Program Overview

Introduction to the MS in Applied Statistics and Data Science

The MS in Applied Statistics and Data Science at Villanova University is a top-level program with a rich history spanning over 70 years. It provides students with the training and skills needed to thrive in a variety of data-driven careers. The program has continually evolved to meet the needs of today's statistics and data science graduates, with new classes being added and developed regularly.

Program Overview

The program offers a strong foundation for statistical analysis, going beyond the teaching of black-box methods to prepare graduates for a variety of data-oriented tasks and positions. There are three tracks that students can choose as a focus for their statistical education: Applied Statistics, Biostatistics, and Data Science. Students may complete their degree on a full or part-time basis, finishing a degree as quickly as 1.5 years or over several years on a one-course-at-a-time basis.

Curriculum

To obtain a master's degree in Applied Statistics and Data Science, students must complete 10 three-credit courses, six of which are required courses and four of which are elective courses. The required courses include:

• STAT 7404 - Statistical Methods
• STAT 7500 - Statistical Programming
• STAT 8400 - Statistical Theory I
• STAT 8406 - Regression Methods Additional required courses vary by track:
• Applied Statistics and Biostatistics tracks: STAT 8401 - Statistical Theory II and STAT 8412 - Linear Models
• Data Science track: STAT 8480 - Data Mining and Predictive Analytics and either STAT 8401 - Statistical Theory II or STAT 8412 - Linear Models

Elective Courses

The four elective courses can be chosen from a list of options, including:

• STAT 8401 - Statistical Theory II
• STAT 8408 - Multivariate Methods
• STAT 8410 - Bayesian Statistics
• STAT 8412 - Linear Models
• STAT 8414 - Categorical Data Analysis
• STAT 8416 - Design of Experiments
• STAT 8444 - Time Series and Forecasting
• STAT 8446 - Survival Data Analysis
• STAT 8448 - Clinical Trials
• STAT 8450 - Longitudinal Data Analysis
• STAT 8452 - Nonparametric Statistics
• STAT 8454 - Sampling Methods
• STAT 8462 - Stochastic Modeling
• STAT 8470 - Statistical Genetics
• STAT 8480 - Data Mining & Predictive Analytics
• STAT 8490 - Deep Learning
• STAT 8790 - Selected Topics I
• STAT 8795 - Selected Topics II
• STAT 8800 - Independent Study

Degree Tracks

The program offers three tracks:

• Applied Statistics
• Biostatistics
• Data Science Each track consists of a particular set of required courses together with two of four electives chosen from a specific subset of electives.

Admissions Requirements

Applicants to the master's program in Applied Statistics must hold a bachelor's degree from an accredited institution, with an undergraduate GPA of at least 3.0, and must have completed undergraduate work in multivariable calculus and linear algebra. Letters of reference are also important, with most applicants having at least one reference from an academic professor. General GRE scores are highly recommended for international students and individuals interested in a funded position.

Funding Opportunities

The program offers department funding, including graduate assistantships and tuition scholarships. Each year, two students receive a graduate assistantship that includes tuition remission plus a modest stipend. The program also usually offers one student per year a tuition scholarship in which the tuition is fully covered.

Career Outcomes

Recent graduates from the program have accepted a variety of roles, such as predictive health analyst, fraud detection, biostatistician, statistical programming, teacher or statistics professor, strategic marketing, financial analyst, and senior scientist. The Bureau of Labor Statistics projected "Statisticians" to be the fourth fastest-growing job, with a 10-year growth of 35%.

Faculty

The program is directed by Yimin Zhang, PhD, and includes a team of dedicated and knowledgeable professors with expertise in various areas of statistics, including biostatistics, data mining, and deep learning. The faculty have won University and national awards recognizing their teaching, research, and service to the field of statistics.

Mission and Learning Goals

The primary mission of the Applied Statistics and Data Science graduate program is to prepare students for careers as statisticians and data scientists in industry, government, academia, and nonprofit organizations. The program aims to provide students with a solid foundation in probability and the theory of statistical inference, as well as advanced programming skills in SAS and R. Graduates will be able to apply statistical methods, present and communicate statistical analyses, and be competent users of advanced methods from multiple core areas of statistics.