COMPUTER AND INFORMATION SCIENCES (CISC)
College of Arts and Sciences, Department of Computer and Information Sciences
O’Shaughnessy Science Hall (OSS) 402, (651) 962-5470
Sawin(chair), Hardt, Jarvis, Marrinan, Miracle, Myre, Salisbury, Werness, and Yilek
In recognition of the ubiquitous nature of computing and the importance of being able to analyze data in the modern world, the Computer and Information Sciences department offers majors in Computer Science (BS) and Statistics (BS).
Computer Science majors develop the knowledge and skills required to design and build software and to create efficient solutions to real-world problems. Our major is designed to develop well-rounded students who can succeed in the challenging and continually changing field of technology. Our curriculum includes a wide variety of cutting-edge topics including, software design and implementation, computer architecture, database design, algorithms, computer networking, computer security, and artificial intelligence. Our graduates have started their careers in prominent local, national, and international businesses, as well as government organizations. Others have gone on to pursue careers in academia at top-ranked universities.
The Statistics major is offered through a joint program between CISC and the Mathematics department. The curriculum of this program is oriented toward real-world applications of statistics and the development of skills in statistical problem solving, data analysis and statistical modeling, statistical software use and programming, data mining and machine learning, and the communication of statistical results to diverse audiences. Graduates of the Statistics major are fully prepared to apply their knowledge and skills in myriad careers and graduate programs, including those found in business and marketing, the health sciences, education, government, and the social and behavioral sciences.
The department encourages Computer Science and Statistics majors to obtain a minor in a complementary discipline. Students interested in teacher licensure should see the various science and mathematics programs in the Department of Teacher Education section of this catalog. A dual undergraduate degree program with Engineering is also available, which is described in the catalog section under School of Engineering. Additionally, we offer a fast track Masters in Graduate Programs in Software degree.
Senior Residency
Students majoring in computer science must have a minimum of 32 credits of STAT/CISC courses from St. Thomas. CISC minors need a minimum of 12 CISC/STAT credits from St. Thomas
Major in Computer Science (B.S.)
Computer Science is a foundation for many different computing careers. Computer scientists design and build software and create efficient solutions to real‐world problems in such fields as artificial intelligence, computer architecture, software engineering, and computer security.
Required courses:
- CISC 130* Introduction to Programming and Problem Solving in the Sciences (4 credits)
or CISC 131* Introduction to Programming and Problem Solving (4 credits) - CISC 230* Object‐Oriented Design and Programming (4 credits)
- CISC 231* Data Structures Using Object‐Oriented Design (4 credits)
- CISC 340* Computer Architecture (4 credits)
- CISC 350* Information Security (4 credits)
- CISC 380* Algorithms (4 credits)
- CISC 480* Senior Capstone (4 credits)
- STAT 220 Statistics I (4 credits)
* Note: A grade of C‐ or above must be earned by majors in these courses.
Note: CISC 131 is recommended for this major
Plus 16 credits from the following:
- CISC 310 Operating Systems (4 credits)
- CISC 342 Computers in Experimental Sciences (4 credits)
- CISC 370 Computer Networking (4 credits)
- CISC 369 Computer Science Research (2-4 credits)
- CISC 370 Computer Networking (4 credits)
- CISC 375 Web Development (4 credits)
- CISC 401 Approved Study Abroad Course (2-4 credits)
- CISC 402 Approved Study Abroad Course (2-4 credits)
- CISC 403 Approved Study Abroad Course (2-4 credits)
- CISC 404 Approved Study Abroad Course (2-4 credits)
- CISC 405 Approved Study Abroad Course (2-4 credits)
- CISC 410 Advanced Information Security (4 credits)
- CISC 420 Computer Graphics (4 credits)
- CISC 440 Artificial Intelligence and Robotics (4 credits)
- CISC 450 Database Design I (4 credits)
- CISC 451 Database Design II (4 credits)
- STAT 360 Advanced Statistical Software (4 credits)
- STAT 400 Data Mining and Machine Learning (4 credits)
Allied Requirements:
- MATH 109 Calculus with Review II (4 credits)
or MATH 113 Calculus I (4 credits) - MATH 114 Calculus II or STAT 320 Statistics II
- MATH 128 Introduction to Discrete Mathematics (4 credits)
Fast Track to a Masters in Graduate Programs in Software (with a Bachelor of Science degree in Computer Science)
Students in the fast track program will take four Graduate Programs in Software (GPS) crosslisted classes and, after completing their undergraduate degree, continue on to take 10
additional GPS classes, instead of 14, to obtain their Master of Science degree in either Software Engineering or Software Management.
