1 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Department of Computer Science and Information Technology Master of Science (M.S.)

Professional Science Master’s (PSM) Information Sheet

The Department of Computer Science and Information Technology and the Department of Mathematics and Statistics offer a Master of Science (M.S.) degree in Computer Science and Quantitative Methods, and a Professional Science Master’s (PSM) degree, also in Computer Science and Quantitative Methods. There are nine areas of study (concentrations) available. • M.S. in Computer Science and Quantitative Methods (MS CSqm)

o Data Management and Analysis Concentration (available online) o Predictive Analytics Concentration (available online) o Information Assurance and Security Concentration - emphasizes on cybersecurity technical administration. o Mathematical Finance Concentration o Mathematics Instruction Concentration (M.S. Only, available hybrid and online)

• PSM in Computer Science and Quantitative Methods (PSM CSqm) o Data Management and Analysis Concentration (available online) o Predictive Analytics Concentration (available online) o Information Assurance and Security Concentration - emphasizes on cybersecurity technical administration. o Mathematical Finance Concentration

Our departments offer both online and on-campus M.S. and PSM degrees ideal for professionals in a wide variety of science and mathematics career fields. These master’s degree programs are designed to allow students to pursue advanced training in science or mathematics while developing workplace skills valued by employers. We aim to produce well-rounded science professionals who have a deep knowledge of their subject but also have the ability to communicate effectively and manage projects. Advisors are available to assist prospective students in choosing a concentration and planning a program that will lead to graduation. Information on all the programs in the Department is available at the Department’s Web site, www.apsu.edu/csci For more information, please contact one of the following: Dr. Jiang Li, Professor and Graduate Coordinator – for Data Management and Analysis Concentration Department of Computer Science & Information Technology Maynard 211, Austin Peay State University, P.O. Box 4414, Clarksville, Tennessee 37044, USA (931) 221- 7828; email: gradpsm@apsu.edu

Dr. Matthew Jones, Professor and Graduate Coordinator – for Predictive Analytics Concentration – for Mathematical Finance Concentration Department of Mathematics and Statistics Maynard 236, Austin Peay State University, P.O. Box 4626, Clarksville, Tennessee 37044, USA (931) 221- 7814; email: gradpsm@apsu.edu

Dr. Joseph V. Elarde, Associate Professor – for Information Assurance and Security Concentration Department of Computer Science & Information Technology Maynard 227, Austin Peay State University, P.O. Box 4414, Clarksville, Tennessee 37044, USA (931) 221-7301; email: gradpsm@apsu.edu

Dr. S. Jackie Vogel, Professor – for Mathematics Instruction Concentration Department of Mathematics and Statistics Maynard 122, Austin Peay State University, P.O. Box 4626, Clarksville, Tennessee 37044, USA (931) 221-7313; email: gradpsm@apsu.edu

2 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Master of Science (M.S.) Degree Computer Science and Quantitative Methods

Data Management and Analysis Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Concentration (21 hours) Course # Course Title Hrs CSCI 5010 Database Management Concepts 3 _____ CSCI 5015 Data Science in Python 3 _____ CSCI 5020 Data Management Applications 3 _____ CSCI 5040 Big Data Modeling and Management 3 _____ CSCI 5060 Database-Driven Web Development 3 _____ CSCI 5080 Data Mining Applications 3 _____ CSCI 5095 Data Mining Project 3 _____ C. Select one from (3 or 6 hours) Course # Course Title Hrs CSCI 5910 Master’s Systems Dev. Project 3/6 _____ CSCI 5920 Master’s Research Project / Thesis 3/6 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. CSCI 5010 - Database Management Concepts An introduction to database development process, database methods of file storage, primitive databases, and data warehousing. Topics include relational model approach to database management, concepts of network and object oriented model, use of SQL querying languages, and security and integrity policies in database management.

CSCI 5015 – Data Science in Python This course will introduce the learner to the basics of the python programming environment, as well as data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. CSCI 5020 - Data Management Applications - Prerequisite: CSCI 5010. An introduction to database features and administrator operations including components, instances, tables, indexes, and profiles using SQL Server and Oracle. Students will learn database management tools including database deployment, user support, change-control procedures, planning for growth, and technology evaluation. CSCI 5040 – Big Data Modeling and Management - Prerequisite: CSCI 5010. An introduction to the Big Data landscape including examples of real world big data problems, the architectural components and programming models used for scalable big data analysis, features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. CSCI 5060 - Database-Driven Web Development - Prerequisite: CSCI 5010 and CSCI 5020. This course introduces the development of web-based data management and information retrieval applications that connect to databases using server side programming languages. Topics also include configuration and maintenance of databases and web servers. Prerequisites: A student MUST have HTML and CSS skills and experience. A student MUST have client-side (JavaScript) web programming skills and experience. If not, the student must take CSCI 5005, and score a minimum “B” grade, before registering this course. Minimum “B” grade for CSCI 5010 and CSCI 5020. CSCI 5080 - Data Mining Applications - Prerequisite: CSCI 5010. This course introduces basic data mining concepts, applications, and techniques. Students will explore the process of data mining, learn various data mining methods including clustering, decision trees, association rules, statistical learning tools, and apply the techniques in solving practical problems using data mining systems. CSCI 5095 - Data Mining Project - Prerequisite: CSCI 5020. Students will work on an appropriate research project in data mining, and use practical data mining systems to discover patterns from real business data, and evaluate and interpret these mined patterns. CSCI 5910 - Master’s Systems Development Project - Prerequisite: CSCI 5095. A supervised computer system development program resulting in completion of a capstone project. CSCI 5920 - Master’s Research Project / Thesis - Prerequisite: CSCI 5095. A supervised computing research program resulting in completion of a capstone project or thesis. Plan for Course Offering Fall Matriculation (2 courses per semester) * Students needing to take CSCI 5005 should plan to do so during the summer prior to the first fall they are enrolled.

