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Morris Campus

Computer Science B.A.

Division of Science & Mathematics - Adm
Division of Science and Mathematics
  • Program Type: Baccalaureate
  • Requirements for this program are current for Spring 2023
  • Required credits to graduate with this degree: 120
  • Required credits within the major: 54
  • Degree: Bachelor of Arts
The computer science curriculum is designed to not only provide a solid background in fundamentals, but also to continuously respond to rapid changes in the field of computing by equipping our students with modern tools, approaches, and cutting-edge concepts and technologies. Coursework in computer science spans three core areas of computing, including theory, software development, and systems. Beginning computer science courses are open to non-majors and satisfy the mathematical and symbolic reasoning component of the general education requirements. All computer science majors must complete a senior seminar capstone experience, and the discipline prides itself on the high quality of students' papers and presentations in this course. The program also includes mathematics or statistics in the required coursework. The computer science discipline is dedicated to offering a flexible set of important and relevant electives, which we update annually. Student input informs the electives we offer, and student input has inspired the creation of several electives. Computer science majors develop software, explore hardware systems, and apply theoretical concepts. Reflecting the collaborative nature of today's world, team work is heavily integrated into computer science coursework. Students are encouraged to use and supplement their formal education through research opportunities, internship experiences, programming and robotics competitions, and student and professional organizations. Many students take advantage of the opportunity to collaborate with computer science faculty on research projects, presenting the results at international, national, and regional conferences, as well as at UMM's Undergraduate Research Symposium. Study in computer science is required for management and math majors at UMM, as well as for students pursuing a variety of pre-engineering programs. Many UMM computer science majors enter the job market upon graduation, primarily in the computing industry. Others pursue postgraduate work toward a masters or doctoral degree in computing, business, library science, or a variety of other fields. The student learning objectives of the computer science program span the following five categories: • Students will be able to apply fundamental principles of computer science to solve problems in all core areas of computer science. • Students will demonstrate technological flexibility through the ability to employ new sets of tools effectively. • Students will be able to communicate technical ideas effectively both orally and in written form. • Students will demonstrate their ability to work in groups as part of an effective team. • Students will be able to identify and analyze ethical implications involving technology.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Admission Requirements
For information about University of Minnesota admission requirements, visit the Office of Admissions website.
General Requirements
All students are required to complete general University and college requirements. For more information, see the general education requirements.
Program Requirements
Students are required to complete 2 semester(s) of any second language. with a grade of C-, or better, or S, or demonstrate proficiency in the language(s) as defined by the department or college.
Grades of D or D+ in CSCI 1201, 1301, 1302, 2101, Math 2202, and 3411 may not be used to meet the major requirements. No more than one course with a grade of D or D+, offset by an equivalent number of credits of A or B grades, may be used to meet the requirements for a computer science major. Non-elective courses may not be taken S-N unless offered S-N only. Up to 4 credits of CSci 4xxx taken S-N may be counted towards the major requirements. A minimum GPA of 2.00 is required in the major to graduate. The GPA includes all, and only, University of Minnesota coursework. Grades of "F" are included in GPA calculation until they are replaced. Elective courses: computer science major electives are divided into three areas: systems courses (CSCI 44xx), theory courses (CSCI 45xx), and programming and languages courses (CSCI 46xx). The discipline offers an array of courses in each area. The courses listed are representative of the courses offered. New courses are continually developed and added to keep up with changes in the field.
