Course Offerings

Below are courses offered in Computer and Information Sciences. For up-to-date information regarding course information for registration, please visit Murphy Online

Spring 2014 Courses

Spring 2014 Courses
Course - Section Title Days Time Location
CISC 120 - 01 Computers in Elementary Educ M - W - F 1335 - 1440 OSS 428
CRN: 20771 4 Credit Hours Instructor: Mark E. Werness (Formerly QMCS 120) This course is intended for elementary education majors. Topics include the role of the computer in elementary and middle-school education, computer applications in science and mathematics, data analysis, software packages for use in elementary and middle-school classrooms, Computer-Assisted-Instruction (CAI), multimedia, electronic portfolios, telecommunication and software creation using tools such as MicroWorlds, Scratch, and HTML. This course fulfills the third course in the Natural Science and Mathematical and Quantative Reasoning. Prerequisite: Elementary Education or SMEE major
CISC 130 - 01 Intro-Program&Prob Solving-Sci - T - - - 0800 - 0940 OSS 432
CRN: 20772 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 01 Intro-Program&Prob Solving-Sci M - W - F 0815 - 0920 OSS 432
CRN: 20772 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 02 Intro-Program&Prob Solving-Sci - - - R - 0800 - 0940 OSS 432
CRN: 20773 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 02 Intro-Program&Prob Solving-Sci M - W - F 0935 - 1040 OSS 432
CRN: 20773 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 03 Intro-Program&Prob Solving-Sci - T - - - 0955 - 1135 OSS 431
CRN: 20879 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 03 Intro-Program&Prob Solving-Sci M - W - F 1335 - 1440 OSS 431
CRN: 20879 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 04 Intro-Program&Prob Solving-Sci - - - R - 0955 - 1135 OSS 431
CRN: 20877 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 04 Intro-Program&Prob Solving-Sci M - W - F 1055 - 1200 OSS 432
CRN: 20877 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 05 Intro-Program&Prob Solving-Sci - T - R - 1730 - 2015 OSS 428
CRN: 21074 4 Credit Hours Instructor: William J. Tuohy (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 131 - 01 Intro-Programming&Prob Solving - T - R - 0800 - 0940 OSS 428
CRN: 20774 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 130 and 230) Introduction to problem solving with computers: logical thinking, design and implementation of algorithms, and basic programming structures. Problems will be motivated by the computer science and management information science disciplines. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students with majors the Department of Computer and Information Sciences. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 131 - 01 Intro-Programming&Prob Solving M - W - - 0815 - 0920 OSS 428
CRN: 20774 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 130 and 230) Introduction to problem solving with computers: logical thinking, design and implementation of algorithms, and basic programming structures. Problems will be motivated by the computer science and management information science disciplines. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students with majors the Department of Computer and Information Sciences. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 200 - 01 Intro-Computer Tech & Bus Appl - T - R - 1330 - 1510 OSS 431
CRN: 20775 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. Through application of basic computing fundamentals, students will be better prepared to purchase computers, diagnose and solve computer problems, use and build local area network/home networks, use and build common software applications, and design simple web pages. Student teams will transfer concepts and skills learned in the course as they assist organizations in the community with their technology needs. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 200 - 02 Intro-Computer Tech & Bus Appl - T - R - 1525 - 1700 OSS 432
CRN: 20776 4 Credit Hours Instructor: Hesham S. Saadawi (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. Through application of basic computing fundamentals, students will be better prepared to purchase computers, diagnose and solve computer problems, use and build local area network/home networks, use and build common software applications, and design simple web pages. Student teams will transfer concepts and skills learned in the course as they assist organizations in the community with their technology needs. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 200 - 03 Intro-Computer Tech & Bus Appl M - W - - 1525 - 1700 OSS 432
