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
Curran (chair), Jarvis, Miracle, Myre, Sawin, Werness, 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 robotics. 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.
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 robotics, computer architecture, software engineering, and computer networking.
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)
Note: CISC 131 is recommended for this major - 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.
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 375 Web Development (4 credits)
- CISC 410 Advanced Information Security (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)
Computer Science (B.A.) majors should work with the department for a transition plan.
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 UST 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 UST 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 131 is recommended for this major
- 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.
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
Required: Five 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)
Note: CISC 131 is recommended for this minor
- Four additional CISC courses, at least two of which must be numbered 300 or above.
Note: One of these four may be chosen from STAT 360, 400, 410 and 411 (formerly IDTH 360, 400, 410 and 411).
Students should choose courses appropriate to their major field of study in consultation with the department chair or a member of the CISC department faculty.
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. This course fulfills the third course in the Natural Science and Mathematical and Quantitative Reasoning. 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 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 243 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||
| CISC 269 | Research | 2 OR 4 | |
| Description of course Research : | No description is available. | ||
| 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 | Computer App-Experimental Sci | 4 | |
| Description of course Computer App-Experimental Sci : | Introduction to the use of computers in the collection and analysis of scientific information. The course is designed to meet the needs of both natural science majors with an interest in scientific computing and computer science majors with an interest in scientific data acquisition and analysis. Emphasis is placed on application of concepts and techniques including hands-on work with data acquisition electronics and LabVIEW programming. Topics include laboratory device interfacing, analog-signal acquisition and processing, frequency transformations, data analysis, image processing, and math modeling and simulation. Prerequisites: A minimum grade of C- in either CISC 130 or 131; and MATH 109 or 111 or 113; and one course in a laboratory science | ||
| 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 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: MATH 128 and, a C- or better in CISC 231 | ||
| CISC 389 | Research | 2 OR 4 | |
| Description of course Research : | No description is available. | ||
| CISC 393 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||
| 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 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 200, and previous or concurrent enrollment in ACCT 316 | ||
| CISC 440 | Artfcl Intelligence & Robotics | 4 | |
| Description of course Artfcl Intelligence & Robotics : | (Formerly QMCS 380) 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: CISC 230 and STAT 220 (IDTH 220) | ||
| 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: CISC 450 or 630 | ||
| 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 475 | Experiential Learning | 2 | |
| Description of course Experiential Learning : | No description is available. | ||
| CISC 476 | Experiential Learning | 2 | |
| Description of course Experiential Learning : | No description is available. | ||
| CISC 477 | Experiential Learning | 4 | |
| Description of course Experiential Learning : | No description is available. | ||
| CISC 478 | Experiential Learning | 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 C- or better in: CISC 350, CISC 340 and CISC 380 | ||
| 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 491 | Research | 2 OR 4 | |
| Description of course Research : | No description is available. | ||
| 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 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 | |
|---|---|---|---|
| IDTH 243 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||
| IDTH 269 | Research | 2 OR 4 | |
| Description of course Research : | No description is available. | ||
| IDTH 295 | Topics | 2 | |
| Description of course Topics : | No description is available. | ||
| IDTH 296 | Topics | 2 | |
| Description of course Topics : | No description is available. | ||
| IDTH 389 | Research | 2 OR 4 | |
| Description of course Research : | No description is available. | ||
| IDTH 393 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||
| IDTH 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 | ||
| Course Number | Title | Credits | |
|---|---|---|---|
| STAT 201 | Introductory Statistics II | 2 | |
| Description of course Introductory Statistics II : | (Formerly IDTH 201) This course is for students desiring to satisfy the coverage of STAT 220 ( a full semester of statistics), when less than one full semester of statistics has been taken. Review of 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. Introduction to basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. Prerequisite: STAT 206 (IDTH 206) or at least .35 semester, but less than one semester of statistics. Note: Students who receive credit for STAT 201 may not receive credit for STAT 220. | ||
| STAT 206 | Statistics in Business | 2 TO 4 | |
| Description of course Statistics in Business : | For transfer articulation purposes only | ||
| STAT 220 | Statistics I | 4 | |
| Description of course Statistics I : | Formerly IDTH 220. Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions; inference for one and two or more independent means; inference for paired means; inference for comparing counts; and inference for simple linear regression. Students must enroll in both a lecture section and software-specific laboratory section, in the same academic semester, to successfully complete STAT 220. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at MATH 108 or above; or MATH 100, 101, or 105. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201. | ||
| STAT 310 | Biostatistics | 4 | |
| Description of course Biostatistics : | This course provides students with the knowledge and skills 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. Prerequisites: STAT 200. | ||
| STAT 314 | Mathematical Statistics | 4 | |
| Description of course Mathematical Statistics : | 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 | Statistics II | 4 | |
| Description of course Statistics II : | Applied linear regression models. Simple linear regression; introduction, inferences, diagnostics, 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: One of the following, STAT 201, STAT 220, STAT 333, MATH 333 | ||
| STAT 333 | Applied Statistical Methods | 4 | |
| Description of course Applied Statistical Methods : | 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 360 | Advanced Statistical Software | 4 | |
| Description of course Advanced Statistical Software : | (Formerly IDTH 360) This course introduces students to an advanced statistical software package to effectively apply statistical methods, in general. Students create data sets from raw data files, create variables within a data set, append and/or modify data sets, create subsets, then apply a whole host of statistical procedures, create graphs and produce reports. The course will be based on several leading advanced statistical software packages, which will be chosen from semester to semester to match the needs of the community. Prerequisites: STAT 220 or STAT 314 | ||
| STAT 370 | Bayesian Models | 4 | |
| Description of course Bayesian 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. Review of parametric statistical models and principles of statistical inference. Application to loss and ruin models. Construction of empirical and parametric models and model selection. Credibility theory. Simulation. Offered every other year. Prerequisite: MATH 313 and STAT 314 or STAT 220 and STAT 320 | ||
| STAT 400 | Data Mining & Machine Learning | 4 | |
| Description of course Data Mining & Machine Learning : | (Formerly IDTH 400) Introduction to statistical learning methods, from a statistical and computational perspective, to deal with massive and complex data. Topics include: Introduction; creating a project and diagram. Data preparation; defining and exploring data sources. Pattern discovery; cluster analysis, market basket analysis. Decision trees; cultivating and pruning decision trees, autonomous tree growth. Regression; transforming inputs, categorical inputs, polynomial regression. Neural Networks; input selection, stopped training. Model assessment; fit statistics, graphs, separate sampling. Model implementation; scored data sets, score code models. Applications. This course will give the basic ideas and intuition behind these methods, and special emphasis will be placed on their application through statistical software. Prerequisites: CISC 130 or 131, and MATH 113, and STAT 320 or 333. | ||
| 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 460 | Statistical Research/Practicum | 4 | |
| Description of course Statistical Research/Practicum : | 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. | ||
| STAT 495 | Individual Study | 2 OR 4 | |
| Description of course Individual Study : | No description is available. | ||