Fall Spring Summer Fall Spring Summer STAT5050 CSCI5010 CSCI5020 CSCI5060 CSCI5095 CSCI5910 / LDSP5100 CSCI 5015 CSCI 5040 CSCI5080 COMM5110 CSCI 5920

Fall Matriculation (3 courses per semester) * Students needing to take CSCI 5005 should plan to do so during the first summer they are enrolled.

Fall Spring Fall Spring CSCI 5010 CSCI 5015 CSCI 5060 CSCI 5010 / STAT 5050 CSCI 5020 CSCI 5080 CSCI 5920 LDSP 5100 CSCI 5040 CSCI 5095 COMM 5110

3 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Master of Science (M.S.) Degree Computer Science and Quantitative Methods

Predictive Analytics Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (9 or 12 hours) Course # Course Title Hrs STAT 5200 SAS Programming 3 _____ CSCI 5005 Introduction to Web Programming 3 _____ (may be waived) CSCI 5010 Database Management Concepts 3 _____ CSCI 5080 Data Mining Applications 3 _____ C. Predictive Analytics Concentration (15 or 18 hours) Course # Course Title Hrs MATH 5170 Finite Mathematics 3 _____ (may be waived) STAT 5120 Regression Analysis 3 _____ STAT 5125 The Generalized Linear Model 3 _____ STAT 5140 Time Series Analysis 3 _____ STAT 5290 Predictive Analytics 3 _____ STAT 5900 Master’s Internship Project 3 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. STAT 5200 – SAS Programming Creating and reading raw data files and SAS data sets, investigating and summarizing data, creating SAS variables and re-coding data values, combining multiple SAS files, creating listings and HTML summaries. CSCI 5005 - Introduction to Web Programming This survey course provides an introduction to web development and web programming by using the essential programming languages that power modern web pages. … The course primarily focuses on applying web programming

concepts such as variables, data types, operators, loops, arrays, control structures, functions and event handling. CSCI 5010 - Database Management Concepts - Prerequisite: CSCI 5005. An introduction to database development process, database methods of file storage, primitive databases, and data warehousing. Topics include relational model approach to database management, concepts of network and object oriented model, use of SQL querying languages, and security and integrity policies in database management. CSCI 5080 - Data Mining Applications - Prerequisite: CSCI 5010 and STAT 5200. This course introduces basic data mining concepts, applications, and techniques. Students will explore the process of data mining, learn various data mining methods including clustering, decision trees, association rules, statistical learning tools, and apply the techniques in solving practical problems using data mining systems. MATH 5170 - Finite Mathematics This course covers mathematical concepts required by students in graduate programs requiring quantitative skills. Topics covered include basic set theory and counting, functions, introduction to probability, interest and annuities, matrix theory, linear systems, and linear programming. STAT 5120 - Regression Analysis Analysis of variance and multiple comparisons, elementary regression models, multiple regression and the general linear model, logistic regression. STAT 5125 - The Generalized Linear Model - Prerequisite: STAT 5120. Topics include binomial and Poisson regression, overdispersion, negative binomial regression, nonparametric regression, random and fixed effects, repeated measures, survival analysis, and censored data. Appropriate statistical software (such as R or SAS). STAT 5140 - Time Series Analysis - Prerequisite: STAT 5120. This course covers methods for analyzing data collected over time. Topics include autoregressive-moving average models (MA, ARMA, ARIMA), exponential smoothing, model identification, estimation, diagnostic checking, forecasting. Appropriate statistical software (such as R or SAS) used throughout. STAT 5290 - Predictive Analytics - Prerequisite: STAT 5120. Advanced statistical techniques for analyzing large and high dimensional data. Topics include data mining strategy, data processing, predictive modeling techniques for decision making, model assessment and comparison. This course will be taught using appropriate statistical software. STAT 5900 - Master’s Internship Project - Prerequisite: CSCI 5095. This is a supervised internship course resulting in completion of a capstone project. Each student is assigned to an industry partner and works with this partner at least 15 hours per week for one semester on a project involving data-driven decision making. Plan for Course Offering Fall Matriculation * Students needing to take MATH 5170 or CSCI 5005 should plan to do so during the summer prior to the first fall they are enrolled.

Summer Fall Spring Summer Fall Spring Summer MATH5170* STAT5050 STAT5120 STAT5200 STAT5125 STAT5290 STAT5900 CSCI5005* LDSP5100 CSCI5010 COMM5110 CSCI5080 STAT5140

Spring Matriculation * Students matriculating in spring terms must receive approval to waive CSCI 5005, MATH 5170, and STAT 5050.