CRN: 21639 4 Credit Hours Instructor: Hesham S. Saadawi (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. Through application of basic computing fundamentals, students will be better prepared to purchase computers, diagnose and solve computer problems, use and build local area network/home networks, use and build common software applications, and design simple web pages. Student teams will transfer concepts and skills learned in the course as they assist organizations in the community with their technology needs. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 200 - 04 Intro-Computer Tech & Bus Appl - T - R - 0955 - 1135 OSS 432
CRN: 22940 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. Through application of basic computing fundamentals, students will be better prepared to purchase computers, diagnose and solve computer problems, use and build local area network/home networks, use and build common software applications, and design simple web pages. Student teams will transfer concepts and skills learned in the course as they assist organizations in the community with their technology needs. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 210 - 01 Information Security M - W - - 1335 - 1510 OSS 432
CRN: 20807 4 Credit Hours Instructor: Scott C. Yilek An introductory course in computer and network security including desktop security, LAN security, and large-scale system security. Topics include authentication, host-based access control, encryption, network access control, and network security protocols. These topics will be analyzed in the context of system requirements, security policy, and risk assessment. Prerequisites: 1) MATH 128 or ENGR 230 or STAT 220(IDTH 220) (may be taken concurrently), and 2) a minimum grade of C- in CISC 130 or 131
CISC 230 - 01 Object Oriented Design & Prog M - W - - 0935 - 1040 OSS 428
CRN: 20777 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 281) Programming and problem solving using an object-oriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from object-oriented design, modeling, algorithms, classes, objects, behavior, state, class associations and hierarchies, polymorphism, inheritance, design requirements and representation, Uniform Modeling Language specification, testing and verification, file processing. Lab included. Prerequisites: A minimum grade of C- in CISC 130 or 131
CISC 230 - 01 Object Oriented Design & Prog - T - R - 0955 - 1135 OSS 428
CRN: 20777 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 281) Programming and problem solving using an object-oriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from object-oriented design, modeling, algorithms, classes, objects, behavior, state, class associations and hierarchies, polymorphism, inheritance, design requirements and representation, Uniform Modeling Language specification, testing and verification, file processing. Lab included. Prerequisites: A minimum grade of C- in CISC 130 or 131
CISC 320 - 01 Systems Analysis and Design I M - W - - 1525 - 1700 OSS 428
CRN: 20782 4 Credit Hours Instructor: Timothy G. Meyer (Formerly QMCS 420) A study of systems analysis methodologies used in the analysis and design of information systems. Emphasis on data, process, and modeling by use of a CASE tool: entity relationship diagrams and data normalization, data flow diagrams, use case diagrams, and data dictionaries. This is a "hands on" course where students form teams to analyze the needs of a business client in the community. Prerequisite: CISC 130 or 131; junior standing
CISC 321 - 01 Systems Analysis and Design II - - - - - -
CRN: 23115 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 421) Continuation of CISC 320. Concentration on user-centered design (UCD), physical design, low- and high- fidelity prototyping, and agile methods. Emphasis on managerial problems in systems development. Continued use of CASE and project-management tools. A "real world" design and prototyping project is an integral part of this course. Prerequisite: CISC 320
CISC 325 - 01 E-Commerce M - W - F 1055 - 1200 OSS 428