Required Courses
CSCI 1201 {Inactive} [M/SR] (4.0 cr)
or CSCI 1301 - Problem Solving and Algorithm Development [M/SR] (4.0 cr)
CSCI 1302 - Foundations of Computer Science [M/SR] (4.0 cr)
or MATH 2202 - Mathematical Perspectives [M/SR] (4.0 cr)
MATH 3411 - Discrete and Combinatorial Mathematics (4.0 cr)
CSCI 2101 - Data Structures [M/SR] (5.0 cr)
CSCI 3413 - Computing Systems: Concepts (3.0 cr)
CSCI 3412 - Computing Systems: Practicum (2.0 cr)
CSCI 3501 - Algorithms and Computability (5.0 cr)
CSCI 3601 - Software Design and Development (5.0 cr)
CSCI 4901 - Senior Seminar (2.0 cr)
IS 1091W - Ethical and Social Implications of Technology [E/CR] (2.0 cr)
Elective Courses
Take 10 or more credit(s) including exactly 3 sub-requirements(s) from the following:
Computing Systems Courses (44xx):
Take 2 - 4 credit(s) from the following:
· CSCI 4403 - Systems: Data Mining (4.0 cr)
· CSCI 4409 - Systems: Programming for Parallel Architecture (2.0 cr)
· CSCI 4410 - Systems: Cloud Computing Architectures (2.0 cr)
· CSCI 4453 - Systems: Database Systems (4.0 cr)
· CSCI 4454 - Systems: Robotics (4.0 cr)
· CSCI 4457 - Systems: Ubiquitous Computing (4.0 cr)
· CSCI 4458 - Systems: Bioinformatic Systems (4.0 cr)
· Theory Courses (45xx):
Take 2 - 4 credit(s) from the following:
· CSCI 4506 - Theory: Fuzzy Logic and Fuzzy Sets (2.0 cr)
· CSCI 4553 - Theory: Evolutionary Computation and Artificial Intelligence (4.0 cr)
· CSCI 4554 - Theory: Cryptography (4.0 cr)
· CSCI 4555 - Theory: Neural Networks and Machine Learning (4.0 cr)
· CSCI 4557 - Theory: Quantum Computing (4.0 cr)
· Processes, Programming, and Languages Courses (46xx):
Take 2 - 4 credit(s) from the following:
· CSCI 4604 - Processes, Programming, and Languages: Graphical User Interfaces (2.0 cr)
· CSCI 4605 - Processes, Programming, and Languages: Refactoring (2.0 cr)
· CSCI 4610 - Processes, Programming, and Languages: Programming for Cloud Computing (2.0 cr)
· CSCI 4651 - Processes, Programming, and Languages: Programming Languages (4.0 cr)
· CSCI 4654 - Processes, Programming, and Languages: Modern Functional Programming (4.0 cr)
· CSCI 4656 - Processes, Programming, and Languages: Human-Computer Interaction and Interface Design (4.0 cr)
· CSCI 4657 - Processes, Programming, and Languages: Programming Languages for Client-Server Systems (4.0 cr)
· CSCI 4658 - Processes, Programming, and Languages: Usability, Design, and Mobile Technologies (4.0 cr)
· CSCI 4659 - Processes, Programming, and Languages: Measuring and Managing Software Quality (4.0 cr)
Math and Statistics Electives
MATH 1101 and above, excluding MATH 2211, or STAT 2xxx and above, excluding STAT 3701.
Take 12 or more credit(s) from the following:
· CSCI 2701 - Introduction to Data Science [M/SR] (4.0 cr)
· MATH 1101 - Calculus I [M/SR] (5.0 cr)
· MATH 1102 - Calculus II [M/SR] (5.0 cr)
· MATH 2101 - Calculus III [M/SR] (4.0 cr)
· MATH 2202 - Mathematical Perspectives [M/SR] (4.0 cr)
· MATH 2401W - Differential Equations [M/SR] (4.0 cr)
· MATH 2501 - Probability and Stochastic Processes [M/SR] (4.0 cr)
· MATH 3111 - Linear Algebra (4.0 cr)
· MATH 3xxx
· MATH 4xxx
· STAT 2xxx
· STAT 3501 - Survey Sampling [M/SR] (4.0 cr)
· STAT 3601 - Data Analysis [M/SR] (4.0 cr)
· STAT 3611 - Multivariate Statistical Analysis [M/SR] (4.0 cr)
· STAT 3901 - Statistical Communication (2.0 cr)
· STAT 4xxx
 
More program views..
View college catalog(s):
· Division of Science and Mathematics

View future requirement(s):
· Fall 2023

View sample plan(s):
· Computer Science 3
· Computer Science 1
· Computer Science 2

View checkpoint chart:
· Computer Science B.A.
