STAT 5234: Experimental Design for Data Science (3 credits)

Understanding data, data collection, and proper data analysis for knowledge discovery and decision-making. Randomization, replication, blocking, data quality evaluations (e.g., representativeness of training data), analysis quality assessment (e.g., robustness of the machine learning algorithm to representativeness of training data). Strengths and weaknesses of experimental designs for data science. Modern qualitative and quantitative techniques for constructing experimental designs and analyzing experimental data. Interpretation and reporting of results.

Prerequisites: (STAT 5615 and STAT 5616) or STAT 5525 or CS 5525

STAT 5504G: Advanced Applied Multivariate Methods (3 credits)

Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminant analysis, cluster analysis. Prerequisite: Graduate Standing required

Prerequisite: STAT 5616 or STAT 5606

STAT 5514G: Advanced Introduction to Categorical Data Analysis (3 credits)

Statistical approaches to analyze categorical data. Probability computation and distribution specification, interval estimation and hypothesis testing, formulating and fitting generalized linear models including logistic and Poisson regression, algorithms used for model fitting, variable selection, and classification trees, and supervised learning.

Prerequisite: Graduate Standing.

STAT 5364G: Advanced Statistical Genomics (3 credits)

Statistical methods for bioinformatics and genetic studies, with an emphasis on statistical analysis, assumptions, and problem-solving. Topics include: basic concepts of genes and genomes, commonly used statistical methods for gene identification, association mapping, and other related problems. Focus on statistical tools for gene expression studies and association studies, multiple comparison procedures, likelihood inference, and preparation for advanced study in the areas of bioinformatics and statistical genetics.

Prerequisite: STAT 5616

STAT 5444G: Advanced Applied Bayesian Statistics (3 credits)

Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means, and regression.

Prerequisite: Graduate Standing.

STAT 5524G: Sample Survey Methods (3 credits)

Statistical methods for the design and analysis of survey sampling. Fundamental survey designs. Methods of randomization vary according to different survey designs. Estimation of population means, proportions, totals, variances, and mean squared errors—design of questionnaires and organization of a survey.

Prerequisite: STAT 4106 or STAT 4706 or STAT 5606 or STAT 5616

STAT 5525 (ADS 5525): Statistical Learning (3 credits)

Theory and application of supervised and unsupervised methods of statistical and machine learning. 5525: Methods of supervised statistical and machine learning for regression and classification. Overview of statistical (data) and algorithmic models. Detailed study of regression models for continuous and discrete data (linear, nonlinear, and generalized linear models). Detailed study of methods for classifying categorical outcomes (logistic and multinomial models, discriminant analysis, naïve Bayes). Tree-based methods for regression and classification. Feature selection, regularization, and dimension reduction for high-dimensional problems (Lasso, Ridge, PCR, PLS). Cross-validation and resampling for model tuning and uncertainty estimation. Statistical analyses using R or Python. 5526: Supervised and unsupervised statistical and machine learning for complex or high-dimensional data. Methods include: global and local models with smoothing (nearest-neighbor, kernel, and basis expansion techniques). Generalized (linear and additive) models. Methods for correlated (clustered) data, mixed models. Unsupervised learning for summarization, visualization, dimension reduction, imputation, and grouping; (K-means, PCA, hierarchical clustering, model-based clustering, association rules, self-organizing maps, and biclustering). Ensemble learning (bagging, model averaging, boosting, stacking). Support vector machines and neural networks. Introduction to methods and algorithms for deep learning. Model interpretability and explain ability. Statistical analyses using R or Python.

Prerequisite: Graduate Standing.

STAT 5474: Statistical Theory of Quality Control (3 credits)

Development of statistical concepts and theory underlying procedures used in quality control applications. Sampling inspection procedures, the sequential probability ratio test, continuous sampling procedures, process control procedures, and experimental design.

Prerequisites: STAT 5104 and STAT 5114

STAT 5664: Applied Time Series (3 credits)

Applied course in time series analysis methods. Topics include regression analysis, detecting and addressing autocorrelation, modeling seasonal or cyclical trends, creating stationary time series, smoothing techniques, forecasting errors, and fitting autoregressive integrated moving average models.

Prerequisite: STAT 5616 or STAT 5606

STAT 5374: Statistical Epidemiology and Observational Studies (3 credits)

Statistical methodology for epidemiology and observational studies. Statistical evaluation and inference for risk and prevalence of population safety and disease risk factors. Epidemiology and observational study design. Emphasis on casual inference and statistical models. Pre: 5034 or 5124 or 5615.

Prerequisite: STAT 5034 or STAT 5124 or STAT 5615

Faculty director: Anne Driscoll Email contact: agryan@vt.edu

John DeLong

Graduate Coordinator
406-A Hutcheson Hall
540-231-5657
jodelong@vt.edu

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