Applied Statistics

Return to: College of Arts and Sciences Departments/Programs

Students who declare the BA in Applied Statistics have the option of choosing one of three concentrations within the major. These concentrations are Finance and Business, Biostatistics, and Data Science. The details of these concentrations are given below. The prerequisites to declare any of the concentrations are the same as described below.

Program Requirements

The B.A. in Applied Statistics requires five core courses and five restricted elective courses. In total the B.A. in Applied Statistics requires 30 credit hours, plus prerequisite courses. There are two lists of restricted elective courses, those that focus on data analysis and those that are more computational.

Of the five restricted elective courses, at least three must be taken from the Data Analysis list. A grade of C- or higher is required for all major courses.

Prerequisites: 6 - 8 credit hours

Students may use AP credit to satisfy the prerequisites.

Calculus II: Fulfilled by one of the following courses:
MATH 1220 - A Survey of Calculus II Credits: 3
MATH 1320 - Calculus II Credits: 4
APMA 1110 - Single Variable Calculus II Credits: 4
Introductory Statistics: Fulfilled by one of the following courses:
STAT 1100 - Chance: An Introduction to Statistics Credits: 3
STAT 1120 - Introduction to Statistics Credits: 3
STAT 2020 - Statistics for Biologists Credits: 4
APMA 3120 - Statistics Credits: 3
Introductory Programming: Fulfilled by one of the following courses:
STAT 1601 - Introduction to Data Science with R Credits: 3
CS 1111 - Introduction to Programming Credits: 3
CS 1112 - Introduction to Programming Credits: 3
CS 1113 - Introduction to Programming Credits: 3
Each of the Following:
STAT 3110 - Foundations of Statistics Credits: 3 (MATH 3100 can also be used when combined with MATH 3350 or MATH 3351)
STAT 3220 - Introduction to Regression Analysis Credits: 3

Core Courses: 15 credit hours

ONE OF THE FOLLOWING:
STAT 3110 - Foundations of Statistics Credits: 3
OR
MATH 3100 - Introduction to Probability Credits: 3
AND
MATH 3351 - Elementary Linear Algebra Credits: 3
OR
MATH 3350 - Applied Linear Algebra Credits: 3
EACH OF THE FOLLOWING:
STAT 3080 - From Data to Knowledge Credits: 3
STAT 3120 - Introduction to Mathematical Statistics Credits: 3
STAT 3220 - Introduction to Regression Analysis Credits: 3
STAT 4996 - Capstone Credits: 3

Restricted Electives:

The Data Analysis restricted electives and the Computational restricted electives are listed below. Students must take five restricted electives, with at least three from the Data Analysis list. At most one of the five restricted electives may be drawn from a non-STAT pneumonic. (This limit on non-STAT courses also applies to the concentrations listed below.)

Data Analysis Restricted Electives: Minimum of 9 credit hours

STAT 3130 - Design and Analysis of Sample Surveys Credits: 3
STAT 3480 - Nonparametric and Rank-Based Statistics Credits: 3
STAT 4120 - Applied Linear Models Credits: 3
STAT 4130 - Applied Multivariate Statistics Credits: 3
STAT 4160 - Experimental Design Credits: 3
STAT 4170 - Financial Time Series and Forecasting Credits: 3
STAT 4220 - Applied Analytics for Business Credits: 3
STAT 4630 - Statistical Machine Learning Credits: 3
STAT 5140: Survival Analysis and Reliability Theory (3 credit hours)
STAT 5150: Actuarial Statistics (3 credit hours)
STAT 5170: Applied Time Series (3 credit hours)
STAT 5265: Investment Science I (3 credit hours)
STAT 5330: Data Mining (3 credit hours)
STAT 5340: Bootstrap and Other Resampling Methods (3 credit hours)
STAT 5390: Exploratory Data Analysis (3 credit hours)
STAT 5740: Mining Unstructured Data (3 credit hours)
ECON 3720 - Introduction to Econometrics Credits: 4
ECON 4720 - Econometric Methods Credits: 3
SOC 5110: Survey Research Methods (3 credit hours)
SYS 4021 - Linear Statistical Models Credits: 4

Computation Restricted Electives: Minimum of 3 credit hours

STAT 3240 - Coding in Matlab/Mathematica with Applications Credits: 3
STAT 3250 - Data Analysis with Python Credits: 3
STAT 3280 - Data Visualization and Management Credits: 3
STAT 4210 - Big Data Tools Credits: 3
STAT 4260 - Databases Credits: 3
STAT 4310 - Data Visualization and Presentation Credits: 3
ASTR 4140 - Research Methods in Astrophysics Credits: 3
COMM 3220 - Database Management Systems and Business Intelligence Credits: 3
CS 4444 - Introduction to Parallel Computing Credits: 3
CS 4740 - Cloud Computing Credits: 3
CS 4750 - Database Systems Credits: 3
PHYS 5630: Computational Physics I (3 credit hours)

Course Duplication Limitations

Only one of STAT 4170 and STAT 5170 will satisfy the major requirements, as these are both versions of a time series course.
Only one of COMM 3220, STAT 4260, STAT 3280, ASTR 4140, and CS 4750 will satisfy the major requirements, as these are all versions of a database course.
Only one of ECON 3720, ECON 4720, STAT 4120, and SYS 4021 will satisfy the major requirements, as these are all versions of a regression course.

Applied Statistics Concentrations

Those declaring the B.A. in Applied Statistics have the option of choosing a major concentration. The concentrations are Finance and Business, Biostatistics, and Data Science. The requirements for these concentrations are given below. The prerequisites to declare any of the concentrations are the same as described earlier.

Description of Capstone

For the capstone, students will work in teams of 3 or 4 to complete an extensive data analysis project. The capstone faculty will work collaboratively to develop a range of hands-on projects for the teams to demonstrate knowledge and skill in data analysis, interpretation, and communication. Each project will require students to determine the nature of the questions of interest; prepare data for analysis; select and perform the appropriate analysis; determine conclusions; and present the results. The capstone project will provide an opportunity to observe how the student works through all aspects of a statistical analysis.

Students will be guided and evaluated by the capstone faculty. The capstone experience will culminate with the submission of a final report of approximately 25 pages, and a formal presentation. If a student fails the capstone course, the Department of Statistics’ Director of Undergraduate Programs will meet with the student to determine a set of revisions and/or alternative academic activities to complete their project. A student who fails to complete their project may retake the course in a subsequent semester.