Spring Summer Fall Spring Summer STAT 5120 STAT 5200 STAT 5125 STAT 5290 STAT 5900 CSCI 5010 COMM 5110 CSCI 5080 STAT 5140

LDSP 5100

4 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Master of Science (M.S.) Degree Computer Science and Quantitative Methods

Information Assurance and Security Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below(with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (21 hours) B.1. (9 hours) Course # Course Title Hrs CSCI 5200 Principles of Information Security 3 _____ CSCI 5600 Computer Ethics 3 _____ CSCI 5603 Cryptography 3 _____ B.2. Select four from (12 hours) CSCI 5520 Network Security 3 _____ CSCI 5601 Computer Forensics and Incident 3 _____ Response CSCI 5602 Securing Cyberspace 3 _____ (Web, DB, Platform) CSCI 5607 IAS/Security Policy and Governance 3 _____ CSCI 5619 Ethical Hacking & Offensive Security 3 _____ CSCI 5624 System Vulnerability Analysis and 3 _____ Auditing CSCI 5625 Intrusion Detection and Prevention 3 _____ CSCI 5628 IAS/Defensive Programming 3 _____ CSCI 5630 IAS/Secure Software Engineering 3 _____ CSCI 5670 Network Applications 3 _____ C. Select one from (3 or 6 hours) Course # Course Title Hrs CSCI 5910 Master’s Systems Dev. Project 3/6 _____ CSCI 5920 Master’s Research Project / Thesis 3/6 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions.

STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. CSCI 5200 - Principles of Information Security An introduction to the technical and management aspects of information security. This course provides the foundation for understanding issues associated with security in computing, including security threats and controls, protection of computer, systems and data, and planning for security through the development of an information security strategy. CSCI 5600 - Computer Ethics Study and analysis of the social, legal and ethical issues that arise from the presence of computers in society. Problems are posed and solutions discussed from the view point of the computer professional. Topics include computer viruses, spyware, spam, life-critical systems and privacy issues. CSCI 5603 - Cryptography - Prerequisite: CSCI 5200, MATH 1710. This course introduces students to the methods of cryptography and cryptanalysis. Topics include classical cryptography, modern cryptographic techniques (symmetric key algorithms, asymmetric key algorithms), cryptographic hash functions, current and historical example uses of cryptography, and public key cryptography. CSCI 5520 - Network Security - Prerequisite: CSCI 5200. An examination of the tools techniques, and technologies used in the securing of information assets via networks. Topics covered include network operating system security, security of transmissions, firewall configuration, vulnerabilities and hardening of network components. Web and distributed system security, and procedures dealing with storage and disposition of sensitive data. CSCI 5601 – Computer Forensic and Incident Response - Prerequisite: CSCI 5200. This course is an introduction to the topics of computer forensics, incident response, cyber-crime and terrorism, cybercrime investigation and prosecution. Students will learn about computer forensics, extracting and proper handling of evidence, and how an organization can setup a security response team, prepare for and manage security incidents. CSCI 5602 – Securing Cyberspace (Web, DB, Platform) - Prerequisite: CSCI 5200. In this course, students will learn how to secure an organization's technological infrastructure, including topics on operating system platforms/hardware, virtual machines, mobile devices, web servers, database servers, additional network components, anti-malware, public facing applications, host-based intrusion detection/prevention, firewalls, and audit and compliance. Course includes laboratory work using Linux and Windows. CSCI 5607 – IAS/Security Policy and Governance - Prerequisite: CSCI 5200. This course covers policy development through monitoring and governance stages - policies such as privacy, acceptable use, physical security, breach disclosure, data collection and retention policies, cloud security, and supply chain are covered. Best-in-class methodologies will be used to create security policy that will communicate the organization's asset protection objectives. CSCI 5624 – System Vulnerability Analysis and Auditing - Prerequisite: CSCI 5200. This course covers the assessment of systems to discover resources that are vulnerable to intrusions and unauthorized access. The analysis of system vulnerability, identification of security deficiencies, security measurement, effectiveness and adequacy, and estimation of vulnerability of system resources to potential disaster hazards of unknown origin are also covered. CSCI 5625 - Intrusion Detection and Prevention - Prerequisite: CSCI 5200. This course covers an in-depth study of the theory and practice of intrusion detection and prevention in cyberspace. Topics include network security, monitoring, auditing, intrusion detection, intrusion prevention, and ethical penetration testing. Emphasis is on methods to identify threats and prevent attacks.

5 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

CSCI 5628 - IAS/Defensive Programming - Prerequisite: CSCI 5200, CSCI 2010. This course provides in-depth coverage of defensive programming techniques. Topics include: input validation and data sanitization, choice of programming language and type-safe languages, examples of common vulnerabilities and coding errors, and secure coding practices. CSCI 5630 - IAS/Secure Software Engineering - Prerequisite: CSCI 5200, CSCI 2010. This course covers the fundamentals of secure coding practices focusing on building security into the software development lifecycle. Topics include: software development lifecycle, secure design principles and patterns, secure software specifications and requirements, secure software development practices, and secure testing and quality assurance. CSCI 5670 - Network Applications - Prerequisite: CSCI 3700. This course introduces a variety of network applications and services. Topics covered include: WWW and HTTP, FTP, Telnet and SSH, Email (POP3 and SMTP), Usenet, P2P, VNC, remote access, chat room, cloud computing, and instant messaging service. CSCI 5910 - Master’s Systems Development Project - Prerequisite: CSCI 5095. A supervised computer system development program resulting in completion of a capstone project. CSCI 5920 - Master’s Research Project / Thesis - Prerequisite: CSCI 5095. A supervised computing research program resulting in completion of a capstone project or thesis.