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CSCI 1301 - Problem Solving and Algorithm Development (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to different problem solving approaches, major programming paradigms, hardware, software, and data representations. Study of the functional programming paradigm, concentrating on recursion and inductively-defined data structures. Simple searching and sorting algorithms.
CSCI 1302 - Foundations of Computer Science (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Basic proof techniques, propositional and predicate logic, induction and invariants, program correctness proofs, basic summations, and simple Big-Oh analysis of algorithms.
MATH 2202 - Mathematical Perspectives (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Introduction to the methodology and subject matter of modern mathematics. Logic, sets, functions, relations, cardinality, and induction. Introductory number theory. Roots of complex polynomials. Other selected topics. prereq: 1101
MATH 3411 - Discrete and Combinatorial Mathematics
Credits: 4.0 [max 4.0]
Prerequisites: 1102 or higher or #
Typically offered: Every Fall
Propositional logic; equivalence relations; recurrence equations; structures and properties of undirected and directed graphs; applications of the aforementioned topics. prereq: 1102 or higher or instr consent
CSCI 2101 - Data Structures (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall
Introduction to data structures, including stacks, queues, trees, and graphs; implementation of abstract data types and introduction to software testing, using object-oriented techniques and reusable libraries. (4 hrs lect, 2 hrs lab) prereq: 1222 (or 1201) or 1301 or instr consent
CSCI 3413 - Computing Systems: Concepts
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Overview of computing systems, operating systems, and networks. Sources of complexity. Fundamental abstractions such as memory, processing, and communication; memory management and data storage; threads, processes, race conditions and deadlock; and inter-process and inter-computer communication. Modularity and organization; virtualization; protection and security; performance. [Note: Credit will not be granted if credit has been received for CSci 3401 or CSci 3402] prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
CSCI 3412 - Computing Systems: Practicum
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Lab experience with key computing systems tools and concepts. Command-line tools; shell and system scripting; system programming. Pointers and explicit memory management. Digital logic, gates, electronics, and microcomputers. Network organization and communication; client-server programming. Processes and threads; parallel and distributed computing. Performance and profiling. [Note: Credit will not be granted if credit has been received for CSci 3401 or CSci 3403] prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
CSCI 3501 - Algorithms and Computability
Credits: 5.0 [max 5.0]
Typically offered: Every Fall
Models of computation (such as Turing machines, deterministic and non-deterministic machines); approaches to the design of algorithms, determining correctness and efficiency of algorithms; complexity classes, NP-completeness, approximation algorithms. (4 hrs lect, 2 hrs lab) prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
CSCI 3601 - Software Design and Development
Credits: 5.0 [max 5.0]
Typically offered: Every Spring
Design and implementation of medium- and large-scale software systems. Principles of organizing and managing such designs and implementations throughout their lifetime. Designing for modularity and software reuse; use of libraries. Dynamics of working in groups. Group work on a substantial software project. prereq: C- or better in 2101 or instr consent
CSCI 4901 - Senior Seminar
Credits: 2.0 [max 2.0]
Grading Basis: S-N only
Typically offered: Every Fall & Spring
In-depth survey of literature in a specific computer-related field of the student's choice. Students analyze various articles or similarly published works, synthesize their contents, and present their work formally in a conference setting. Multiple writing and speaking experiences reviewed by faculty and classmates. Requires attendance and presentation at a student conference near the end of the semester in addition to regular class meetings. prereq: IS 1091 or Phil 2115 or instr consent, jr or sr
IS 1091W - Ethical and Social Implications of Technology (E/CR)
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
Description of appropriate technological advances. Historical development related to technology and its development cycle. Discussion of the ethical and social implications of technology.