For each of the graduate-level cross-listed courses(^) in the list below that a St. Thomas undergraduate student completes with a grade of at least C-, St. Thomas Graduate Programs in Software (GPS) will grant a three credit reduction in the number of credits required to complete the Master of Science (MS) degree (either major software engineering or software management) and waive the corresponding required GPS course up to a maximum of 12 credits.
The MS degree requires the student to complete 42 graduate credits. With the above credit reductions for CISC courses taken as a St. Thomas undergraduate, that student as a graduate student
must complete a minimum of 30 graduate credits (10 graduate courses) at the University of St. Thomas for GPS to award the MS degree. The student must begin their GPS MS program within one year of completing their St. Thomas undergraduate degree to be eligible for these credit reductions.
Required Courses:
- CISC 130* Introduction to Programming and Problem Solving in the Sciences (4 credits)
or CISC 131* Introduction to Programming and Problem Solving (4 credits) - CISC 230* Object-Oriented Design and Programming (4 credits)
- CISC 231 * Data Structures Using Object-Oriented Design (4 credits)
- CISC 340* Computer Architecture (4 credits)
- CISC 350* Information Security (4 credits)
- CISC 380* Algorithms (4 credits)
- CISC 480* Senior Capstone (4 credits)
- STAT 220 Statistics I (4 credits)
- CISC 605^ Technical Communications (4 credits)
- CISC 610^ Software Engineering (4 credits)
- CISC 625^ Software Project Management (4 credits)
- CISC 630^ Database Design (4 credits)
* Note: A grade of C- or above must be earned by majors in these courses.
Note: CISC 131 is recommended for this major
Plus four credits from the following:
- CISC 310 Operating Systems (4 credits)
- CISC 342 Computers in Experimental Sciences (4 credits)
- CISC 370 Computer Networking (4 credits)
- CISC 375 Web Development (4 credits)
- CISC 410 Advanced Information Security (4 credits)
- CISC 440 Artificial Intelligence and Robotics (4 credits)
- STAT 360 (Formerly IDTH 360) Advanced Statistical Software (4 credits)
- STAT 400 (Formerly IDTH 400) Data Mining and Machine Learning (4 credits)
Allied Requirements:
- MATH 109 Calculus with Review II (4 credits)
or MATH 113 Calculus I (4 credits) - MATH 114 Calculus II or STAT 320 Statistics II
- MATH 128 Introduction to Discrete Mathematics (4 credits)
Major or Minor in Statistics
This is an interdisciplinary major in the department of Mathematics and Computer and Information Sciences. This joint major allows students to pursue an interest in mathematical statistics, applied statistics, and related areas including biostatistics, operations research, and data mining. In addition, there are two minors, one in Applied Statistics and one in Mathematical Statistics.
See Statistics
Minor in Computer and Information Sciences
- CISC 130 Introduction to Programming and Problem Solving in the Sciences (4 credits)
or CISC 131* Introduction to Programming and Problem Solving (4 credits)
Note: CISC 131 is recommended for this minor
And one of the following elective options:
- Four additional CISC courses, at least two of which must be numbered 300 or above.
