Relevant Coursework:

Course Descriptions and Details

West Chester University | M.S. Applied Statistics, Data Science Concentraton

Full degree requirements:
M.S. in Applied Statistics - Data Science Concentration

Enrolled Courses (as of Jan 2026)

Modern Experimental Design (STA514)

Focusing on recent journal articles, this course will investigate issues associated with design of various studies and experiments. Pharmaceutical clinical trials, case-controlled studies, cohort studies, survey design, bias, causality and other topics.

Completed Courses (as of Jan 2026)

Data Visualization (STA553)

Principles of data visualization and how to addresses questions about what, why, and how to visualize. Topics included visualization design elements such as colors, shapes, and movements, etc.; data exploratory visualization; statistical graphics and model visualization; process visualization; dashboard design; and the ethics of data visualization.

Applied Statistical Machine Learning (STA552)

Introduction to commonly used models and algorithms in data science fields, including both supervised and unsupervised machine learning algorithms. Topics included but not limited to probabilistic and linear classification, neural networks, tree-based models, unsupervised learning (clustering and feature extraction), and semi-supervised learning algorithms. This course covered both theories and applications.

Foundations of Data Science (STA551)

The first part of this course was dedicated to data science foundations such as statistical models, machine learning algorithms, model performance metrics, and major resampling algorithms. The second part focused on data science processes including data science project life cycle, model selection, validation, performance evaluation, and data science ethics. The last part of the course discussed data science infrastructure and pipelines.

Intermediate Linear Model (STA513)

Rigorous mathematical and computational treatment of linear models. Model types included but not limited to random effects models, mixed effects models, and generalized linear models.

Principles of Experimental Analysis (STA512)

Course included technology-driven introduction to regression and other common statistical multivariable modeling techniques. Emphasis placed on interdisciplinary actions.

Intro to Stat Computing & Data Management (STA511)

Overview of SAS for management and manipulation of data, conducting statistical analysis and generating reports and graphics.

Introduction to Categorical Data Analysis (STA507)

Data-driven introduction to statistical techniques for analysis of data arising from medical and public health studies. Contingency tables, logistic regression survival models, non parametric methods and other topics.Topics included but not limited to analysis of contingency tables, logistic regression, Poisson regression, and generalized estimating equations.

Mathematical Statistics I & II (STA505 & STA506)

A rigorous treatment of probability spaces and an introduction to the estimation of parameters. Correlation, sampling, tests of significance, analysis of variance, and other topics.

Intro to R & Intro to Python for Statistics (STA503 & STA502)

Introductory course in R programming. Major topics included setting up Rstudio, R data objects, data input/output, built-in and user-defined R functions, control statement and looping, basic R plot functions, commonly used R libraries, and R markdown.

Introductory course in Python programming. Major topics included utilization of Python and Jupyter Notebook, basic syntax, data input/output, control flows, data visualization and manipulation, along with basic descriptive statistics and statistical tests. Utilization of common libraries such as NumPy, Pandas and Maplotlib.