CSCI 4403 - Systems: Data Mining
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
This course provides a broad introduction to the data mining field. The topics covered are: Data exploration, transformation and preprocessing. Handling data quality problems. Supervised and unsupervised models. Cross-Validation. Performance measures. Feature generation and feature selection techniques to optimize models? performance. Underfitting and Overfitting. Data Visualization. Introduction to Deep Learning methods and applications. Using SQL to data mine large data sets. prereq: 2101 or instr consent
CSCI 4409 - Systems: Programming for Parallel Architecture
Credits: 2.0 [max 2.0]
Typically offered: Periodic Spring
Study of programming models, languages, and approaches for parallel computer architectures. Topics include introduction to parallel computing and parallel architectures, approaches to program parallelization, mechanisms for communication and synchronization between tasks, and study of programming language support for parallel computation. prereq: 3412, 3413 or instr consent
CSCI 4410 - Systems: Cloud Computing Architectures
Credits: 2.0 [max 2.0]
Typically offered: Periodic Fall & Spring
Survey of cloud computing architectures such as "Infrastructure as Service" and "Platform as Service". Distributed computing, distributed data, and commonly utilized technologies such as software containers, virtual machines, and networking essentials will also be covered. prereq: 3412 or instr consent
CSCI 4453 - Systems: Database Systems
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Introduction to relational, object-relational, and object database systems. Topics include the relational model, SQL and related query languages, JDBC and database applications programming, database design, query processing and optimization, indexing techniques, and transaction management. prereq: 2101 or instr consent
CSCI 4454 - Systems: Robotics
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
An introduction to robotic systems. Topics may include robot classification, mechanical armatures, concepts of kinematics and coordinate systems, basic electronic circuits as applied to robotic systems, embedded system architecture and programming, communications hardware and protocols, and algorithms in robotics. Some lecture times may be replaced by supervised work in electronics lab and machine shop; times for this work are to be arranged with the instructor. prereq: 2101 or instr consent
CSCI 4457 - Systems: Ubiquitous Computing
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Study of the mechanisms and environments of ubiquitous computing. Topics may include computer and network architectures for ubiquitous computing, mobile computing mechanisms, multimodal interaction, pervasive software systems, location mechanisms, techniques for security and user-authentication, and experimental ubiquitous computing systems. prereq: 3412 or instr consent
CSCI 4458 - Systems: Bioinformatic Systems
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Introduction to bioinformatics with an emphasis on computer systems. Possible topics include: utilizing software for genetic sequencing, large-scale data management using databases, algorithms for construction of phylogenetic trees, bioinformatic scripting, and other tools for bioinformatics. prereq: 3412 or instr consent
CSCI 4506 - Theory: Fuzzy Logic and Fuzzy Sets
Credits: 2.0 [max 2.0]
Typically offered: Periodic Fall & Spring
Fuzzy logic and fuzzy sets are used in expert systems, controllers, pattern recognition, databases, decision making, robotics, and economics. The basic theory of fuzzy sets and fuzzy logic along with a brief survey of some of the current research. May include presentations and/or a project. prereq: 3501 or instr consent
CSCI 4553 - Theory: Evolutionary Computation and Artificial Intelligence
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Introduction to Evolutionary Computation as an Artificial Intelligence tool for developing solutions to problems that are difficult to describe precisely or solve formally, as well as comparisons with other AI techniques. Includes discussions of theoretical background and tools, implementation issues, and applications. prereq: 2101 or instr consent
CSCI 4554 - Theory: Cryptography
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Theory and applications of cryptography. Overview of necessary mathematical concepts. Discussion of algorithms and protocols including public and private key encryption, authentication, and zero knowledge proofs. prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
CSCI 4555 - Theory: Neural Networks and Machine Learning
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Study of the underlying theory, structure, and behavior of neural networks and of how neural networks compare to and can be used to supplement other methods of machine learning. Methods such as decision tree learning, inductive learning, reinforcement learning, supervised learning, and explanation-based learning are examined. Analysis of the strengths and weaknesses of various approaches to machine learning. Includes an implementation project. prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
CSCI 4557 - Theory: Quantum Computing
Credits: 4.0 [max 4.0]
Typically offered: Periodic Spring