6 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Master of Science (M.S.) Degree Computer Science and Quantitative Methods

Mathematical Finance Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (15 hours) Course # Course Title Hrs MATH 5130 Financial Mathematics 3 _____ MATH 5140 Financial Derivatives 3 _____ MATH 5220 Computational Methods in Finance 3 _____ MATH 5260 Stochastic Processes 3 _____ MATH 5900 Master’s Internship Project 3 _____ C. Electives, select two from (6 hours) Course # Course Title Hrs MATH 5460 Applied Mathematics 3 _____ MATH 5670 Numerical Analysis 3 _____ STAT 5125 The Generalized Linear Model 3 _____ STAT 5200 SAS Programming 3 _____ CSCI 5080 Data Mining Applications 3 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. MATH 5130 – Financial Mathematics Prepares students for actuarial exam 2/FM: present and accumulated values of cash flows, reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, valuing of contingent cash flows, and financial instruments including derivatives and arbitrage-free pricing.

MATH 5140 - Financial Derivatives - Prerequisite: MATH 5130 and MATH 5240. This course covers introductory financial derivatives, general properties of options, the binomial option pricing model, the Black-Scholes option pricing model, Greek options, risk management, and interest rate derivatives. This course prepares students for actuarial exam 3F/MFE. MATH 5220 - Computational Methods in Finance - Prerequisite: MATH 5130. This course covers comprehensively Monte-Carlo simulations for applications in finance. Topics include generation of pseudo- and quasi- random numbers, trees, variance reduction techniques and finite differences. MATH 5260 - Stochastic Processes - Prerequisite: MATH 4240 or STAT 4240 or MATH 5240 or STAT 5240. An introduction to stochastic processes with their applications: Poisson and compound Poisson processes; discrete and continuous time Markov chains; renewal theory; random walks and Brownian motion. MATH 5900 - Master’s Internship Project- Prerequisite: MATH 5140 and MATH 5220 and Dept Chair Approval. Permission of the Department Chair. A supervised internship program resulting in completion of a capstone project. MATH 5460 - Applied Mathematics - Prerequisite: MATH 2110 and MATH 3120 and MATH 3450. Analysis and solution of mathematical problems arising from scientific and industrial settings includingmathematical models requiring differential equations. Writing and presentation of mathematical models and solutions. MATH 5670 - Numerical Analysis Digital computer programming, finite differences, numerical integration, matrix computations, numerical solutions of non-linear systems and differential equations. STAT 5125 - The Generalized Linear Model - Prerequisite: STAT 5120 or STAT 4120. Topics include binomial and Poisson regression, overdispersion, negative binomial regression, nonparametric regression, random and fixed effects, repeated measures, survival analysis, and censored data. Appropriate statistical software (such as R or SAS). STAT 5200 - SAS Programming Creating and reading raw data files and SAS data sets, investigating and summarizing data, creating SAS variables and re-coding data values, combining multiple SAS files, creating listings and HTML summaries. CSCI 5080 - Data Mining Applications - Prerequisite: CSCI 5010 and STAT 5200. This course introduces basic data mining concepts, applications, and techniques. Students will explore the process of data mining, learn various data mining methods including clustering, decision trees, association rules, statistical learning tools, and apply the techniques in solving practical problems using data mining systems. Plan for Course Offering Fall Matriculation

* MATH 5670 and STAT 5200 are electives. Fall Spring Summer Fall Spring

STAT 5050 STAT 5260 COMM 5110 MATH 5900 MATH 5670* STAT 5200* MATH 5220 LDSP 5100 MATH 5130 MATH 5140

7 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Master of Science (M.S.) Degree Computer Science and Quantitative Methods

Mathematics Instruction Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete either an Action Research Project or Comprehensive exam. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (15 hours) Course # Course Title Hrs MATH 5520 Algebra Advanced Perspective 3 _____ MATH 5640 Geometry Advanced Perspective 3 _____ MATH 5350 Calculus Advanced Perspective 3 _____ C. Mathematics Instruction Emphasis Option 1 (12 hours) Non-College Instruction Path, Research Project Required C.1. (6 hours) Course # Course Title Hrs MATH 5090 Scientific Writing in Mathematics 3 _____ MATH 5940 Research in Mathematics 3 _____ C.2. Select two from (6 hours) MATH 5070 Methods, Materials, Strategies Tch. 3 _____ Math MATH 5080 Mathematics in a Tech. World 3 _____ MATH 5120 Contemporary Approaches to 3 _____ Problem Solving and Proof MATH 5170 Finite Mathematics 3 _____ D. Mathematics Instruction Emphasis Option 2 (12 hours) General Education College Instruction Path,

Comprehensive Exam Required D.1. Select four from (12 hours) MATH 5110 Number Theory 3 _____ MATH 5120 Contemporary Approaches to 3 _____ Problem Solving and Proof MATH 5170 Finite Mathematics 3 _____ MATH 5210 Topology 3 _____ MATH 5260 Stochastic Process 3 _____ MATH 5460 Applied Mathematics 3 _____ MATH 5670 Numerical Analysis 3 _____ STAT 5200 SAS Programming 3 _____ CSCI 5005 Introduction to Web Programming 3 _____

Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. MATH 5520 - Algebra from an Advanced Perspective This course assumes an undergraduate math major with a proof course. Indepth coverage of the following topics: real and complex numbers, functions, equations, divisibility, congruence, isomorphisms, groups, fields, proof, and more. MATH 5640 - Geometry from an Advanced Perspective This course is an advanced treatment of geometry. It presents geometry with connections to algebra and analysis through a study of transformational geometry, Euclidean geometry, and trigonometry. Topics include congruence, deduction, proof, transformations, postulate, and more. MATH 5350 - Calculus from an Advanced Perspective This course provides current and prospective high school teachers with an advanced treatment of calculus. The course content parallels the Advanced Placement (AP) Calculus syllabus, providing an indepth and rigorous treatment of the major calculus along with pedagogical insight from the growing body of research on teaching and learning calculus. MATH 5090 - Scientific Writing in Mathematics Seminar-type course which examines current research related to mathematics and developing students’ writing skills in preparing reports, articles, and other professional communications. MATH 5940 - Research in Mathematics - Prerequisite: Dept Chair Approval. This course is a field project in mathematics education to be determined by the student and the chair of the student’s graduate committee. The course should be taken at the end of the program of study. MATH 5070 - Methods, Materials and Strategies in Teaching Mathematics Discussion of methods, aids, and materials used in teaching mathematics and strategies for their use. MATH 5080 - Mathematics in a Technological World This course will explore the application of recent technologies to mathematical problem solving. It will further investigate the use of these technologies in teaching mathematics. MATH 5120 - Contemporary Approaches to Problem Solving and Proof This course promotes incorporating technology into problem solving and proof with an emphasis on combinatorics, probability, and other topics. MATH 5170 - Finite Mathematics Basic probability and matrix algebra with applications. MATH 5210 - Topology Sets, metric spaces, limits, continuous maps and homeomorphisms, connectedness and compact topological spaces.

8 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

MATH 5260 - Stochastic Processes An introduction to stochastic processes with their applications: Poisson and compound Poisson processes; discrete and continuous time Markov chains; renewal theory; random walks and Brownian motion. MATH 5460 - Applied Mathematics Analysis and solution of mathematical problems arising from scientific and industrial settings including mathematical models requiring differential equations. Writing and presentation of mathematical models and solutions. MATH 5670 - Numerical Analysis Digital computer programming, finite differences, numerical integration, matrix computations, numerical solutions of non-linear systems and differential equations. STAT 5200 – SAS Programming Creating and reading raw data files and SAS data sets, investigating and summarizing data, creating SAS variables and re-coding data values, combining multiple SAS files, creating listings and HTML summaries. CSCI 5005 - Introduction to Web Programming This survey course provides an introduction to web development and web programming by using the essential programming languages that power modern web pages. … The course primarily focuses on applying web programming concepts such as variables, data types, operators, loops, arrays, control structures, functions and event handling. Plan for Course Offering Summer Matriculation * Or other mathematics electives. ** Required for Option 1 only. Summer Odd Fall Odd Spring Even Summer Even Fall Even Spring Odd MATH5520 STAT5050 MATH5120* MATH5350 MATH5090** MATH5940** MATH5640 MATH5070* LDSP5100 COMM5110

9 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Professional Science Master’s (PSM) Degree Computer Science and Quantitative Methods

Data Management and Analysis Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (9 or 12 hours) Course # Course Title Hrs STAT 5200 SAS Programming 3 _____ CSCI 5005 Introduction to Web Programming 3 _____ (may be waived) CSCI 5010 Database Management Concepts 3 _____ CSCI 5080 Data Mining Applications 3 _____ C. Data Management & Analysis Concentration (12 hours) Course # Course Title Hrs CSCI 5020 Data Management Applications 3 _____ CSCI 5060 Database-Driven Web Development 3 _____ CSCI 5095 Data Mining Project 3 _____ CSCI 5900 Master’s Internship Project 3 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. STAT 5200 – SAS Programming Creating and reading raw data files and SAS data sets, investigating and summarizing data, creating SAS variables and re-coding data values, combining multiple SAS files, creating listings and HTML summaries. CSCI 5005 - Introduction to Web Programming

This survey course provides an introduction to web development and web programming by using the essential programming languages that power modern web pages. … The course primarily focuses on applying web programming concepts such as variables, data types, operators, loops, arrays, control structures, functions and event handling. CSCI 5010 - Database Management Concepts - Prerequisite: CSCI 5005. An introduction to database development process, database methods of file storage, primitive databases, and data warehousing. Topics include relational model approach to database management, concepts of network and object oriented model, use of SQL querying languages, and security and integrity policies in database management. CSCI 5020 - Data Management Applications - Prerequisite: CSCI 5010. An introduction to database features and administrator operations including components, instances, tables, indexes, and profiles using SQL Server and Oracle. Students will learn database management tools including database deployment, user support, change-control procedures, planning for growth, and technology evaluation. CSCI 5060 - Database-Driven Web Development - Prerequisite: CSCI 5020 This course introduces the development of web-based data management and information retrieval applications that connect to databases using server side programming languages. Topics also include configuration and maintenance of databases and web servers. Prerequisites: A student MUST have HTML and CSS skills and experience. A student MUST have client-side (JavaScript) web programming skills and experience. If not, the student must take CSCI 5005, and score a minimum “B” grade, before registering this course. Minimum “B” grade for CSCI 5010 and CSCI 5020. CSCI 5080 - Data Mining Applications - Prerequisite: CSCI 5010 and STAT 5200. This course introduces basic data mining concepts, applications, and techniques. Students will explore the process of data mining, learn various data mining methods including clustering, decision trees, association rules, statistical learning tools, and apply the techniques in solving practical problems using data mining systems. CSCI 5095 - Data Mining Project - Prerequisite: CSCI 5060, CSCI 5080. Students will work on an appropriate research project in data mining, and use practical data mining systems to discover patterns from real business data, and evaluate and interpret these mined patterns. CSCI 5900 - Master’s Internship Project - Prerequisite: CSCI 5095. This is a supervised internship course resulting in completion of a capstone project. Each student is assigned to an industry partner and works with this partner at least 15 hours per week for one semester on a project involving data-driven decision making. Plan for Course Offering Fall Matriculation * Students needing to take CSCI 5005 should plan to do so during the summer prior to the first fall they are enrolled.

