MS and Certificate Program Requirements

For a list of all degree program requirements, please see the Degree Course Requirements . Below is an overview of the progression of Applied Statistics courses.

In STA511 and STA503, first semester MS students learn the basic computer programming skills necessary for statistical analysis of data, namely by learning SAS and R. SAS is the software used by pharmaceutical and many other industrial companies. R is also commonly used in a wide variety of companies and institutions for many applications. By learning the basics of these software programs in the first semester, MS students can utilize these skills for data analysis in all subsequent courses. In subsequent classes, students will then be able to learn statistical computing techniques concurrently with the theory and purpose of more complex statistical techniques

First year MS students also study mathematical statistics. In STA505 and STA506, students study the underlying theory of the material taught in the degree program. Thus, even after just one semester, students have an understanding of theoretical statistics and computer programming and are ready to delve into advanced statistical topics using real-life examples. STA504 covers the same material as STA505, but has an extra hour each week devoted to review of important calculus topics important to statistics.

After completing the core classes in the first year (STA503, STA504/505, STA506, STA511, and STA512), students have the opportunity to take two or three electives chosen from a wide range of important and timely topics. Again, the Degree Course Requirements show which electives are part of each concentration and the side panel displays syllabi for all of these courses.

STA512, STA507, and STA513 give students the skills required to design experiments and perform analysis on the kind of data found in real-life settings In each of these classes, critical thinking skills as related to statistical analyses are emphasized. Moreover, each class strongly emphasizes the ability to effectively communicate complex statistical ideas to non-statisticians. Students are evaluated on their ability to communicate data analysis results and to justify the choice of analysis, through both written reports and oral presentations. Additionally, role-playing scenarios are utilized to train students to communicate effectively in a statistical consulting situation.

STA514 is a culmination of the classroom portion of the program, as students gain experience in critically reading journal articles with regard to their statistical content. Many of the procedures introduced in preceding classes will be reinforced through practical applications. Again, the skills developed in this class will be those highly valued by potential employers.

STA531 is a Special Topics course that changes annually. It is designed to teach students about areas that are of current interest in Applied Statistics, but not covered in depth in other courses. Past and potential topics include data mining, survival analysis, statistical genetics, database marketing, time series, and non-parametric methods. Members of local industry may teach or co-teach STA531 courses.

MS students also have the opportunity to get hands-on experience working with statisticians and scientists by taking STA601 and participating in an internship with a local company. Upon completion of their internships, students write a summary report and present the results of their research to program faculty. Recent students have completed internships at companies such as AstraZeneca, Chase, Citibank, Endo Pharmaceuticals, GlaxoSmithKline, Incyte, Merck, QVC, University of Pennsylvania School of Medicine, Stroud Water Research Center, and many others.

STA609 and STA610 give thesis-track students the opportunity to conduct supervised research in an area of interest. The thesis may address any topic approved by the

Program Director

, including exploration of a new statistical theory or application of a statistical model to a new problem. The thesis may be interdisciplinary and involve work with professors from multiple departments.  A student may extend his or her thesis work over more than one semester if needed. Thesis credits cannot be used as a substitute for other required course credits.


The Certificate Program in Applied Statistics requires 19 hours of coursework but does not include the mathematical theory classes included in the MS program.

Important information about the educational debt, earnings, and completion rates of students who attended the Certificate in Applied Statistics program.

Program Prerequisites

Prerequisite knowledge for the MS program will be a course in Multivariate Calculus. We have created a pathway that makes it convenient for students to fulfill all prerequisites. If a student has taken Calculus I or equivalent, they can take the new MAT243 over the summer, as an online asynchronous class. This class will cover all of the Calculus prerequisite topics, as well as an introduction to the topics needed from Linear Algebra. It will also be expected that a student will have completed an introduction to Statistics at the undergraduate level.

No background in calculus or other advanced math topics is required for the Applied Statistics Certificate. The only prerequisite for the Certificate Program is MAT121 or an equivalent introduction to statistics.

Extracurricular Events

For extracurricular events in the Applied Statistics department, and for new student orientation materials for Applied Statistics graduate students, please visit the Applied Statistics Events page.

Summer 2022 Course Offerings (Now Online)

Summer I

STA 536 - Data Mining (Crossett))

Summer II

STA 531 - Introduction to Python for Statistical Programming (Lavery)

Fall 2022 Course Offerings

STA 503 - Introduction to R (Crossett)

STA 504 - Mathematical Statistics I with Calculus Review (Peng)

STA 505 - Mathematical Statistics I (Gallop)

STA 507 - Categorical Data Analysis (McClintock)

STA 511 - Introduction to Statistical Programming (Rieger)

STA 513 - Intermediate Linear Models (Gallop)

STA 545 - Clinical Trials (Godbold)

Winter 2021/22 Course Offerings

STA537 - Advanced SAS Programming (Gallop & Rieger)

Spring 2022 Course Offerings

STA 506 - Mathematical Statistics II (Crossett)

STA 512 - Principles of Experimental Analysis (McClintock)

STA 514 - Modern Experimental Design and Sampling Methods (Rieger)

STA 533 - Longitudinal Data Analysis (Gallop)

STA 553 - Data Visualizations and Infographics (Peng)

 

Example Courses & Syllabi

STA 503 Introduction to R

STA 504 Mathematical Statistics I (with calculus review)

STA 505 Mathematical Statistics I

STA 506 Mathematical Statistics II

STA 507 Intro. Categorical Data Analysis

STA 511 Intro. Statistical Computing

STA 512 Principles of Experimental Analysis 

STA 513 Intermediate Linear Models

STA 514 Modern Experimental Design and Sampling Methods

STA 532 Survival Analysis

STA 533 Longitudinal Data Analysis

STA 534 Time Series

STA 535 Multivariate Data Analysis

STA 536 Data Mining

STA 537 Advanced SAS Programming

STA 538 Statistical Programming Using R

STA 539 Applied Bayesian Methods

STA 540 Statistical Consulting

STA 541 Categorical Data Analysis II

STA 542 Observational Course

STA 543 Statistical Methods in Business and Finance

STA 544 Marketing Analytics

STA 545 Clinical Trials

STA 546 Foundations of Bioinformatics

STA 551 Foundations of Data Science

STA 552 Applied Statistical Machine Learning

STA 553 Data Visualization

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