PhD in Applied Statistics

Preparing students for success
in the new data-centered world

The PhD program in Applied Statistics is a research-intensive program designed for students who demand an in-depth understanding of statistical tools for solving real-world problems with innovation. The knowledge and skills that students learn prepare them to become professionals in a broad range of data-driven fields, including university professors and research statisticians in industry, government, and non-profit sectors.

Renowned FACULTY

The Applied Statistics professors support students interested in a diverse range of statistical topics including linear models, data mining and analytics, statistical process control, spatial statistics, longitudinal analysis, statistical computing, nonparametric and robust methods, change and anomaly detection, Bayesian inference, network analysis, and statistical learning.

What to expect

Students are expected to finish the PhD program in four years. They are required to take at least 48 credit hours of approved coursework and at least 24 dissertation hours. During the summer months, students are expected to enhance their research skills under the supervision of their academic advisors. Subject to evaluation by the PhD program Director, some coursework (but no more than 24 credit hours) may be transferred from previous graduate programs.

Masters-Level Courses

Required Course

Course Title

Hours

ST 552

Applied Regression Analysis

3

ST 553

Applied Multivariate Analysis

3

ST 554

Mathematical Statistics I

3

ST 555

Mathematical Statistics II

3

ST 560

Statistical Methods

3

Masters-Level Electives

Course

Course Title

Hours

ST 521

Statistical Data Management

3

ST 522

Advanced Statistical Data Management

3

ST 531

Knowledge Discovery & Data Mining I

3

ST 532

Advanced Data Mining II

3

ST 540

Statistical Programming and Computing with R

3

ST 545

Statistical Learning

3

ST 561

Applied Design of Experiments

3

ST 570

Time Series Analysis

3

ST 575

Statistical Quality Control

3

PhD Courses

Required Course

Course Title

Hours

ST 603

Advanced Statistical Inference

3

ST 610

Advanced Linear Models

3

ST 640

Statistical Computing

3

ST 645

Advanced Statistical Learning

3

 

PhD Electives

Course

Course Title

Hours

ST 615

Theory of Regression and Generalized Linear Models

3

ST 635

Nonparametric Statistics

3

ST 697

Bayesian Inference

3

ST 697

Advanced Design of Experiments

3

ST 697

Advanced Multivariate Analysis

3

ST 697

Current Research Topics

3

All students, both international and domestic, with relevant preparation in calculus, linear algebra, probability, and statistics are welcome to apply. Prospective students without an MS degree in statistics or without proper preparation at a similar level are encouraged to apply to our Master of Science program. A complete application package must be submitted at the Graduate School web-page (https://graduate.ua.edu/prospective-students/apply-now) and should include the following items:

  • Resume
  • Statement of purpose
  • Previous transcripts from undergraduate and graduate institutions
  • TOEFL/IELTS (for international students only) and GRE/GMAT test scores
  • Three letters of recommendation from professional references
  • Application fee.

GRE Exam Requirement:

Students may choose to take either GRE or GMAT. Students are expected to achieve an overall GRE score of at least 310, including at least 160 on the quantitative reasoning section.

TOEFL Exam Requirement:

All students whose first language is not English must submit an official TOEFL (Test of English as a Foreign Language) or IELTS score. The minimum required iBT TOEFL score is 90 (575 in paper-based TOEFL). The language requirement may be waived for students with a two-year US-based degree.

We prefer that students enter the program in the fall semester. In some special situations, students can be allowed to enter the program in the spring semester. Application materials for consideration for fall enrollment with financial support must be received by January 15. Applications without requests for financial assistance must be submitted by March 15. Application materials for consideration for spring enrollment with financial support must be received by October 1. Applications without requests for financial assistance must be submitted by November 1.

