Twin Cities campus
 
Twin Cities Campus

Statistical Science B.S.

Statistics, School of
College of Liberal Arts
  • Program Type: Baccalaureate
  • Requirements for this program are current for Fall 2017
  • Required credits to graduate with this degree: 120
  • Required credits within the major: 63 to 68
  • Degree: Bachelor of Science
Statistics is the science of learning from data, measuring, controlling, and communicating uncertainty. It provides the navigation essential for controlling the course of scientific and societal advances. The statistical science BS is intended for students who express interest in graduate education in statistics or a related area. It shares a core sequence of applied statistics courses with the statistical practice BA program, but requires more mathematics. This program allow students the flexibility to take additional advanced mathematics courses that are expected for admission to many graduate programs.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Admission Requirements
Students must complete 1 courses before admission to the program.
For information about University of Minnesota admission requirements, visit the Office of Admissions website.
Required prerequisites
Preparatory Course
Complete STAT 3011 or STAT 3021 with a grade of C- or better in order to declare the Statistical Science B.S. program.
STAT 3011 - Introduction to Statistical Analysis [MATH] (4.0 cr)
or STAT 3021 - Introduction to Probability and Statistics (3.0 cr)
General Requirements
All students in baccalaureate degree programs are required to complete general University and college requirements including writing and liberal education courses. For more information about University-wide requirements, see the liberal education requirements. Required courses for the major, minor or certificate in which a student receives a D grade (with or without plus or minus) do not count toward the major, minor or certificate (including transfer courses).
Program Requirements
CLA degrees require students to complete 48 credits of upper division coursework taken at the 3xxx, 4xxx, or 5xxx level. Students may earn no more than one degree from the statistics program: a BA or a BS or a minor. All incoming CLA freshmen must complete the First Year Experience course sequence.
Calculus
Take 3 courses for a total of 12 credits.
MATH 1271 - Calculus I [MATH] (4.0 cr)
or MATH 1371 - CSE Calculus I [MATH] (4.0 cr)
or MATH 1571H - Honors Calculus I [MATH] (4.0 cr)
MATH 1272 - Calculus II (4.0 cr)
or MATH 1372 - CSE Calculus II (4.0 cr)
or MATH 1572H - Honors Calculus II (4.0 cr)
MATH 2263 - Multivariable Calculus (4.0 cr)
or MATH 2374 - CSE Multivariable Calculus and Vector Analysis (4.0 cr)
or MATH 2573H - Honors Calculus III (4.0 cr)
Linear Algebra
Take one of the following sequences, for either 4 or 8 credits. Students planning to minor in mathematics should take Option 2.
Option 1
CSCI 2033 - Elementary Computational Linear Algebra (4.0 cr)
or Option 2
MATH 2243 - Linear Algebra and Differential Equations (4.0 cr)
MATH 4242 - Applied Linear Algebra (4.0 cr)
or Option 3
MATH 2373 - CSE Linear Algebra and Differential Equations (4.0 cr)
MATH 4242 - Applied Linear Algebra (4.0 cr)
Programming for Statisticians
Take 1 course for 4 credits.
CSCI 1113 - Introduction to C/C++ Programming for Scientists and Engineers (4.0 cr)
or CSCI 2021 - Machine Architecture and Organization (4.0 cr)
Major Courses
Take 6 courses for a total of 23 credits.
STAT 3032 - Regression and Correlated Data (4.0 cr)
STAT 3701 - Introduction to Statistical Computing (3.0 cr)
STAT 4051 - Applied Statistics I (4.0 cr)
STAT 4052 - Applied Statistics II (4.0 cr)
STAT 5101 - Theory of Statistics I (4.0 cr)
or MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
STAT 5102 - Theory of Statistics II (4.0 cr)
Electives
Take a total of 14 credits, at least 4 credits of which must be listed below as STAT Electives.
Take 14 or more credit(s) from the following:
STAT Electives
Students planning to pursue a minor in mathematics, or an advanced degree in statistics or biostatistics should consult the undergraduate adviser for suggested coursework.