Summarization of relevant mathematical and quantum mechanical concepts. Basic quantum algorithms concepts and simple algorithms are explored, along with Shor's algorithm, Grover's algorithm, and the quantum Fourier transform. prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101, CSci 3501 or Math 1101 or higher or instr consent
CSCI 4604 - Processes, Programming, and Languages: Graphical User Interfaces
Credits: 2.0 [max 2.0]
Typically offered: Periodic Fall & Spring
An exploration into designing Graphical User Interfaces. Aspects of human-computer interaction are discussed along with how to design good user interfaces. Students complete a user interface design project. prereq: 2101 or instr consent
CSCI 4605 - Processes, Programming, and Languages: Refactoring
Credits: 2.0 [max 2.0]
Prerequisites: 3601 or #
Typically offered: Periodic Fall & Spring
Introduction to methodologies for the long-term development and maintenance of software systems. Discussion of methods of fixing errors and extending functionality in a controlled manner that builds on and improves the underlying system design, as well as tools for regression testing to help catch introduced errors. There is a significant programming component as well as change documentation and classroom presentations. prereq: 3601 or instr consent
CSCI 4610 - Processes, Programming, and Languages: Programming for Cloud Computing
Credits: 2.0 [max 2.0]
Typically offered: Periodic Fall & Spring
Survey of cloud computing practices such as "Software as Services", and "Function as Service" with an emphasis on implementation. Topics to be covered include networking essentials, distributed algorithms, programming for software clusters, and stream programming. prereq: 3412 or instr consent
CSCI 4651 - Processes, Programming, and Languages: Programming Languages
Credits: 4.0 [max 4.0]
Prerequisites: 2101 or #
Typically offered: Periodic Fall & Spring
History of programming languages, formal specification of syntax and semantics of programming languages from a variety of paradigms (procedural, functional, logic-programming, object-oriented, and parallel paradigms), modern language features. prereq: 2101 or instr consent
CSCI 4654 - Processes, Programming, and Languages: Modern Functional Programming
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Survey of concepts, tools, and techniques from the realm of functional programming. Topics include higher order functions, currying, type systems, concurrency models, mechanisms for managing state, and methods of compilation and evaluation such as graph reduction and term rewriting. prereq: CSci 1302 or both Math 2202 and Math 3411, CSci 2101 or instr consent
CSCI 4656 - Processes, Programming, and Languages: Human-Computer Interaction and Interface Design
Credits: 4.0 [max 4.0]
Prerequisites: 2101 or #
Typically offered: Periodic Fall & Spring
Introduction to the design, evaluation, and implementation of interactive computing systems for human use with a particular emphasis on user interfaces. Possible domains include usability issues for desktop applications, embedded systems, and Web design. Student projects include evaluative studies and sample implementations. prereq: 2101 or instr consent
CSCI 4657 - Processes, Programming, and Languages: Programming Languages for Client-Server Systems
Credits: 4.0 [max 4.0]
Prerequisites: 3601 or #
Typically offered: Periodic Fall & Spring
Client/Server model and related Internet protocols. Server-side data storage. Common programming languages and technologies for client-side and server-side data processing. Related security issues. prereq: 3601 or instr consent
CSCI 4658 - Processes, Programming, and Languages: Usability, Design, and Mobile Technologies
Credits: 4.0 [max 4.0]
Prerequisites: 3601 or #
Typically offered: Periodic Fall
Design, evaluation, and use of innovative handheld, mobile, and wearable technologies. Topics include needs and issues unique to mobile users, as well as social and organizational impacts of mobile technologies. The course consists of a mix of lectures and seminar-style discussions, with projects incorporating important aspects of design, implementation, and evaluation. prereq: 3601 or instr consent
CSCI 4659 - Processes, Programming, and Languages: Measuring and Managing Software Quality
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall
Exploration of metrics and tools for assessing the health and quality of a software system, including technical debt, system complexity, duplication, and maintainability. Ways of communicating about software systems such as code reviews. Use of techniques such as refactoring and design patterns to improve systems. Includes substantial application and project work. prereq: 3601 or instr consent
CSCI 2701 - Introduction to Data Science (M/SR)
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 2701/Stat 2701
Typically offered: Every Spring
Same as Stat 2701. Introduction to data science and informatics and their application to real world scenarios. Computational approaches to data types; database creation including technologies such as SQL/no-SQL; data visualization; data reduction, condensation, partitioning; statistical modeling; and communicating results. prereq: CSci 1222 (or CSci 1201) or CSci 1251 or CSci 1301, Stat 1601 or Stat 2601 or Stat 2611 or instr consent
MATH 1101 - Calculus I (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall & Spring