Entrance and Qualifying Exam

At the end of the first academic year, PhD students are required to take a written qualifying examination. The exam is usually administered at the end of a spring semester and is based on the required MS level courses including ST 552, 553, 554, 555, and 560. The qualifying exam requirement may be waived for students holding an MS degree in statistics. Students interested in waiving the exam must take an entrance exam in August before the start of the fall semester. The entrance exam represents a light version of the comprehensive exam that is based on major and fundamental concepts covered in the required MS level courses. Students passing the entrance exam are allowed to register for PhD-level courses. Students failing the entrance exam are expected to register for MS level courses in which knowledge and skill deficiencies have been identified.

Comprehensive Exam

At the end of the second year or upon the completion of at least four (12 credit hours) PhD level statistics classes, students must take a comprehensive exam. The goal of the comprehensive exam is to assess the potential of a student to conduct independent research. Individual research projects are assigned to students for independent work over a two-week period in April. By the end of the two-week term, students must submit a written report and present their findings at an Applied Statistics Ph.D. seminar. Applied Statistics graduate faculty assess the quality of completed projects based on the following rubrics:

  • Comprehensive literature review.
  • Soundness of the proposed research approach and adequacy of future research plans.
  • Strength of experimental support.
  • Quality of oral presentation and ability to address questions and concerns.
  • Quality of written report.

By the time of the comprehensive exam at the latest, students are expected to find a dissertation advisor.

Annual Interviews

Every year, PhD students meet with Applied Statistics faculty to discuss their academic performance and progress in the program

Dismissal Policy

Students who fail to meet the program requirements such as maintaining at least a 3.50 GPA, passing the qualifying exam, comprehensive exam, proposal, and dissertation defenses, or finding a dissertation advisor and forming a dissertation committee will be required to resolve all academic issues during the next semester. The failure to do so over the next semester leads to the immediate dismissal from the PhD program. The disruption of financial support can be recommended if GPA falls below 3.50 but is higher than 3.00. The financial support can be resumed when GPA becomes at least 3.50 again.

Proposal Defense

Within a year after passing the comprehensive exam, students must form a dissertation committee and present and defend their dissertation proposal. The proposal usually focuses on the already obtained findings and plans regarding research yet to be accomplished.

Dissertation Defense

The dissertation defense is the final test that usually occurs at the end of the fourth year. A dissertation must present some original contributions to the statistics literature. A PhD candidate must present a written document acceptable to the dissertation committee and Graduate School and pass the oral dissertation defense.

Current students

Mohammed Alzahrani

  • 4th Year PhD Student
  • Education:
    • BS in Qualitative Methods: King Saudi University
    • MS in Applied Statistics with a concentration in Business Analytics: Bowling Green State University
  • Work Experience: Lecturer, King Saudi University
  • Research Focus: Regression – Dimension reduction

Yvette Feng

  • 2nd Year PhD Student
  • Education:
    • MS: Bowling Green State University
  • Research Focus: Dimension reduction

Angelina Kolomoytseva

  • 3rd Year PhD Student
  • Education:
    • BS in Mathematics: Kuban State University
    • MS Applied Statistics and Decision Analytics, MBA: Western Illinois University
  • Work Experience: Graduate Teaching and Research Assistant, Western Illinois University; Business and Data Analyst, Mount St. Joseph University
  • Research Focus: Applied Statistics, Cluster Analysis, Finite Mixture Models
  • 1st Year PhD Student
  • Education:
    • BA in Economics: Northwest University of Political Science and Law
    • MA in Applied Economics: Southern Methodist University
  • 1st Year PhD Student
  • Education:
    • BS in Applied Mathematics, Statistics Concentration: San Jose State University
    • MS in Statistics: San Jose State University
  • Research Focus:Cluster Analysis, Mixture Models, Computational Statistics, Missing Data Analysis
  • 2nd Year PhD Student
  • Education:
    • Bachelor: China Foreign Affairs University
    • Master of Science: The University of Alabama
  • Work Experience: Research Data Analyst of Rural Health Research, The University of Alabama
  • Research Focus: Dimension Reduction, Classification

Connect with Your team

mbperry@ua.edu
(205) 348-9864
Alston 350

General questions about the application process?

Contact April Ingram, Director of PhD programs, at aaingram@culverhouse.ua.edu.

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