Take 4 or more credit(s) from the following:
· STAT 3501 - Internship in Statistical Practice (1.0 cr)
· STAT 5031 - Statistical Methods for Quality Improvement (4.0 cr)
· STAT 5201 - Sampling Methodology in Finite Populations (3.0 cr)
· STAT 5401 - Applied Multivariate Methods (3.0 cr)
· STAT 5421 - Analysis of Categorical Data (3.0 cr)
· STAT 5511 - Time Series Analysis (3.0 cr)
· STAT 5601 - Nonparametric Methods (3.0 cr)
· STAT 5931 - Topics in Statistics (3.0 cr)
· STAT 5932 {Inactive} (3.0 cr)
· Other Electives
Take at most 10 credit(s) from the following:
· CSCI 2011 - Discrete Structures of Computer Science (4.0 cr)
· CSCI 2011H - Honors Discrete Structures of Computer Science (4.0 cr)
· CSCI 2021 - Machine Architecture and Organization (4.0 cr)
· CSCI 2041 - Advanced Programming Principles (4.0 cr)
· CSCI 3003 - Introduction to Computing in Biology (3.0 cr)
· CSCI 3081W - Program Design and Development [WI] (4.0 cr)
· CSCI 4011 - Formal Languages and Automata Theory (4.0 cr)
· CSCI 4041 - Algorithms and Data Structures (4.0 cr)
· CSCI 4041H - Algorithms and Data Structures (4.0 cr)
· CSCI 4061 - Introduction to Operating Systems (4.0 cr)
· CSCI 4131 - Internet Programming (3.0 cr)
· CSCI 4211 - Introduction to Computer Networks (3.0 cr)
· CSCI 4511W - Introduction to Artificial Intelligence [WI] (4.0 cr)
· CSCI 4611 - Programming Interactive Computer Graphics and Games (3.0 cr)
· CSCI 4707 - Practice of Database Systems (3.0 cr)
· CSCI 4950 - Senior Software Project (3.0 cr)
· CSCI 4970W - Advanced Project Laboratory [WI] (3.0 cr)
· CSCI 5103 - Operating Systems (3.0 cr)
· CSCI 5105 - Introduction to Distributed Systems (3.0 cr)
· CSCI 5106 - Programming Languages (3.0 cr)
· CSCI 5115 - User Interface Design, Implementation and Evaluation (3.0 cr)
· CSCI 5117 - Developing the Interactive Web (3.0 cr)
· CSCI 5125 - Collaborative and Social Computing (3.0 cr)
· CSCI 5143 - Real-Time and Embedded Systems (3.0 cr)
· CSCI 5161 - Introduction to Compilers (3.0 cr)
· CSCI 5211 - Data Communications and Computer Networks (3.0 cr)
· CSCI 5221 - Foundations of Advanced Networking (3.0 cr)
· CSCI 5231 - Wireless and Sensor Networks (3.0 cr)
· CSCI 5271 - Introduction to Computer Security (3.0 cr)
· CSCI 5302 - Analysis of Numerical Algorithms (3.0 cr)
· CSCI 5304 - Computational Aspects of Matrix Theory (3.0 cr)
· CSCI 5403 - Computational Complexity (3.0 cr)
· CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
· CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
· CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics (3.0 cr)
· CSCI 5471 - Modern Cryptography (3.0 cr)
· CSCI 5481 - Computational Techniques for Genomics (3.0 cr)
· CSCI 5511 - Artificial Intelligence I (3.0 cr)
· CSCI 5512 - Artificial Intelligence II (3.0 cr)
· CSCI 5521 - Introduction to Machine Learning (3.0 cr)
· CSCI 5523 - Introduction to Data Mining (3.0 cr)
· CSCI 5525 - Machine Learning (3.0 cr)
· CSCI 5551 - Introduction to Intelligent Robotic Systems (3.0 cr)
· CSCI 5552 - Sensing and Estimation in Robotics (3.0 cr)
· CSCI 5561 - Computer Vision (3.0 cr)
· CSCI 5607 - Fundamentals of Computer Graphics 1 (3.0 cr)
· CSCI 5608 - Fundamentals of Computer Graphics II (3.0 cr)
· CSCI 5609 - Visualization (3.0 cr)
· CSCI 5611 - Animation & Planning in Games (3.0 cr)
· CSCI 5707 - Principles of Database Systems (3.0 cr)
· CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
· CSCI 5715 - From GPS and Virtual Globes to Spatial Computing (3.0 cr)
· CSCI 5801 - Software Engineering I (3.0 cr)
· CSCI 5802 - Software Engineering II (3.0 cr)