or - Three additional CISC courses, at least two of which are numbered 300 or above, and one course from the list below
Elective Options:
- BIOL 464 Bioinformatics (4 credits)
- DIMA 258 Writing/Designing for the Web (4 credits)
- ENTR 371 Silicon Valley & Entr Thinking (4 credits)
- ENGR 230 Digital Design (4 credits)
or ENGR 331 Designing with Microprocessors (4 credits)
or ENGR 421 - ENGL 204 Critical Discourse of Video Games (4 credits)
- GEOG 421 Applied Geographic Info Sys (4 credits)
- MATH 315 Applied Math & Modeling I (4 credits)
or MATH 316 Applied Math & Modeling II (4 credits)
or MATH 385 Math Meths/Numerical Anal (4 credits) - NSCI 340 Computational Neuroscience (4 credits)
- PHIL 220 Logic (4 credits)
- PHYS 323 Methods of Exp. Physics (4 credits)
or PHYS 325 Methods of Comp. Physics (4 credits)
or STAT 360 Comp STAT & Data Analysis (4 credits)
or STAT 400 Data Mining & Machine Learning (4 credits)
or STAT 410 Operations Research I (4 credits)
or STAT 411 Operations Research II (4 credits) - STCM 346 Digital Content and Strategy (4 credits)
Students should choose elective courses appropriate to their major field of study or area of interest in consultation with the department chair of a member of the CISC department faculty. Note that elective courses may have additional pre-requisites.
Teacher Licensure
Elementary Education with a co-major in Science, Technology, Engineering, and Mathematics for Elementary Education
See Education
| Course Number | Title | Credits | |
|---|---|---|---|
| CISC 120 | Computers in Elementary Educ | 4 | |
| Description of course Computers in Elementary Educ : | 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. Prerequisite: Elementary Education or SMEE major | ||
| CISC 130 | Intro-Program&Prob Solving-Sci | 4 | |
| Description of course Intro-Program&Prob Solving-Sci : | 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 Prerequisite: Placement into MATH 108 or higher or completion of STAT 220 with a C- or better, or completion of one of: MATH 108, 109, 113, 114, or 200 | ||
| CISC 131 | Intro-Programming&Prob Solving | 4 | |
| Description of course Intro-Programming&Prob Solving : | 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 Prerequisite: Placement into MATH 108 or higher or completion of STAT 220 with a C- or better, or completion of one of: MATH 108, 109, 113, 114, or 200 | ||
| CISC 200 | Intro-Computer Tech & Bus Appl | 4 | |
| Description of course Intro-Computer Tech & Bus Appl : | (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 201 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 202 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 203 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 204 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 205 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 216 | Quantitative Techniques - Busn | 2 | |
| Description of course Quantitative Techniques - Busn : | (Formerly QMCS 216) The use of microcomputer spreadsheet software to aid in solving quantitative business problems. This course is to be taken by students who have been given transfer credits for the equivalent of some part but not all of CISC 200 and who are required to take CISC 200. NOTE: Students who receive credit for CISC 216 may not receive credit for CISC 110 or 200. | ||
| CISC 230 | Object Oriented Design & Prog | 4 | |
| Description of course Object Oriented Design & Prog : | (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 | Data Structures-Object Design | 4 | |
| Description of course Data Structures-Object Design : | 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, and MATH 128 (may be taken concurrently) | ||
| CISC 260 | Data Fundamentals and Apps | 4 | |
| Description of course Data Fundamentals and Apps : | This course will prepare students to apply fundamental tools that are used to manipulate data. It will provide an introduction to spreadsheets, database technologies, and programming. Students will learn how to employ these tools to solve problems related to business, life sciences, and actuarial sciences.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 CISC 260 may not receive credit for CISC 200, 110 or 216. | ||
| CISC 269 | Computer Science Research | 2 OR 4 | |
| Description of course Computer Science Research : | Prerequisite: Permission of CISC faculty member and CISC department | ||
| CISC 295 | Topics | 2 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 296 | Topics | 2 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 297 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 298 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 310 | Operating Systems | 4 | |
| Description of course Operating Systems : | 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 340 or ENGR 330 | ||
| CISC 320 | Systems Analysis and Design I | 4 | |
| Description of course Systems Analysis and Design I : | (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: A minimum grade of C- in CISC 230 | ||
| CISC 321 | Systems Analysis and Design II | 4 | |