CRN: 20778 4 Credit Hours Instructor: Hesham S. Saadawi (Formerly QMCS 425) A study of relevant technologies and how they are used in today's modern organizations to help manage the information resource of the organization. Emphasis is placed on the use of the Internet and World Wide Web and how they have changed organizational operations and strategies. This is an "active learning" course in which students will be researching current information systems technologies (such as Electronic Commerce [e-commerce]) and will be participating in the design and development of an e-commerce website for a fictitious organization. Prerequisite: CISC 130 or 131; junior standing
CISC 340 - 01 Computer Architecture M - W - F 1215 - 1320 OSS 432
CRN: 20840 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 300 and 340) Structure and organization of computer systems and components, including the design of central processors, memory, and input/output systems. Instruction sets and basic machine language programming. Prerequisites: A minimum grade of C- in CISC 230
CISC 370 - 01 Computer Networking - T - R - 1525 - 1700 OSS 431
CRN: 20779 4 Credit Hours Instructor: Timothy J. Salo (Formerly QMCS 370) An introduction to computer networking. Covers communication protocol concepts, local area networks, Internet protocols, firewalls, and network security. Prerequisites: A minimum grade of C- in CISC 230
CISC 419 - 01 Accounting Information Systems M - W - - 1730 - 1915 OSS 431
CRN: 20780 4 Credit Hours Instructor: Suzette Allaire (Formerly QMCS 419) This course will provide an understanding of the conceptual framework and practices of accounting information systems and the ability to work effectively with computer specialists and management to design, implement and audit such systems. Examples of subjects included are: systems development life cycle (SDLC), systems analysis phase of the SDLC, data and process models, operations of a corporate data center, including internal controls, database integrity, audit considerations for both internal and external auditors, unit integration, and system testing. Prerequisites: CISC 110 or 200, and previous or concurrent enrollment in ACCT 316
CISC 419 - 02 Accounting Information Systems - T - R - 1525 - 1700 OSS 333
CRN: 20781 4 Credit Hours Instructor: Ann K. Staelgraeve (Formerly QMCS 419) This course will provide an understanding of the conceptual framework and practices of accounting information systems and the ability to work effectively with computer specialists and management to design, implement and audit such systems. Examples of subjects included are: systems development life cycle (SDLC), systems analysis phase of the SDLC, data and process models, operations of a corporate data center, including internal controls, database integrity, audit considerations for both internal and external auditors, unit integration, and system testing. Prerequisites: CISC 110 or 200, and previous or concurrent enrollment in ACCT 316
CISC 419 - 03 Accounting Information Systems - T - R - 1730 - 1915 OSS 431
CRN: 20878 4 Credit Hours Instructor: Wendy S. Degler (Formerly QMCS 419) This course will provide an understanding of the conceptual framework and practices of accounting information systems and the ability to work effectively with computer specialists and management to design, implement and audit such systems. Examples of subjects included are: systems development life cycle (SDLC), systems analysis phase of the SDLC, data and process models, operations of a corporate data center, including internal controls, database integrity, audit considerations for both internal and external auditors, unit integration, and system testing. Prerequisites: CISC 110 or 200, and previous or concurrent enrollment in ACCT 316
CISC 478 - 01 Experiential Learning - - - - - -
CRN: 23118 4 Credit Hours Instructor: Jason E. Sawin
CISC 478 - 02 Experiential Learning - - - - - -
CRN: 23119 4 Credit Hours Instructor: Patrick L. Jarvis
CISC 490 - 01 Topics: Algorithms M - W - F 1055 - 1200 OSS 328
CRN: 22573 4 Credit Hours Instructor: Scott C. Yilek Introduction to design and analysis of algorithms. Course topics include basic theoretic algorithms, divide and conquer, graph algorithms, dynamic programming, and greedy algorithms. The course will also give an introduction to computational complexity, including NP-completeness and the P versus NP problem. Prerequisites: MATH 128, and CISC 230 with a minimum grade of C-.
CISC 495 - I1 Individual Study - - - - - -
CRN: 22831 4 Credit Hours Instructor: Scott C. Yilek
CISC 495 - I2 Individual Study - - - - - -
CRN: 23051 4 Credit Hours Instructor: Patrick L. Jarvis
CISC 495 - I3 Individual Study - - - - - -
CRN: 23090 4 Credit Hours Instructor: Patrick L. Jarvis
CISC 495 - I4 Individual Study - - - - - -
CRN: 23117 4 Credit Hours Instructor: Jan M. Gardner