· MATH 2283 - Sequences, Series, and Foundations (3.0 cr)
· MATH 3283W - Sequences, Series, and Foundations: Writing Intensive [WI] (4.0 cr)
· MATH 4065 - Theory of Interest (4.0 cr)
· MATH 4067W - Actuarial Mathematics in Practice [WI] (3.0 cr)
· MATH 4151 - Elementary Set Theory (3.0 cr)
· MATH 4152 - Elementary Mathematical Logic (3.0 cr)
· MATH 4281 - Introduction to Modern Algebra (4.0 cr)
· MATH 4428 - Mathematical Modeling (4.0 cr)
· MATH 4512 - Differential Equations with Applications (3.0 cr)
· MATH 4567 - Applied Fourier Analysis (4.0 cr)
· MATH 4603 - Advanced Calculus I (4.0 cr)
· MATH 4604 - Advanced Calculus II (4.0 cr)
· MATH 4707 - Introduction to Combinatorics and Graph Theory (4.0 cr)
· MATH 5067 - Actuarial Mathematics I (4.0 cr)
· MATH 5068 - Actuarial Mathematics II (4.0 cr)
· MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I (4.0 cr)
· MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II (4.0 cr)
· MATH 5165 - Mathematical Logic I (4.0 cr)
· MATH 5166 - Mathematical Logic II (4.0 cr)
· MATH 5248 - Cryptology and Number Theory (4.0 cr)
· MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves (4.0 cr)
· MATH 5285H - Honors: Fundamental Structures of Algebra I (4.0 cr)
· MATH 5286H - Honors: Fundamental Structures of Algebra II (4.0 cr)
· MATH 5335 - Geometry I (4.0 cr)
· MATH 5336 - Geometry II (4.0 cr)
· MATH 5345H - Honors: Introduction to Topology (4.0 cr)
· MATH 5378 - Differential Geometry (4.0 cr)
· MATH 5385 - Introduction to Computational Algebraic Geometry (4.0 cr)
· MATH 5445 - Mathematical Analysis of Biological Networks (4.0 cr)
· MATH 5447 - Theoretical Neuroscience (4.0 cr)
· MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
· MATH 5485 - Introduction to Numerical Methods I (4.0 cr)
· MATH 5486 - Introduction To Numerical Methods II (4.0 cr)
· MATH 5525 - Introduction to Ordinary Differential Equations (4.0 cr)
· MATH 5535 - Dynamical Systems and Chaos (4.0 cr)
· MATH 5583 - Complex Analysis (4.0 cr)
· MATH 5587 - Elementary Partial Differential Equations I (4.0 cr)
· MATH 5588 - Elementary Partial Differential Equations II (4.0 cr)
· MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
· MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
· MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
· MATH 5654 - Prediction and Filtering (4.0 cr)
· MATH 5705 - Enumerative Combinatorics (4.0 cr)
· MATH 5707 - Graph Theory and Non-enumerative Combinatorics (4.0 cr)
· MATH 5711 - Linear Programming and Combinatorial Optimization (4.0 cr)
· PUBH 3415 - Introduction to Clinical Trials - Online (3.0 cr)
· PUBH 6420 - Introduction to SAS Programming (1.0 cr)
· PUBH 6431 - Topics in Hierarchical Bayesian Analysis (1.0 cr)
· PUBH 6432 - Biostatistical Methods in Translational and Clinical Research (1.0 cr)
· PUBH 6470 - SAS Procedures and Data Anaysis (3.0 cr)
· PUBH 7415 - Introduction to Clinical Trials (3.0 cr)
· WRIT 3562W - Technical and Professional Writing [WI] (4.0 cr)
· CSCI 4203 - Computer Architecture (4.0 cr)
or EE 4363 - Computer Architecture and Machine Organization (4.0 cr)
· CSCI 4921 - History of Computing [TS, HIS] (3.0 cr)
or HSCI 4321 - History of Computing [TS, HIS] (3.0 cr)
· CSCI 5204 - Advanced Computer Architecture (3.0 cr)
or EE 5364 - Advanced Computer Architecture (3.0 cr)
Take at most 6 credit(s) from the following:
· STAT 1905 {Inactive} (3.0 cr)
Senior Project: Consultation and Communication for Statisticians
This course is required of all majors, including double-majors who have met the writing intensive requirement in the other major.