| Description of course Systems Analysis and Design II : | (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 340 | Computer Architecture | 4 | |
| Description of course Computer Architecture : | Introduction to the design and organization of computer systems. Topics covered in this course include digital logic, machine data and instruction representations, computer arithmetic, instruction sets and assembly language, ALU and CPU design, pipelining, cache systems, memory, performance metrics, and parallelism. Prerequisites: a minimum grade of C- in CISC 230 | ||
| CISC 342 | Data Acquisition and Analysis | 4 | |
| Description of course Data Acquisition and Analysis : | Introduction to the use of computers to collect, analyze, and interact with real-world information. The course is designed to meet the needs of students with interests in using computing devices to interact with the physical world. Emphasis is placed on application of concepts and techniques in addition to microcontroller programming. Topics include laboratory device interfacing, analog signal acquisition and processing, frequency transformations, data analysis, and image processing. Lab included. Prerequisites: A minimum grade of C- or better in CISC 130 or 131; C- or better in MATH 109 or 111 or 113; and a C- or better in STAT 220 | ||
| CISC 350 | Information Security | 4 | |
| Description of course Information Security : | (Formerly CISC 210) An introductory course in computer security. Topics include operating system security, cryptography, user authentication, application security, secure programming, web security and privacy issues, and ethical issues in the field of computer security. Emphasis is on understanding the technical aspects of how adversaries exploit systems and the techniques for defending against these attacks. Prerequisites: MATH 128 (may be taken concurrently), and a minimum grade of C- in CISC 230 | ||
| CISC 360 | Data Visualization | 4 | |
| Description of course Data Visualization : | In this course, students will focus on computer-based design approaches and techniques for manipulating and visualizing data. A variety of data sources and corresponding visualization techniques will be examined. Particular attention will be given to effective visual communication of the meaning behind the data. Additionally, students will investigate the theme of storytelling with data. Prerequisites: A minimum grade of C- in CISC 130 or 131 and either CISC 260 or CISC 450. | ||
| CISC 369 | Computer Science Research | 2 OR 4 | |
| Description of course Computer Science Research : | Prerequisite: Permission of CISC faculty member and CISC department | ||
| CISC 370 | Computer Networking | 4 | |
| Description of course Computer Networking : | (Formerly QMCS 370) An introduction to computer networking. covering theory and implementation of basic networking concepts including communication protocols, local area networks, http protocol and client-server and peer-to-peer computing. Prerequisites: A minimum grade of C- in CISC 231 | ||
| CISC 375 | Web Development | 4 | |
| Description of course Web Development : | (Formerly CISC 270) This course examines the theory and practice of developing applications for the World Wide Web. Students will learn practical technique for designing and implementing Web applications, with a particular emphasis on server-side processing and data-driven Web sites. Prerequisite: A minimum grade of C- in CISC 230 | ||
| CISC 380 | Algorithms | 4 | |
| Description of course Algorithms : | Introduction to the design and analysis of algorithms. Course topics include the following algorithm design paradigms: 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: A minimum grade of C- or better in: MATH 128 and CISC 231 | ||
| CISC 393 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||
| CISC 401 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 402 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 403 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 404 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 405 | Approved Study Abroad Course | 2 TO 4 | |
| Description of course Approved Study Abroad Course : | The subject of this course will vary depending upon the study abroad program selected. | ||
| CISC 410 | Advanced Information Security | 4 | |
| Description of course Advanced Information Security : | A more in-depth study of security issues than CISC 350 (formerly 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 350 (formerly CISC 210) | ||
| CISC 419 | Accounting Information Systems | 4 | |
| Description of course Accounting Information Systems : | This course is designed to provide students with knowledge of concepts and practices of accounting information systems and the ability to work effectively with computer specialists and management in organizations. Major topics include accounting systems fundamentals, cybersecurity, enterprise risk management and internal controls, business data and process management, enterprise systems, data analytics, and emerging technologies. Prerequisites: CISC 200 and ACCT 311. | ||