Summer 2014 Courses

Summer 2014 Courses
Course - Section Title Days Time Location
CISC 200 - 01 Intro-Computer Tech & Bus Appl M T W R - 1015 - 1215 OSS 432
CRN: 30213 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. Through application of basic computing fundamentals, students will be better prepared to purchase computers, diagnose and solve computer problems, use and build local area network/home networks, use and build common software applications, and design simple web pages. Student teams will transfer concepts and skills learned in the course as they assist organizations in the community with their technology needs. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.

Fall 2014 Courses

Fall 2014 Courses
Course - Section Title Days Time Location
CISC 130 - 01 Intro-Program&Prob Solving-Sci - T - - - 0800 - 0940 OSS 432
CRN: 40819 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 01 Intro-Program&Prob Solving-Sci M - W - F 0815 - 0920 OSS 432
CRN: 40819 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 02 Intro-Program.&Prob Solv.-Sci - - - R - 0800 - 0940 OSS 432
CRN: 40820 4 Credit Hours Instructor: Ann K. Lockwood (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 02 Intro-Program.&Prob Solv.-Sci M - W - F 0935 - 1040 OSS 432
CRN: 40820 4 Credit Hours Instructor: Ann K. Lockwood (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 04 Intro-Program&Prob Solving-Sci M - W - F 1055 - 1200 OSS 431
CRN: 41059 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 04 Intro-Program&Prob Solving-Sci - T - - - 0955 - 1135 OSS 432
CRN: 41059 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 05 Intro-Program&Prob Solving-Sci M - W - F 1335 - 1440 OSS 428
CRN: 41153 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 05 Intro-Program&Prob Solving-Sci - - - R - 1330 - 1510 OSS 431
CRN: 41153 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 131 - 01 Intro-Programming&Prob Solving - T - R - 0800 - 0940 OSS 428
CRN: 41932 4 Credit Hours Instructor: Patrick L. Jarvis This course is designed for students with majors in the Department of Computer and Information Sciences and focuses on logical thinking, the design and implementation of algorithms in a procedural language, testing, correctness, and the use of common programming structures such as arrays. In addition, basic machine concepts are covered including hardware organization and representation of information in the machine. The typical student will be adept at using the computer but will have no prior programming experience. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 131 - 01 Intro-Programming&Prob Solving M - W - - 0815 - 0920 OSS 428
CRN: 41932 4 Credit Hours Instructor: Patrick L. Jarvis This course is designed for students with majors in the Department of Computer and Information Sciences and focuses on logical thinking, the design and implementation of algorithms in a procedural language, testing, correctness, and the use of common programming structures such as arrays. In addition, basic machine concepts are covered including hardware organization and representation of information in the machine. The typical student will be adept at using the computer but will have no prior programming experience. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 200 - 01 Intro-Computer Tech & Bus Appl M - W - F 1215 - 1320 OSS 432
CRN: 40822 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. It will provide an introduction to programming and problem solving for non-majors. Spreadsheet and database software will be used to solve problems related to business. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 200 - 02 Intro-Computer Tech & Bus Appl M - W - F 0935 - 1040 OSS 431
CRN: 40823 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. It will provide an introduction to programming and problem solving for non-majors. Spreadsheet and database software will be used to solve problems related to business. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 230 - 01 Object Oriented Design & Prog M - W - - 0935 - 1040 OSS 428
CRN: 40826 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 281) Programming and problem solving using an object-oriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from object-oriented design, algorithms, modeling, design requirements and representation, Uniform Modeling Language specification, implementation of object-oriented models, testing, and verification, and elementary design patterns. Lab included Prerequisites: A minimum grade of C- in CISC 130 or 131
CISC 230 - 01 Object Oriented Design & Prog - T - R - 0955 - 1135 OSS 428
CRN: 40826 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 281) Programming and problem solving using an object-oriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from object-oriented design, algorithms, modeling, design requirements and representation, Uniform Modeling Language specification, implementation of object-oriented models, testing, and verification, and elementary design patterns. Lab included Prerequisites: A minimum grade of C- in CISC 130 or 131
CISC 231 - 01 Data Structures-Object Design M - W - F 1055 - 1200 OSS 333
CRN: 40824 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 350) Presents the fundamental suite of data structures and the algorithms used to implement them. Topics include: abstract data types, algorithm development and representation, searching, sorting, stacks, queues, lists, trees, measuring algorithm complexity, object-oriented design and implementation of moderately large and complex systems. Course assumes the student has proficiency in object-oriented specification, design, and implementation. Prerequisites: A minimum grade of C- in CISC 230, MATH 128
CISC 297 - 01 TOPICS:Bus.Appl.Prob.Sol.&Prog - T - R - 1330 - 1510 OSS 428
CRN: 43198 4 Credit Hours Instructor: Patrick L. Jarvis This course will prepare students to use computers in a business environment and daily life. It will provide an introduction to programming and problem solving for non-majors with emphasis on problems drawn from the area of actuarial science. Students will learn both a programming language and an application package designed to implement programming features in a manner more accessible to non-programmers. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. This course is for ACSC majors only; or by permission of the instructor.
CISC 310 - 01 Operating Systems M - W - - 1525 - 1700 OSS 431
CRN: 40991 4 Credit Hours Instructor: Ann K. Lockwood (Formerly QMCS 360) The basic principles of designing and building operating systems. Sequential versus concurrent processes, synchronization and mutual exclusion, memory management techniques, CPU scheduling, input/output device handling, file systems design, security and protection. Prerequisite: A minimum grade of C- in CISC 230
CISC 410 - 01 Advanced Information Security M - W - F 1335 - 1440 OSS 431
CRN: 41439 4 Credit Hours Instructor: Scott C. Yilek A more in-depth study of security issues than CISC 210. This course will focus on modern attack techniques and defenses in the areas of application security, network security, cryptographic protocols, and web security. Prerequisite: A minimum grade of C- in CISC 210
CISC 419 - 01 Accounting Information Systems M - W - - 1525 - 1700 OSS 428
CRN: 40825 4 Credit Hours Instructor: Chelley M. Vician (Formerly QMCS 419) This course will provide an understanding of the conceptual framework and practices of accounting information systems and the ability to work effectively with computer specialists and management to design, implement and audit such systems. Examples of subjects included are: systems development life cycle (SDLC), systems analysis phase of the SDLC, data and process models, operations of a corporate data center, including internal controls, database integrity, audit considerations for both internal and external auditors, unit integration, and system testing. Prerequisites: CISC 110 or 200, and previous or concurrent enrollment in ACCT 316
CISC 450 - 01 Database Design I M - W - F 0935 - 1040 OSS 333
CRN: 40992 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 450) Introduction to database management systems design philosophy. Design considerations for satisfying both availability and integrity requirements. Data models used to structure the logical view of the database. Schema, subschemas, and database administration. Emphasis on general purpose relational database management systems using SQL. Prerequisite: a minimum grade of C- in CISC 230