Take exactly 1 course(s) totaling exactly 3 credit(s) from the following:
· STAT 4893W - Consultation and Communication for Statisticians [WI] (3.0 cr)
Upper Division Writing Intensive within the major
Students are required to take one upper division writing intensive course within the major. If that requirement has not been satisfied within the core major requirements, students must choose one course from the following list. Some of these courses may also fulfill other major requirements.
Take 0 - 1 course(s) from the following:
· STAT 4893W - Consultation and Communication for Statisticians [WI] (3.0 cr)
 
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STAT 3011 - Introduction to Statistical Analysis (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: (Select a set)
Typically offered: Every Fall, Spring & Summer
Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.
STAT 3021 - Introduction to Probability and Statistics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall, Spring & Summer
This is an introductory course in statistics whose primary objectives are to teach students the theory of elementary probability theory and an introduction to the elements of statistical inference, including testing, estimation, and confidence statements. prereq: Math 1272
MATH 1271 - Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00067 - Math 1271/Math 1281/Math 1371/
Typically offered: Every Fall, Spring & Summer
Differential calculus of functions of a single variable, including polynomial, rational, exponential, and trig functions. Applications, including optimization and related rates problems. Single variable integral calculus, using anti-derivatives and simple substitution. Applications may include area, volume, work problems. prereq: 4 yrs high school math including trig or satisfactory score on placement test or grade of at least C- in [1151 or 1155]
MATH 1371 - CSE Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1142/1271/1281/1371/1571H
Typically offered: Every Fall & Spring
Differentiation of single-variable functions, basics of integration of single-variable functions. Applications: max-min, related rates, area, curve-sketching. Use of calculator, cooperative learning. prereq: CSE or pre-bioprod concurrent registration is required (or allowed) in biosys engn (PRE), background in [precalculus, geometry, visualization of functions/graphs], instr consent; familiarity with graphing calculators recommended
MATH 1571H - Honors Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00067 - Math 1142/1271/1281/1371/1571H
Grading Basis: A-F only
Typically offered: Every Fall
Differential/integral calculus of functions of a single variable. Emphasizes hard problem-solving rather than theory. prereq: Honors student and permission of University Honors Program
MATH 1272 - Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1272/1282/1252/1372/1572
Typically offered: Every Fall, Spring & Summer
Techniques of integration. Calculus involving transcendental functions, polar coordinates. Taylor polynomials, vectors/curves in space, cylindrical/spherical coordinates. prereq: [1271 or equiv] with grade of at least C-
MATH 1372 - CSE Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1272/1282/1252/1372/1572
Typically offered: Every Spring
Techniques of integration. Calculus involving transcendental functions, polar coordinates, Taylor polynomials, vectors/curves in space, cylindrical/spherical coordinates. Use of calculators, cooperative learning. prereq: Grade of at least C- in [1371 or equiv], CSE or pre-Bioprod/Biosys Engr
MATH 1572H - Honors Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: 00068 - Math 1272/1282/1252/1372/1572
Grading Basis: A-F only
Typically offered: Every Spring
Continuation of 1571. Infinite series, differential calculus of several variables, introduction to linear algebra. prereq: 1571H, honors student, permission of University Honors Program
MATH 2263 - Multivariable Calculus
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2263/2373/2573H/3251
Typically offered: Every Fall, Spring & Summer
Derivative as linear map. Differential/integral calculus of functions of several variables, including change of coordinates using Jacobians. Line/surface integrals. Gauss, Green, Stokes Theorems. prereq: [1272 or 1372 or 1572] w/grade of at least C-
MATH 2374 - CSE Multivariable Calculus and Vector Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2263/2373/2573H/3251
Typically offered: Every Fall & Spring
Derivative as linear map. Differential/integral calculus of functions of several variables, including change of coordinates using Jacobians. Line/surface integrals. Gauss, Green, Stokes theorems. Use of computer technology. prereq: [1272 or 1282 or 1372 or 1572] w/grade of at least C-, CSE or pre-Bioprod/Biosys Engr
MATH 2573H - Honors Calculus III
Credits: 4.0 [max 4.0]
Course Equivalencies: 00069 - Math 2263/2374/3251
Grading Basis: A-F only
Typically offered: Every Fall