Summer Fall Spring Summer Fall Spring Summer CSCI5005* STAT5050 CSCI5010 CSCI5020 CSCI5060 CSCI5095 CSCI5900

LDSP5100 STAT5200 CSCI5080 COMM5110 Spring Matriculation * Students needing to take CSCI 5005 should plan to do so during the first summer they are enrolled.

Spring Summer Fall Spring Summer CSCI 5010 CSCI 5020 CSCI 5060 CSCI 5095 CSCI 5900 STAT 5050 STAT 5200 CSCI 5080 COMM 5110

CSCI 5005* LDSP 5100

10 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Professional Science Master’s (PSM) Degree Computer Science and Quantitative Methods

Predictive Analytics Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (9 or 12 hours) Course # Course Title Hrs STAT 5200 SAS Programming 3 _____ CSCI 5005 Introduction to Web Programming 3 _____ (may be waived) CSCI 5010 Database Management Concepts 3 _____ CSCI 5080 Data Mining Applications 3 _____ C. Predictive Analytics Concentration (15 or 18 hours) Course # Course Title Hrs MATH 5170 Finite Mathematics 3 _____ (may be waived) STAT 5120 Regression Analysis 3 _____ STAT 5125 The Generalized Linear Model 3 _____ STAT 5140 Time Series Analysis 3 _____ STAT 5290 Predictive Analytics 3 _____ STAT 5900 Master’s Internship Project 3 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. STAT 5200 – SAS Programming Creating and reading raw data files and SAS data sets, investigating and summarizing data, creating SAS variables and re-coding data values, combining multiple SAS files, creating listings and HTML summaries. CSCI 5005 - Introduction to Web Programming This survey course provides an introduction to web development and web programming by using the essential programming languages that power modern web pages. … The course primarily focuses on applying web programming

concepts such as variables, data types, operators, loops, arrays, control structures, functions and event handling. CSCI 5010 - Database Management Concepts - Prerequisite: CSCI 5005. An introduction to database development process, database methods of file storage, primitive databases, and data warehousing. Topics include relational model approach to database management, concepts of network and object oriented model, use of SQL querying languages, and security and integrity policies in database management. CSCI 5080 - Data Mining Applications - Prerequisite: CSCI 5010 and STAT 5200. This course introduces basic data mining concepts, applications, and techniques. Students will explore the process of data mining, learn various data mining methods including clustering, decision trees, association rules, statistical learning tools, and apply the techniques in solving practical problems using data mining systems. MATH 5170 - Finite Mathematics This course covers mathematical concepts required by students in graduate programs requiring quantitative skills. Topics covered include basic set theory and counting, functions, introduction to probability, interest and annuities, matrix theory, linear systems, and linear programming. STAT 5120 - Regression Analysis Analysis of variance and multiple comparisons, elementary regression models, multiple regression and the general linear model, logistic regression. STAT 5125 - The Generalized Linear Model - Prerequisite: STAT 5120. Topics include binomial and Poisson regression, overdispersion, negative binomial regression, nonparametric regression, random and fixed effects, repeated measures, survival analysis, and censored data. Appropriate statistical software (such as R or SAS). STAT 5140 - Time Series Analysis - Prerequisite: STAT 5120. This course covers methods for analyzing data collected over time. Topics include autoregressive-moving average models (MA, ARMA, ARIMA), exponential smoothing, model identification, estimation, diagnostic checking, forecasting. Appropriate statistical software (such as R or SAS) used throughout. STAT 5290 - Predictive Analytics - Prerequisite: STAT 5120. Advanced statistical techniques for analyzing large and high dimensional data. Topics include data mining strategy, data processing, predictive modeling techniques for decision making, model assessment and comparison. This course will be taught using appropriate statistical software. STAT 5900 - Master’s Internship Project - Prerequisite: CSCI 5095. This is a supervised internship course resulting in completion of a capstone project. Each student is assigned to an industry partner and works with this partner at least 15 hours per week for one semester on a project involving data-driven decision making. Plan for Course Offering Fall Matriculation * Students needing to take MATH 5170 or CSCI 5005 should plan to do so during the summer prior to the first fall they are enrolled.

Summer Fall Spring Summer Fall Spring Summer MATH5170* STAT5050 STAT5120 STAT5200 STAT5125 STAT5290 STAT5900 CSCI5005* LDSP5100 CSCI5010 COMM5110 CSCI5080 STAT5140

Spring Matriculation * Students matriculating in spring terms must receive approval to waive CSCI 5005, MATH 5170, and STAT 5050.

Spring Summer Fall Spring Summer STAT 5120 STAT 5200 STAT 5125 STAT 5290 STAT 5900 CSCI 5010 COMM 5110 CSCI 5080 STAT 5140

LDSP 5100

11 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Professional Science Master’s (PSM) Degree Computer Science and Quantitative Methods

Information Assurance and Security Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below(with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (9 or 12 hours) Course # Course Title Hrs CSCI 5005 Introduction to Web Programming 3 _____ (may be waived) CSCI 5200 Principles of Information Security 3 _____ CSCI 5520 Network Security 3 _____ CSCI 5625 Intrusion Detection and Prevention 3 _____ C. Cybersecurity Technical Administration Emphasis (12 hours) C.1. Select three from (9 hours) Course # Course Title Hrs CSCI 5601 Computer Forensics and Incident 3 _____ Response CSCI 5602 Securing Cyberspace (Web, DB, 3 _____ Platform) CSCI 5607 IAS/Security Policy and Governance 3 _____ CSCI 5624 System Vulnerability Analysis and 3 _____ Auditing C.2. (3 hours) CSCI 5900 Master’s Internship Project 3 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances.