| CISC 420 | Computer Graphics | 4 | |
| Description of course Computer Graphics : | This course introduces the principles of interactive computer graphics. Computer graphics serves as the foundation for many areas, such as computer animation, video game design, and data visualization. Topics covered in this course include raster vs. vector techniques and hardware, 2-dimensional and 3-dimensional rendering, and shading and illumination models. Prerequisites: C- or better in CISC 230 and a C- or better in MATH 128. It is recommended that you also have knowledge of vector and matrix math. | ||
| CISC 440 | Artfcl Intelligence & Robotics | 4 | |
| Description of course Artfcl Intelligence & Robotics : | Theory and implementation techniques using computers to solve problems, play games, prove theorems, recognize patterns, create artwork and musical scores, translate languages, read handwriting, speak and perform mechanical assembly. Emphasis placed on implementation of these techniques in robots. Prerequisites: Minimum grade of C- in CISC 231 and STAT 220* or STAT 201 Note: AP Statistics score of 3 or above will also meet this requirement | ||
| CISC 450 | Database Design I | 4 | |
| Description of course Database Design I : | This course introduces the fundamental concepts of database management, including aspects of data models, database languages, database design, indexing, and other topics in the field. Emphasis on general purpose relational database management systems using Relational Algebra and SQL. Prerequisites: MATH 128 and a minimum grade of C- in CISC 230 | ||
| CISC 451 | Database Design II | 4 | |
| Description of course Database Design II : | Advanced database analysis, design, and implementation including data warehousing, distributed databases, materialized views, grid computing, and replication. Storage and efficient retrieval of temporal data, objects, and non-textual information. Prerequisite: A C- in CISC 450 | ||
| CISC 460 | Senior Project | 4 | |
| Description of course Senior Project : | Work on a software analysis, design, and implementation project or on a computer support project under the direction of a faculty member. Prerequisite: Senior standing and permission of the instructor | ||
| CISC 469 | Computer Science Research | 2 OR 4 | |
| Description of course Computer Science Research : | Prerequisite: Permission of CISC faculty member and CISC department | ||
| CISC 476 | Experiential Learning | 0 TO 4 | |
| Description of course Experiential Learning : | No description is available. | ||
| CISC 480 | Senior Capstone | 4 | |
| Description of course Senior Capstone : | The senior capstone course provides computer science majors the opportunity to integrate the knowledge that they have gained from across the curriculum. Students will work in groups to design, document, and implement a large-sized software project. During this process, students will be exposed to programming team organization, software development practices, as well as tools that facilitate the development of software systems. Prerequisites: Senior standing and a minimum grade of C- or better in: CISC 350, CISC 340, and CISC 380 (which 380 may be taken concurrently) | ||
| CISC 483 | Seminar | 2 | |
| Description of course Seminar : | No description is available. | ||
| CISC 484 | Seminar | 2 | |
| Description of course Seminar : | No description is available. | ||
| CISC 485 | Seminar | 4 | |
| Description of course Seminar : | No description is available. | ||
| CISC 486 | Seminar | 4 | |
| Description of course Seminar : | No description is available. | ||
| CISC 487 | Topics | 2 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 488 | Topics | 2 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 489 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 490 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| CISC 495 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||
| CISC 605 | Technical Communications | 4 | |
| Description of course Technical Communications : | Instructors present the fundamentals of technical communication as practiced in industry, emphasizing clarity and organizational skills. Students engage in exercises that focus on technical writing, editing, public speaking and graphic design, and apply their skills across a broad range of activities, including critique of presentations and writing of proposals, reports, memoranda, user manuals, instructional modules, and specifications. The course includes techniques that assist an understanding of the structure of the language, and an appreciation for format and content, to better prepare students for project documentation. | ||
| CISC 610 | Software Engineering | 4 | |