Integral calculus of several variables. Vector analysis, including theorems of Gauss, Green, Stokes. prereq: Math 1572H or Math 2574H, honors student and permission of University Honors Program
CSCI 2033 - Elementary Computational Linear Algebra
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Matrices/linear transformations, basic theory. Linear vector spaces. Inner product spaces. Systems of linear equations, Eigenvalues, singular values. Algorithms/computational matrix methods using MATLAB. Use of matrix methods to solve variety of computer science problems. prereq: [MATH 1271 or MATH 1371], [1113 or 1133 or knowledge of programming concepts]
MATH 2243 - Linear Algebra and Differential Equations
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2243/2373/2573
Typically offered: Every Fall, Spring & Summer
Linear algebra: basis, dimension, matrices, eigenvalues/eigenvectors. Differential equations: first-order linear, separable; second-order linear with constant coefficients; linear systems with constant coefficients. prereq: [1272 or 1282 or 1372 or 1572] w/grade of at least C-
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: 01212 - Math 4242/Math 4457
Typically offered: Every Fall, Spring & Summer
Systems of linear equations, vector spaces, subspaces, bases, linear transformations, matrices, determinants, eigenvalues, canonical forms, quadratic forms, applications. prereq: 2243 or 2373 or 2573
MATH 2373 - CSE Linear Algebra and Differential Equations
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2243/2373/2573
Typically offered: Every Fall & Spring
Linear algebra: basis, dimension, eigenvalues/eigenvectors. Differential equations: linear equations/systems, phase space, forcing/resonance, qualitative/numerical analysis of nonlinear systems, Laplace transforms. Use of computer technology. prereq: [1272 or 1282 or 1372 or 1572] w/grade of at least C-, CSE or pre-Bio Prod/Biosys Engr
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: 01212 - Math 4242/Math 4457
Typically offered: Every Fall, Spring & Summer
Systems of linear equations, vector spaces, subspaces, bases, linear transformations, matrices, determinants, eigenvalues, canonical forms, quadratic forms, applications. prereq: 2243 or 2373 or 2573
CSCI 1113 - Introduction to C/C++ Programming for Scientists and Engineers
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Programming for scientists/engineers. C/C++ programming constructs, object-oriented programming, software development, fundamental numerical techniques. Exercises/examples from various scientific fields. prereq: Math 1271 or Math 1371 or Math 1571H or instr consent
CSCI 2021 - Machine Architecture and Organization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Introduction to hardware/software components of computer system. Data representation, boolean algebra, machine-level programs, instruction set architecture, processor organization, memory hierarchy, virtual memory, compiling, linking. Programming in C. prereq: 1913 or 1933 or instr consent
STAT 3032 - Regression and Correlated Data
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
This is a second course in statistics with a focus on linear regression and correlated data. The intent of this course is to prepare statistics, economics and actuarial science students for statistical modeling needed in their discipline. The course covers the basic concepts of linear algebra and computing in R, simple linear regression, multiple linear regression, statistical inference, model diagnostics, transformations, model selection, model validation, and basics of time series and mixed models. Numerous datasets will be analyzed and interpreted using the open-source statistical software R. prereq: STAT 3011 or STAT 3021
STAT 3701 - Introduction to Statistical Computing
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Elementary Monte Carlo, simulation studies, elementary optimization, programming in R, and graphics in R. Prerequisites: (MATH 1272 or 1372 or 1572H), CSCI 1113, and STAT 3032
STAT 4051 - Applied Statistics I
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
This is the first semester of the Applied Statistics sequence for majors seeking a BA or BS in statistics. The course introduces a wide variety of applied statistical methods, methodology for identifying types of problems and selecting appropriate methods for data analysis, to correctly interpret results, and to provide hands-on experience with real-life data analysis. The course covers basic concepts of single factor analysis of variance (ANOVA) with fixed and random effects, factorial designs, analysis of covariance (ANCOVA), repeated measures analysis with mixed effect models, principal component analysis (PCA) and multidimensional scaling, robust estimation and regression methods, and rank tests. Numerous datasets will be analyzed and interpreted, using the open-source statistical software R and Rstudio.