CSCI 5005 - Introduction to Web Programming This survey course provides an introduction to web development and web programming by using the essential programming languages that power modern web pages. … The course primarily focuses on applying web programming concepts such as variables, data types, operators, loops, arrays, control structures, functions and event handling. CSCI 5200 - Principles of Information Security An introduction to the technical and management aspects of information security. This course provides the foundation for understanding issues associated with security in computing, including security threats and controls, protection of computer, systems and data, and planning for security through the development of an information security strategy. CSCI 5520 - Network Security - Prerequisite: CSCI 5200. An examination of the tools techniques, and technologies used in the securing of information assets via networks. Topics covered include network operating system security, security of transmissions, firewall configuration, vulnerabilities and hardening of network components. Web and distributed system security, and procedures dealing with storage and disposition of sensitive data. CSCI 5625 - Intrusion Detection and Prevention - Prerequisite: CSCI 5200. This course covers an in-depth study of the theory and practice of intrusion detection and prevention in cyberspace. Topics include network security, monitoring, auditing, intrusion detection, intrusion prevention, and ethical penetration testing. Emphasis is on methods to identify threats and prevent attacks. CSCI 5601 – Computer Forensic and Incident Response - Prerequisite: CSCI 5200. This course is an introduction to the topics of computer forensics, incident response, cyber-crime and terrorism, cybercrime investigation and prosecution. Students will learn about computer forensics, extracting and proper handling of evidence, and how an organization can setup a security response team, prepare for and manage security incidents. CSCI 5602 – Securing Cyberspace (Web, DB, Platform) - Prerequisite: CSCI 5200. In this course, students will learn how to secure an organization's technological infrastructure, including topics on operating system platforms/hardware, virtual machines, mobile devices, web servers, database servers, additional network components, anti-malware, public facing applications, host-based intrusion detection/prevention, firewalls, and audit and compliance. Course includes laboratory work using Linux and Windows. CSCI 5607 – IAS/Security Policy and Governance - Prerequisite: CSCI 5200. This course covers policy development through monitoring and governance stages - policies such as privacy, acceptable use, physical security, breach disclosure, data collection and retention policies, cloud security, and supply chain are covered. Best-in-class methodologies will be used to create security policy that will communicate the organization's asset protection objectives. CSCI 5624 – System Vulnerability Analysis and Auditing - Prerequisite: CSCI 5200. This course covers the assessment of systems to discover resources that are vulnerable to intrusions and unauthorized access. The analysis of system vulnerability, identification of security deficiencies, security measurement, effectiveness and adequacy, and estimation of vulnerability of system resources to potential disaster hazards of unknown origin are also covered. CSCI 5900 - Master’s Internship Project - Prerequisite: CSCI 5095. This is a supervised internship course resulting in completion of a capstone project. Each student is assigned to an industry partner and works with this partner at least 15 hours per week for one semester on a project involving data-driven decision making.

Plan for Course Offering Fall Matriculation * Students needing to take CSCI 5005 should plan to do so during the summer prior to the first fall they are enrolled.

Fall Spring Fall Spring CSCI 5200 LPSD 5100 STAT 5050 CSCI5900

COMM 5110 CSCI 5625 CSCI 5602 CSCI 5520 CSCI 5601 CSCI 5607 or 5624

Spring Matriculation * Students needing to take CSCI 5005 should plan to do so during the first summer they are enrolled.

Spring Fall Spring Fall CSCI 5200 LPSD 5100 STAT 5050 CSCI5900

COMM 5110 CSCI 5625 CSCI 5602 CSCI 5520 CSCI 5601 CSCI 5607 or 5624

12 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Professional Science Master’s (PSM) Degree Computer Science and Quantitative Methods

Mathematical Finance Concentration Graduation Checklist ___ Complete the General Core. ___ Complete the Major Field Core. ___ Complete the Concentration Courses. ___ Maintain a GPA of 3.0 or better. ___ Complete all credit hours below (with a GPA of 3.0 or better). A. General Core (9 hours) Course # Course Title Hrs COMM 5110 Leadership and Communication 3 _____ LDSP 5100 Leadership for the Scientist-Manager 3 _____ STAT 5050 Probabilistic & Statistical Reasoning 3 _____ B. Major Field Core (15 hours) Course # Course Title Hrs MATH 5130 Financial Mathematics 3 _____ MATH 5140 Financial Derivatives 3 _____ MATH 5220 Computational Methods in Finance 3 _____ MATH 5260 Stochastic Processes 3 _____ MATH 5900 Master’s Internship Project 3 _____ C. Electives, select two from (6 hours) Course # Course Title Hrs MATH 5460 Applied Mathematics 3 _____ MATH 5670 Numerical Analysis 3 _____ STAT 5125 The Generalized Linear Model 3 _____ STAT 5200 SAS Programming 3 _____ CSCI 5080 Data Mining Applications 3 _____ Course Descriptions COMM 5110 - Leadership and Communication This course focuses on leadership as a function of communication behavior. Through discussion, cases and exercises, participants will explore effective communication strategies within an organizational setting. The course will cover team leadership skills, rhetorical sensitivity, charisma and practical suggestions for improving leadership effectiveness. LDSP 5100 - Leadership for the Scientist-Manager The course addresses the interplay between management and leadership with emphasis on management topics such as hiring, motivating, and appraising employees, leading change and ethical implications of leadership actions. STAT 5050 - Probabilistic & Statistical Reasoning Measures of central tendency and spread, probability distributions, conditional probability and independence, expectation, confidence intervals and hypothesis tests for means, proportions, and variances. MATH 5130 – Financial Mathematics Prepares students for actuarial exam 2/FM: present and accumulated values of cash flows, reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, valuing of contingent cash flows, and financial instruments including derivatives and arbitrage-free pricing.