| Description of course Software Engineering : | This is a survey course covering software engineering concepts, techniques, and methodologies. Topics covered include software engineering; software process and its difficulties; software life-cycle models; project planning including cost estimation; design methodologies including structured design, data-structure oriented design, object- oriented design; and software testing. A brief review of data structures is included. Prerequisite: A minimum grade of C- in CISC 231 | ||
| CISC 625 | Software Project Management | 4 | |
| Description of course Software Project Management : | Students gain a management perspective and a development process for planning, estimating, and controlling software development. They learn to develop a well-defined plan before beginning any software development effort; how to handle changes during the execution of the plan; how to incorporate quality criteria in the development cycle; and how to use methods to keep the project on track. Included in the course is the use of project management software and simulation software in the development and control of the project plan. Prerequisite: A minimum grade of C- in CISC 231 | ||
| CISC 627 | Software Planning & Testing | 4 | |
| Description of course Software Planning & Testing : | This course presents a software planning and quality perspective that guides the selection of tools and application of techniques needed for the successful completion of software development projects. A successful software project must manage many different, yet integrated activities. These activities include software development lifecycle tasks such as requirements gathering, software design, and code implementation. Many other activities also need to be planned and managed, such as project scope, schedule, and cost. In any successful software project, when issues arise (e.g. the requirements change, a defect in the software is discovered, scheduled activities do not go as planned, etc.) they need to be prioritized and appropriately addressed. To minimize the impact of software quality issues, software testing and quality improvement activities need to be planned, executed and coordinated. The purpose of this course is to learn the foundational concepts and practices needed to produce software that is completed on time, within budget, and with the necessary scope and quality required. While software development activities are covered in other courses, this course will focus more on the software planning and testing activities. Project management topics covered include: integration management, scope management, time management, cost management, and quality management from a software planning perspective. Software testing and quality topics covered include: testing terms and concepts, lower-level testing (e.g. unit and integration testing), higher-level testing (e.g. system and acceptance testing), and test automation. Agile Project and Product Management using Scrum will be introduced as an approach for directing these activities and laying the foundation for continuous process improvement and quality assurance. | ||
| CISC 630 | Database Design | 4 | |
| Description of course Database Design : | This course focuses on relational database design and system concepts. Database design includes database concepts, data models, conceptual (EER) and relational schema designs, query languages (SQL), physical data storage and access methods, and physical schema designs. Database systems includes query processing, transaction concepts and management such as concurrency control and recovery from failure, and database security and authorization. Students will complete a relational database design project. Prerequisites: MATH 128 and a minimum grade of C- in CISC 230 | ||
| Course Number | Title | Credits | |
|---|---|---|---|
| STAT 201 | Introductory Statistics II | 2 | |
| Description of course Introductory Statistics II : | This course provides students who already have a solid conceptual understanding of statistics the opportunity to apply their knowledge to analyzing data using modern statistical software. Topics include data visualization, inference for one and two samples, analysis of variance, chi-square tests for goodness of fit and association, and simple and multiple linear regression. Prerequisites: STAT 206 or AP Statistics Credit. Note, students who receive credit for STAT 201 may not receive credit for STAT 220. | ||
| STAT 206 | Introductory Statistics I | 2 TO 4 | |
| Description of course Introductory Statistics I : | For transfer articulation purposes only. Used when the transferred course does not include extensive data analysis using modern statistical software that is an essential component of STAT 220. | ||
| STAT 220 | Introductory Statistics | 4 | |