Limits and continuity; the concepts, properties, and some techniques of differentiation, antidifferentiation, and definite integration and their connection by the Fundamental Theorem. Partial differentiation. Some applications. Students learn the basics of a computer algebra system. prereq: 1012, 1013 or placement
MATH 1102 - Calculus II (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall & Spring
Techniques of integration. Further applications involving mathematical modeling and solution of simple differential equations. Taylor's Theorem. Limits of sequences. Use and theory of convergence of power series. Students use a computer algebra system. prereq: 1101
MATH 2101 - Calculus III (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Multivariable and vector calculus. Three-dimensional analytic geometry; partial differentiation; multiple integration; gradient, divergence, and curl; line and surface integrals; divergence theorem; Green and Stokes theorems; applications. prereq: 1102 or instr consent
MATH 2202 - Mathematical Perspectives (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Introduction to the methodology and subject matter of modern mathematics. Logic, sets, functions, relations, cardinality, and induction. Introductory number theory. Roots of complex polynomials. Other selected topics. prereq: 1101
MATH 2401W - Differential Equations (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
First-order and second-order differential equations with methods of solution and applications, Laplace transforms, systems of equations, series solutions, existence and uniqueness theorems, the qualitative theory of differential equations. prereq: 1102 or instr consent
MATH 2501 - Probability and Stochastic Processes (M/SR)
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2501/Stat 2501
Typically offered: Every Fall
Same as Stat 2501. Probability theory; set theory, axiomatic foundations, conditional probability and independence, Bayes' rule, random variables. Transformations and expectations; expected values, moments, and moment generating functions. Common families of distributions; discrete and continuous distributions. Multiple random variables; joint and marginal distributions, conditional distributions and independence, covariance and correlation, multivariate distributions. Properties of random sample and central limit theorem. Markov chains, Poisson processes, birth and death processes, and queuing theory. prereq: 1101 or instr consent
MATH 3111 - Linear Algebra
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Math majors are highly encouraged to take this course in their second year. Matrix algebra, systems of linear equations, finite dimensional vector spaces, linear transformations, determinants, inner-product spaces, characteristic values and polynomials, eigenspaces, minimal polynomials, diagonalization of matrices, related topics; applications. [Note: no credit for students who have received cr for Math 2111] prereq: 1102 or CSci 1302 or instr consent
STAT 3501 - Survey Sampling (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Fall Even Year
Introduction to basic concepts and theory of designing surveys. Topics include sample survey designs including simple random sampling, stratified random sampling, cluster sampling, systemic sampling, multistage and two-phase sampling including ratio and regression estimation, Horvitz-Thomson estimation, questionnaire design, non-sampling errors, missing value-imputation method, sample size estimation, and other topics related to practical conduct of surveys. prereq: 1601 or 2601 or instr consent
STAT 3601 - Data Analysis (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Nature and objectives of statistical data analysis, exploratory and confirmatory data analysis techniques. Some types of statistical procedures; formulation of models, examination of the adequacy of the models. Some special models; simple regression, correlation analysis, multiple regression analysis, analysis of variance, use of statistical computer packages. prereq: 1601 or 2601 or 2611 or instr consent
STAT 3611 - Multivariate Statistical Analysis (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Analysis of categorical data. Loglinear models for two- and higher-dimensional contingency tables. Logistic regression models. Aspects of multivariate analysis, random vectors, sample geometry and random sampling, multivariate normal distribution, inferences about the mean vector, MANOVA. Analysis of covariance structures: principal components, factor analysis. Classification and grouping techniques: discrimination and classification, clustering, use of statistical computer packages. prereq: 1601 or 2601 or 2611 or instr consent
STAT 3901 - Statistical Communication
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Finding and utilizing sources of statistical information including data. Techniques for searching statistical literature, as well as reading and interpreting these sources. Principles of technical writing and communication in statistics. Writing, editing, and revising an extensive review paper on a statistical topic. Collaboration and statistical consulting skills needed for clients and project teams, explaining analyses, and writing reports understandable to non-statisticians. Attendance at senior seminar presentations is required. prereq: stat major, jr or sr status or instr consent