MATH 5140 - Financial Derivatives - Prerequisite: MATH 5130 and MATH 5240. This course covers introductory financial derivatives, general properties of options, the binomial option pricing model, the Black-Scholes option pricing model, Greek options, risk management, and interest rate derivatives. This course prepares students for actuarial exam 3F/MFE. MATH 5220 - Computational Methods in Finance - Prerequisite: MATH 5130. This course covers comprehensively Monte-Carlo simulations for applications in finance. Topics include generation of pseudo- and quasi- random numbers, trees, variance reduction techniques and finite differences. MATH 5260 - Stochastic Processes - Prerequisite: MATH 4240 or STAT 4240 or MATH 5240 or STAT 5240. An introduction to stochastic processes with their applications: Poisson and compound Poisson processes; discrete and continuous time Markov chains; renewal theory; random walks and Brownian motion. MATH 5900 - Master’s Internship Project- Prerequisite: MATH 5140 and MATH 5220 and Dept Chair Approval. Permission of the Department Chair. A supervised internship program resulting in completion of a capstone project. MATH 5460 - Applied Mathematics - Prerequisite: MATH 2110 and MATH 3120 and MATH 3450. Analysis and solution of mathematical problems arising from scientific and industrial settings includingmathematical models requiring differential equations. Writing and presentation of mathematical models and solutions. MATH 5670 - Numerical Analysis Digital computer programming, finite differences, numerical integration, matrix computations, numerical solutions of non-linear systems and differential equations. STAT 5125 - The Generalized Linear Model - Prerequisite: STAT 5120 or STAT 4120. Topics include binomial and Poisson regression, overdispersion, negative binomial regression, nonparametric regression, random and fixed effects, repeated measures, survival analysis, and censored data. Appropriate statistical software (such as R or SAS). STAT 5200 - SAS Programming Creating and reading raw data files and SAS data sets, investigating and summarizing data, creating SAS variables and re-coding data values, combining multiple SAS files, creating listings and HTML summaries. CSCI 5080 - Data Mining Applications - Prerequisite: CSCI 5010 and STAT 5200. This course introduces basic data mining concepts, applications, and techniques. Students will explore the process of data mining, learn various data mining methods including clustering, decision trees, association rules, statistical learning tools, and apply the techniques in solving practical problems using data mining systems. Plan for Course Offering Fall Matriculation

* MATH 5670 and STAT 5200 are electives. Fall Spring Summer Fall Spring

STAT 5050 STAT 5260 COMM 5110 MATH 5900 MATH 5670* STAT 5200* MATH 5220 LDSP 5100 MATH 5130 MATH 5140

13 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Master of Science (M.S.) in Computer Science and Quantitative Methods Data Management and Analysis Concentration Prerequisite Chart Coming soon… Master of Science (M.S.) in Computer Science and Quantitative Methods Predictive Analytics Concentration Prerequisite Chart

CSCI 5005 (may be waived) Intro. to Web Programming

CSCI 5010 Database Management Concepts

STAT 5120 Regression Analysis

STAT 5290 Predictive Analytics

CSCI 5080 Data Mining Applications

STAT 5125 The Generalized Linear Model

STAT 5900 Master’s Internship Project

LDSP 5100 Leadership for the Scientist-Manager

COMM 5110 Leadership and Communication

STAT 5200 SAS Programming

STAT 5050 Probabilistic & Statistical Reasoning

MATH 5170 (may be waived) Finite Mathematics

STAT 5140 Time Series Analysis

14 Department of Computer Science and Information Technology Austin Peay State University, Tennessee, USA

* This page is for informational purposes only. Changes to your degree plan or updates to the bulletin may change the list of required classes. You should regularly check with a Computer Science advisor for up-to-date degree requirements and to ensure timely graduation. Last updated: October 25, 2017

Professional Science Master’s (PSM) degree in Computer Science and Quantitative Methods Data Management and Analysis Concentration Prerequisite Chart

Professional Science Master’s (PSM) degree in Computer Science and Quantitative Methods Predictive Analytics Concentration Prerequisite Chart

CSCI 5005 (may be waived) Intro. to Web Programming

CSCI 5010 Database Management Concepts

CSCI 5020 Data Management Applications

CSCI 5060 DB-Driven Web Development

CSCI 5080 Data Mining Applications

CSCI 5095 Data Mining Project

CSCI 5900 Master’s Internship Project

LDSP 5100 Leadership for the Scientist-Manager

COMM 5110 Leadership and Communication

STAT 5050 Probabilistic & Statistical Reasoning

STAT 5200 SAS Programming

CSCI 5005 (may be waived) Intro. to Web Programming

CSCI 5010 Database Management Concepts

STAT 5120 Regression Analysis

STAT 5290 Predictive Analytics

CSCI 5080 Data Mining Applications

STAT 5125 The Generalized Linear Model

STAT 5900 Master’s Internship Project

LDSP 5100 Leadership for the Scientist-Manager

COMM 5110 Leadership and Communication

STAT 5200 SAS Programming

STAT 5050 Probabilistic & Statistical Reasoning

MATH 5170 (may be waived) Finite Mathematics

STAT 5140 Time Series Analysis