| Description of course Introductory Statistics : | This course is composed of an in-depth study of the processes through which statistics can be used to learn about environments and events. There will be an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in a variety of real-world contexts. Topics include data collection, research design, data visualization, bootstrap confidence intervals, inference for one and two samples, randomized hypothesis testing, analysis of variance, chi-square tests for goodness of fit and association, and simple and multiple linear regression. Extensive data analysis using modern statistical software is an essential component of this course. Prerequisites: Prerequisites: Math placement at level of MATH 108 or above; or MATH 100, 101, 105, 109, 111, or 113. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201 or STAT 206. | ||
| STAT 269 | Statistics Research | 2 OR 4 | |
| Description of course Statistics Research : | No description is available. | ||
| STAT 298 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| STAT 310 | Biostatistics | 4 | |
| Description of course Biostatistics : | In this course, students acquire the knowledge and skill required to effectively apply intermediate statistical methods in biology, medicine, public health, and other health-related fields. There is an emphasis on the following inferential statistical techniques: one-way and factorial ANOVA, interactions, repeated measures, and general linear models; logistic regression for cohort and case-control studies; nonparametric and distribution-free statistics; loglinear models and contingency table analyses; survival data, Kaplan-Meier methods, and proportional hazards models. Prerequisites: STAT 201 or STAT 220 or STAT 314 or MATH 303 | ||
| STAT 314 | Mathematical Statistics | 4 | |
| Description of course Mathematical Statistics : | Students will learn the theory and applications of point estimation, interval estimation, and hypothesis testing. Students will construct intervals and tests using a variety of statistical tools including frequentist statistical theory, Bayesian statistical theory, and resampling-based simulation. Prerequisites: Grades C- or higher in MATH 240 and MATH 313. NOTE: Students who receive credit for MATH 314 may not receive credit for MATH 303. | ||
| STAT 320 | Applied Regression Analysis | 4 | |
| Description of course Applied Regression Analysis : | This course provides students with the knowledge to effectively use various forms of regression models to address problems in a variety of fields. Students learn both simple and multiple forms of linear, ordinal, nominal, and beta regression models. There is an emphasis on simultaneous inference, model selection and validation, detecting collinearity and autocorrelation, and remedial measures for model violations. Students are also introduced to the use of time series and forecasting methods. Prerequisites: Grades C- or higher in STAT 201 or STAT 220 or STAT 314 or MATH 303. | ||
| STAT 333 | Predictive Modeling | 4 | |
| Description of course Predictive Modeling : | The course introduces the theory and applications of simple and multiple regression methods, including model construction and selection, transformation of variables and residual analysis; introduction to GLM (generalized linear models) for categorical and count response variables; time series analysis with ARIMA (autoregressive integrated moving average models). Students are introduced to principles of data collection and analysis, learn to work with statistical literature. Students present a writing intensive small group course project. Prerequisites: Grades C- or higher in MATH 240; AND STAT 220 or STAT 314 or MATH 303. | ||
| STAT 336 | Data Comm and Visualization | 4 | |
| Description of course Data Comm and Visualization : | This course will prepare students to effectively communicate the insights from data analysis. The course will cover the three main methods of communicating information about data—visually, orally, and in writing. Students will learn to tailor their communication to their audience and create publication-ready and boardroom-ready presentations of their results. Prerequisites: CISC 130 or 131; AND STAT 201 or STAT 220 or STAT 314 or MATH 303. | ||
| STAT 360 | Comp STAT & Data Analysis | 4 | |
| Description of course Comp STAT & Data Analysis : | This course introduces students to advanced computational methods in statistics and data analysis that require a thorough knowledge of a programming language such as Python or R. There will be an intensive focus on investigating the correlation and covariance structure of data, including data extraction and modification, dimensionality reduction, and structural equation modeling. Prerequisites: Grades C- or higher in CISC 130 or 131; AND MATH 109, 112 or 113; AND STAT 320 or 333 or ECON 315. | ||
| STAT 369 | Statistics Research | 2 OR 4 | |
| Description of course Statistics Research : | No description is available. | ||
| STAT 370 | Bayesian Statistical Models | 4 | |
| Description of course Bayesian Statistical Models : | The course covers a range of statistical models used in applications including Actuarial Science, Finance, Health and Social Sciences. It is oriented towards practical model construction and problem solving. The theory of Monte Carlo and Markov Chain Monte Carlo simulation is considered as well as its practical implementation. Credibility theory serves as one of the major applications. Prerequisites: MATH 109, 112 or 113; AND STAT 314 or 320. | ||