Below are courses offered in Statistics. For up-to-date information regarding course information for registration, please visit Murphy Online.

Spring 2014 Courses

Spring 2014 Courses
Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M - W - F 0815 - 0920 OSS 313
CRN: 20989 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 02 Statistics I M - W - F 0815 - 0920 OSS 431
CRN: 20990 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 03 Statistics I M - W - F 0935 - 1040 OSS 431
CRN: 20991 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 04 Statistics I M - W - F 0935 - 1040 OSS 313
CRN: 20992 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 05 Statistics I M - W - F 1055 - 1200 OSS 431
CRN: 20993 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 06 Statistics I M - W - F 1055 - 1200 OSS 327
CRN: 20994 4 Credit Hours Instructor: Leigh Lawton Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 07 Statistics I M - W - - 1525 - 1700 OSS 431
CRN: 20995 4 Credit Hours Instructor: Agnes Kiss Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 08 Statistics I M - W - - 1730 - 1915 OSS 432
CRN: 20996 4 Credit Hours Instructor: Agnes Kiss Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 09 Statistics I - T - R - 0800 - 0940 OSS 431
CRN: 20997 4 Credit Hours Instructor: Marc D. Isaacson Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 10 Statistics I - T - R - 0800 - 0940 OSS 313
CRN: 20998 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 11 Statistics I - T - R - 0955 - 1135 OSS 313
CRN: 20999 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 12 Statistics I - T - R - 0955 - 1135 OSS 329
CRN: 21000 4 Credit Hours Instructor: Marc D. Isaacson Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 13 Statistics I - T - R - 1330 - 1510 OSS 329
CRN: 21001 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 13 Statistics I - - - R - 1330 - 1510 OSS 432
CRN: 21001 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 14 Statistics I - T - R - 1525 - 1700 OSS 329
CRN: 21002 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 14 Statistics I - - - R - 1525 - 1700 OSS 428
CRN: 21002 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 41 Honors: Statistics I - T - R - 0800 - 0940 OSS 313
CRN: 22576 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 310 - 01 Biostatistics M - W - F 1215 - 1320 OSS 428
CRN: 21641 4 Credit Hours Instructor: Erin M. Curran This course provides students with the knowledge and skils needed to effectively apply basic statistical methods in health related fields, such as Biology, Medicine, and Public Health. Students learn inferential statistical techniques involving topics in estimation, hypothesis testing, nonparametric methods, clinical trials, contingency tables, review of analysis of variance and linear regression, and a brief introduction to experimental design.
STAT 314 - 01 Mathematical Statistics - T - R - 1330 - 1510 OSS 227
CRN: 22501 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.
STAT 333 - 01 Applied Statistical Methods - T - R - 1525 - 1700 OSS 227
CRN: 20988 4 Credit Hours Instructor: Arkady Shemyakin Regression and exponential smoothing methods; Stochastic Time Series: auto- and cross-correlation, autoregressive moving average models; application to forecasting. Prerequisites: MATH 303 or 314 or STAT 314 or permission of instructor
STAT 333 - 02 Applied Statistical Methods - T - R - 0955 - 1135 SCB 211
CRN: 22599 4 Credit Hours Instructor: Arkady Shemyakin Regression and exponential smoothing methods; Stochastic Time Series: auto- and cross-correlation, autoregressive moving average models; application to forecasting. Prerequisites: MATH 303 or 314 or STAT 314 or permission of instructor
STAT 460 - 01 Statistical Research/Practicum - - - - - -
CRN: 22849 4 Credit Hours Instructor: Arkady Shemyakin Students will work individually with the instructor to identify a statistical research topic of current interest or to identify a real practical problem, for which statistics can be used to produce a feasible solution. State and local governments, companies, businesses, TV channels, or even faculty doing research should be the natural source of real practical problems to be solved. For either the research or the practical problem, the final outcome should be a report with publication potential.

