MS: Statistics-Analytics

MS Concentration in Analytics

The Master of Science Concentration in Analytics combines the mathematical and statistical training of the traditional MS in Statistics with enhanced computational and data analytic training for those planning careers in information intensive industries or research. The program includes fundamental training in mathematical and applied statistics as well as specialized training in data management, analysis, and model building with large datasets and databases. The specialized courses have an emphasis on statistical computing, data management, and statistical learning, which encompasses the more statistical topics that fall under the broader title of data mining. Students are encouraged to gain experience in a business or consulting environment as part of the program.

Prerequisites

The prerequisites for the program include calculus through multivariable calculus, linear algebra equivalent to MATH 415, and an introduction to mathematical statistics and probability equivalent to STAT 400. Students in this program should also have prior exposure to computing using business software, statistical software such as SAS or SPSS, and an interactive programming environment such as C,  R or Matlab.

Course Requirements

The concentration requires completing 10 courses, organized around five broad areas of expertise. The first course in probability and statistics, STAT 410, may be waived for students entering with credit for the same or an equivalent course. An approved internship may be used as a substitute for STAT 427, a course in statistical consulting. The 10 required courses are described below. (For course descriptions, visit the Academic Catalog.)

Mathematical Statistics (For course descriptions, visit the Academic Catalog.)
1. STAT 410
2. STAT 510

STAT 410 is a course in probability and mathematical statistics and prepares students for STAT 510, which is the first, and most practical, of two courses in mathematical statistics that are required for doctoral programs. This course forms the foundation for statistical inference that is encountered throughout the remainder of the curriculum.

Applied Statistics (For course descriptions, visit the Academic Catalog.)
3. STAT 425
4. One of STAT 424, STAT 426, STAT 429, STAT 430, STAT, 432, STAT 578

STAT 425 is a thorough course in linear regression and data analysis that is fundamental for further study in analytics. The fourth course is a selection of one of several traditional statistics courses including analysis of variance, categorical data analysis, time series, and special topics.

Statistical Consulting (For course descriptions, visit the Academic Catalog.)
5. STAT 427 or Approved Internship (STAT 593 - Internship)

It is critical for students to be exposed to team problem solving in a consulting or business environment. That is the purpose of STAT 427. An approved internship that entails applying skills in this same way may be used in place of STAT 427.

Statistical Computing (For course descriptions, visit the Academic Catalog.)
6. STAT 440
7. STAT 448
8. One of STAT 428, CS 412

STAT 440 focuses on databases, data management, and sampling and develops skills that are vital for students to succeed in information intensive careers. STAT 448 is a survey of important and common methods of data analysis, all taught emphasizing applications utilizing large-scale statistical software. An additional course in computation is required with choices of computational theory and methods in statistics considered by STAT 428 or CS 412, which is an introductory course in data mining.

Advanced Analytics (For course descriptions, visit the Academic Catalog.)
9. STAT 542
10. One of STAT 525, STAT 571, CS 512

STAT 542 is a new course in statistical learning that covers state-of-the-art and proven modern methods for classification, clustering, model selection, and predictive modeling in the context of large datasets. A tenth class can be a choice of advanced statistical computing theory, multivariate analysis, and an advanced computer science course in data mining.

Other Requirements: 

2.75 Minimum GPA