| STAT 380 | Spatial Statistics | 4 | |
| Description of course Spatial Statistics : | This course provides students with the background necessary to investigate spatially-referenced data and processes. There is an emphasis on specifying and fitting hierarchical models to represent geostatistical or point-referenced data, lattice or aerial data, and point process data. Students will also be introduced to the use of formal spatial data structures, point pattern analysis and cluster detection, spatial interpolation and kriging, spatial autocorrelation and variogram analysis, and spatial autoregressive models. Prerequisites: STAT 320 or STAT 333 | ||
| STAT 400 | Data Mining & Machine Learning | 4 | |
| Description of course Data Mining & Machine Learning : | In this course students will learn methods for working with massive and complex data. They will explore these topics from both statistical and computational perspectives. Topics include data preparation, defining and exploring data sources, pattern discovery, cluster analysis, decision trees, regression, neural networks, memory-based reasoning, survival analysis, and genetic algorithms. Software used in the course includes, but is not limited to, JMP, Excel, Java, R, Python, and Minitab. Prerequisites: Grades C- or higher in CISC 130 or 131 AND MATH 109, 112 or 113; AND STAT 320 or 333 or ECON 315. | ||
| STAT 410 | Operations Research I | 4 | |
| Description of course Operations Research I : | (Formerly IDTH 410) Introduction to computer and analytic techniques to support the decision-making process. Topics include: Introduction to linear programming algorithms, sensitivity, duality, transportation, assignment, transshipment, integer linear programming, network models, project scheduling, inventory models, and waiting line models. Prerequisites: MATH 113 or MATH 114 or MATH 128; and either STAT 220 or STAT 314/MATH 314 | ||
| STAT 411 | Operations Research II | 4 | |
| Description of course Operations Research II : | (Formerly IDTH 411) Advanced modeling and analytic techniques to support the decision-making process. Topics include: forecasting, decision analysis, multicriteria decision problems, simulation, Markov processes, dynamic programming, and nonlinear programming. Prerequisites: STAT 410 (or IDTH 410) and MATH 114 | ||
| STAT 413 | Generalized Linear Mixed Model | 4 | |
| Description of course Generalized Linear Mixed Model : | This course provides students we a review of methods of inference in the context of the linear model and generalized linear model. Students will then learn about correlation structures and linear models, and finally will create and conduct inference on generalized linear models. The course will emphasize analyzing real world data using modern statistical software. Additionally, students will understand the statistical theory underlying inference for generalized linear mixed models. Prerequisites: STAT 360 | ||
| STAT 414 | Network Models and Simulations | 4 | |
| Description of course Network Models and Simulations : | This course provides a systematic approach to the use of network modeling in the understanding and prediction of complex social, technological, and biological systems such as the emergence of fake news, the exchange of information across network routers, and the spread of infectious diseases. There will be an emphasis on efficient numerical methods for describing, visualizing, constructing, and simulating processes across both directed and undirected networks that may be static or dynamic in nature. Prerequisites: STAT 360. | ||
| STAT 460 | Statistical Practicum | 4 | |
| Description of course Statistical Practicum : | This course provides students the opportunity to develop and pursue an advanced statistical analysis with real world relevance and application. In addition to working with a faculty instructor, students are also given the opportunity to collaborate with professional mentors from various industries and to participate in national competitions. Previous sponsors include the Minnesota Department of Natural Resources, the Travelers Companies, U.S. Bancorp, SCOR Reinsurance, Drake Bank, and numerous professors from other departments at St. Thomas. Grade of C- or higher in STAT 360 and senior standing. | ||
| STAT 469 | Statistics Research | 2 OR 4 | |
| Description of course Statistics Research : | No description is available. | ||
| STAT 476 | Experiential Learning | 1 TO 4 | |
| Description of course Experiential Learning : | No description is available. | ||
| STAT 490 | Topics | 4 | |
| Description of course Topics : | The subject matter of these courses will vary from year to year, but will not duplicate existing courses. Descriptions of these courses are available in the Searchable Class Schedule on Murphy Online, View Searchable Class Schedule | ||
| STAT 495 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||