Apr 02, 2025  
2025-2026 Graduate Bulletin 
    
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2025-2026 Graduate Bulletin

Computer Science and Quantitative Methods (P.S.M.)

Dr. Jiang Li 
Department of Computer Science and Information Technology
Professor
Program Coordinator: Computer Science and Quantitative Methods Data Management and Analysis Concentration
Location:  Maynard Mathematics and Computer Science Building, Room 231
Phone: 931-221-7828
Email: gradpsm@apsu.edu
Website: www.apsu.edu/csci

 

Dr. Mir A. Hasan
Department of Computer Science and Information Technology
Assistant Professor
Program Coordinator: Computer Science and Quantitative Methods Cybersecurity Concentration
Location:  Maynard Mathematics and Computer Science Building, Room 227
Phone: 931-221-7856
Email: hasanm@apsu.edu
Website: www.apsu.edu/csci

Dr. Ramanjit Sahi
Department of Mathematics and Statistics
Professor
Program Coordinator: Computer Science and Quantitative Methods Mathematical Finance Concentration
Location:  Maynard Mathematics and Computer Science Building, Room 231
Phone: 931-221-7812
Email: sahir@apsu.edu
Website:  www.apsu.edu/mathematics

 

Dr. Matt Jones
Department of Mathematics and Statistics
Professor
Program Coordinator: Computer Science and Quantitative Methods Predictive Analytics Concentration
Location:  Maynard Mathematics and Computer Science Building, Room 236
Phone: 931-221-7814
Email: jonesmatt@apsu.edu
Website:  www.apsu.edu/mathematics

 

The Department of Computer Science and Information Technology and the Department of Mathematics and Statistics at Austin Peay offer both an online and on-campus Professional Science Masters ideal for professionals in a wide variety of science and math career fields.

The Professional Science Masters (P.S.M.) in Computer Science and Quantitative Methods bring together science and business skills to give students a greater competitive advantage in the workforce. Both degrees enable students to pursue advanced training while working full time. The concentrations listed below are ideal for anyone with a bachelor’s degree, working in industry or military, and/or having a desire to advance their career with a technical degree. These programs are designed to allow students to complete their degrees while continuing to hold employment.

The P.S.M. program offers four concentrations:

  1. Data Management and Analysis (online and on-campus)
  2. Predictive Analytics (online and on-campus)
  3. Cybersecurity (online and on-campus)
  4. Mathematical Finance (online and on-campus)

Intended as an alternative to research-focused doctoral programs, P.S.M. programs normally consist of study in a designated science or mathematics discipline but also contain a professional component that provides a fundamental education in professional fields such as communication, policy, business, law, or cybersecurity.

The programs are offered online, hybrid, and on-campus through the APSU Clarksville Campus and the Fort Campbell Campus allowing for students to gain admission for all terms.  The Data Management and Analysis and Predictive Analytics concentrations will soon offer classes on campus and online.

Listed below are the required application materials and the associated scores (if necessary) for each.

Admissions Requirements

  • Application for admission
  • Application Fee:  $45 domestic or $55 international
  • Bachelor’s degree, conferred in any field, from accredited institutions indicating a cumulative undergraduate GPA of at least 2.5
  • Official transcripts from all colleges attended
  • Verification of lawful presence in the United States (i.e. submit a license or other approved document)
  • GRE scores are not required (optional)
  • Resume listing three references
  • Personal Essay (the Personal Essay should be no longer than one page and should address the applicant’s career goals, why the applicant wishes to pursue the PSM at APSU, and the applicant’s quantitative/analytical background in terms of prior coursework, research, and/or employment)
  • Military documents (if applicable)
  • Admission decision by department

For more information about the application process, please visit apsu.edu/grad-studies.

Program CIP Code

06.11.0802.00

Program Modality

  • On-Ground
  • Online

Program Student Learning Outcomes

Cybersecurity Concentration

  • Students will develop proficiency in the computer science and statistical content knowledge specific to their concentration.
  • Students will develop the ability to create appropriate data management plans in scientific, business, government, or industrial contexts.
  • Students will develop the ability to identify appropriate algorithms, data structures, and statistical techniques for practical problem situations involving large data sets.
  • Students will develop the ability to draw appropriate conclusions and to recommend decisions based on analysis of data while recognizing the effect of assumptions and the limitations of the analysis.
  • Students will develop the business background to understand the finance, marketing, and employee and consumer relationships which form the context in which data-driven decisions are made and implemented.
  • Students will develop the communications kills necessary to consult with clients about their needs and to convey information to a non-technical audience.

 

Data Management and Analysis Concentration

  • Students will develop skills to study real world problems and then design and implement appropriate solutions.
  • Students will develop the ability to create appropriate data management plans in scientific, business, government, or industrial contexts.
  • Students will develop the ability to identify appropriate algorithms, data structures, and statistical techniques for problems involving large data sets including Big Data.
  • Students will develop programming skills to use data analysis functions and libraries in practical data science problems.
  • Students will develop skills to design and administer databases on different platforms, and program in a scripting language used to interface databases to web pages. 
  • Students will develop skills to use data mining methods including data integration, transformation, visualization, classification, and clustering in real world projects. 
  • Students will develop the ability to apply knowledge in data warehouse and data cube technology to the analysis of large data sets including Big Data.
  • Students will develop the ability to draw appropriate conclusions and to recommend decisions based on analysis of the data while recognizing the effect of assumptions and the limitations of the analysis. 
  • Students will develop the ability to engage in a supervised computing research program resulting in completion of a project or thesis.
  • Students will develop the business background to understand the finance, marketing, and employee and consumer relationships which form the context in which data-driven decisions are made and implemented.
  • Students will develop the communication skills necessary to consult with clients about their needs to to convey information to a non-technical audience.

 

Mathematical Finance Concentration

  • Students will develop proficiency in the computer science and statistical content knowledge specific to their concentration.
  • Students will develop the ability to create appropriate data management plans in scientific, business, government, or industrial contexts.
  • Students will develop the ability to identify appropriate algorithms, data structures, and statistical techniques for practical problem situations involving large data sets.
  • Students will develop the ability to draw appropriate conclusions and to recommend decisions based on analysis of data while recognizing the effect of assumptions and the limitations of the analysis.
  • Students will develop the business background to understand the finance, marketing, and employee and consumer relationships which form the context in which data-driven decisions are made and implemented.
  • Students will develop the communications kills necessary to consult with clients about their needs and to convey information to a non-technical audience.

 

Predictive Analytics Concentration

  • Students will develop proficiency in the computer science and statistical content knowledge specific to their concentration.
  • Students will develop the ability to create appropriate data management plans in scientific, business, government, or industrial contexts.
  • Students will develop the ability to identify appropriate algorithms, data structures, and statistical techniques for practical problem situations involving large data sets.
  • Students will develop the ability to draw appropriate conclusions and to recommend decisions based on analysis of data while recognizing the effect of assumptions and the limitations of the analysis.
  • Students will develop the business background to understand the finance, marketing, and employee and consumer relationships which form the context in which data-driven decisions are made and implemented.
  • Students will develop the communications kills necessary to consult with clients about their needs and to convey information to a non-technical audience.

 

Curriculum