Program Information

The Master of Arts in Data Analysis and Applied Statistics (DAAS) is a part-time, flexible degree program that will be offered in the Washington metropolitan area starting in Fall of 2019.  This is a terminal Master’s degree designed for working professionals and employers who desire an advanced education in data analytics. The curriculum has been designed to help meet overwhelming demand in the Washington metropolitan area for an analytically sophisticated workforce. 

Location + Schedule

Classes will be taught at the Northern Virginia Center (NVC) in Falls Church. The NVC is conveniently located near the Orange Line Metro stop at West Falls Church-VT/UVA and I-66 with plenty of parking. Classes are 7:00-10:00pm once per week.

Address: 7054 Haycock Rd Falls Church, VA 22043

Coursework

The DAAS degree consists of 11 courses (33 credit hours) of which seven are core courses, including a capstone project class, and four are specialized electives. 

The electives in our initial offering in the fall of 2019 will have a Data Science focus. Starting in 2020 we intend to begin offering other variants with electives in public health, economic analysis, cybersecurity.

The first four courses in the program comprise a Certificate in Data Analysis and Applied Statistics as an alternative for students and their employers who do not desire or require a Master’s degree credential. 

 

Year 1

STAT 5054

Introduction to Statistical Programming

In class - Core Curriculum
Certificate requirement

STAT 5615

Statistics in Research I

Online - Core Curriculum
Certificate requirement

STAT 5024

Effective Communication in Statistics

In class - Core Curriculum
Certificate requirement

STAT 5616

Statistics in Research II

Online - Core Curriculum

STAT 5214G

Advanced Methods of Regression

In class - Core Curriculum

STAT 5525

Data Analytics I

Online - Data Science Elective

Year 2

STAT 5004G

Advanced Statistical Programming

In class - Core Curriculum

STAT 5526

Data Analytics II

Online - Data Science Elective

STAT 5504

Multivariate Statistical Methods

In class - Data Science Elective

STAT 5984

Experimental Design for Data Science

Online - Data Science Elective

STAT 5904

Project and Report

In class - Core Curriculum

Course Descriptions

STAT 5004G - Advanced Statistical Computing

 

 

STAT 5024 - Effective Communication in Statistics

Communication skills 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.

STAT 5054 - Introduction to Statistical Programming

Introduction to modern programming packages (R Suite) for data analysis. Basics of coding, language syntax, and statistical functionality to read in raw data files and data sets, subset data, create variables, and recode data. Summaries in the form of tables and graphs. Data analysis using standard statistical methods and data management and analysis of large data sets. Applied data analysis is emphasized rather than statistical theory. 

STAT 5105G - Introduction to Mathematical Statistics

Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 

STAT 5214G - Advanced Methods of Regression

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.

STAT 5615 - Statistics in Research I

Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 

STAT 5616 - Statistics in Research II

Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of covariance.

STAT 5904 - Project and Report

STAT 5525 - Data Analytics I

Basic techniques in data analytics including the preparation and manipulation of data for analysis and the creation of data files from multiple and dissimilar sources. The data mining and knowledge discovery process. Overview of data mining algorithms in classification, clustering, association analysis, probabilistic modeling, and matrix decompositions. Detailed study of classification methods including tree-based methods, Bayesian methods, logistic regression, ensemble, bagging and boosting methods, neural network methods, use of support vectors and Bayesian networks. Detailed study of clustering methods including k-means, hierarchical and self-organizing map methods. Prerequisite: Graduate Standing required.

STAT 5526 - Data Analytics II

Techniques in unsupervised and visualized learning in high dimension spaces. Theoretical, probabilistic, and applied aspects of data analytics. Methods include generalized linear models in high dimensional spaces, regularization, lasso and related methods, principal component regression (pca), tree methods, and random forests. Clustering methods including k-means, hierarchical clustering, biclustering, and model-based clustering will be thoroughly examined. Distance-based learning methods include multidimensional scaling, the self-organizing map, graphical/network models, and isomap. Supervised learning will consist of discriminant analyses, supervised pca, support vector machines, and kernel methods.

STAT 5984 - Experimental Design for Data Science

STAT 5504 - Multivariate Statistical Methods

Methods of inference for multivariate distributions. Multivariate distributions, location and dispersion problems for one and two samples, multivariate analysis of variance, linear models, repeated measurements, inference for dispersion and association parameters, principal components, discriminant and cluster analysis, and simultaneous inference. R will be used.