Summer 2014 Courses

Summer 2014 Courses
Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M T W R - 1015 - 1215 OSS 313
CRN: 30051 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 02 Statistics I M T W R - 1500 - 1700 OSS 313
CRN: 30052 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201

Fall 2014 Courses

Fall 2014 Courses
Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M - W - F 0815 - 0920 OSS 313
CRN: 41139 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 02 Statistics I M - W - F 0815 - 0920 OSS 431
CRN: 41140 4 Credit Hours Instructor: Marc D. Isaacson Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 03 Statistics I M - W - F 0935 - 1040 OSS 313
CRN: 41141 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 04 Statistics I M - W - F 0935 - 1040 OEC 206
CRN: 41142 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 05 Statistics I M - W - F 1055 - 1200 OEC 206
CRN: 41143 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 09 Statistics I - T - R - 0800 - 0940 OSS 313
CRN: 41147 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 10 Statistics I - T - R - 0800 - 0940 OSS 431
CRN: 41148 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 11 Statistics I - T - R - 0955 - 1135 OSS 431
CRN: 41149 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 12 Statistics I - T - R - 0955 - 1135 OSS 329
CRN: 41150 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 13 Statistics I - T - R - 1330 - 1510 OSS 329
CRN: 41151 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 15 Statistics I - T - R - 1525 - 1700 OSS 329
CRN: 41152 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 16 Statistics I - T - R - 1730 - 1915 OSS 431
CRN: 41441 4 Credit Hours Instructor: Agnes Kiss Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 314 - 01 Mathematical Statistics - T - R - 0955 - 1135 OSS 214
CRN: 41216 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.
STAT 314 - 02 Mathematical Statistics - T - R - 1525 - 1700 OSS 227
CRN: 41713 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.
STAT 320 - 01 Statistics II M - W - F 1215 - 1320 OSS 428
CRN: 43197 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 320 or QMCS 320 Applie linear regression models. Simple linear regression; introduction, inferences, diagonstics, remedial measures, simultaneous inference. Matrix approach in linear regression. Multiple regression; inference, remedial measures, extra sums of squares, partial determinations, standardized models, use of indicator and mixed variables, polynomial regression, model selection and validation, diagnostics, remedial measures, multicollinearity and effects, autocorrelation. Single and multi-factor analysis of variance: analysis of factor level means, interactions, inferences, diagnostics and remedial measures. A statistical package must be used as tool. Optional topics may include: logistic regression, design of experiments, and forecasting. Prerequisite: STAT 202 or 333 or IDTH 201 or 220 or MATH 333