STAT 4052 - Applied Statistics II
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This is the second semester of the core Applied Statistics sequence for majors seeking a BA or BS in statistics. Both Stat 4051 and Stat 4052 are required in the major. The course introduces a wide variety of applied statistical methods, methodology for identifying types of problems and selecting appropriate methods for data analysis, to correctly interpret results, and to provide hands-on experience with real-life data analysis. The course covers basic concepts of classification, both classical methods of linear classification rules as well as modern computer-intensive methods of classification trees, and the estimation of classification errors by splitting data into training and validation data sets; non-linear parametric regression; nonparametric regression including kernel estimates; categorical data analysis; logistic and Poisson regression; and adjustments for missing data. Numerous datasets will be analyzed and interpreted, using the open-source statistical software R and Rstudio.
STAT 5101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Course Equivalencies: 00259 - Math 5651/Stat 5101
Typically offered: Every Fall
Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: [Math 2263 or Math 2374 or Math 2573H], [CSCI 2033 or Math 2373 or Math 2243]
MATH 5651 - Basic Theory of Probability and Statistics
Credits: 4.0 [max 4.0]
Course Equivalencies: 00259 - MATH 4653/Math 5651/Stat 5101
Typically offered: Every Fall & Spring
Logical development of probability, basic issues in statistics. Probability spaces, random variables, their distributions/expected values. Law of large numbers, central limit theorem, generating functions, sampling, sufficiency, estimation. prereq: [2263 or 2374 or 2573], [2243 or 2373]; [2283 or 2574 or 3283] recommended.
STAT 5102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Course Equivalencies: Stat 4102/5102
Typically offered: Every Spring
Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: 5101 or Math 5651
STAT 3501 - Internship in Statistical Practice
Credits: 1.0 [max 2.0]
Prerequisites: Statistics Major
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
Internship for statistics undergraduate students, in the university or in the community with supervision provided by statistics faculty and on-site mentors. prereq: Statistics Major
STAT 5031 - Statistical Methods for Quality Improvement
Credits: 4.0 [max 4.0]
Typically offered: Periodic Spring
Random variability/sampling. Controlling statistical process. Shewhart/accumulative charting. Analyzing plant data, trend surface, and variance/design of experiments. prereq: [3021 or 3022 or 4102 or 5021 or 5102 or 8102], Math 1272
STAT 5201 - Sampling Methodology in Finite Populations
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Simple random, systematic, stratified, unequal probability sampling. Ratio, model based estimation. Single stage, multistage, adaptive cluster sampling. Spatial sampling. prereq: 3022 or 4102 or 5021 or 5102 or instr consent
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering.
STAT 5421 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models.
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: Theoretical understanding
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: 3022 or 4102 or 5021 or 5102 or instr consent
STAT 5931 - Topics in Statistics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Topics vary according to student needs and available staff.
CSCI 2011 - Discrete Structures of Computer Science
Credits: 4.0 [max 4.0]
Course Equivalencies: 02004
Typically offered: Every Fall & Spring
Foundations of discrete mathematics. Sets, sequences, functions, big-O, propositional/predicate logic, proof methods, counting methods, recursion/recurrences, relations, trees/graph fundamentals. prereq: MATH 1271 or MATH 1371 or instr consent
CSCI 2011H - Honors Discrete Structures of Computer Science
Credits: 4.0 [max 4.0]
Course Equivalencies: 02004 - CSci 2011/CSci 2011H
Grading Basis: A-F only
Typically offered: Every Spring
Foundations of discrete mathematics. Sets, sequences, functions, big-O, propositional/predicate logic, proof methods, counting methods, recursion/recurrences, relations, trees/graph fundamentals. Advanced topics in discrete structures as time permits. prereq: [MATH 1271 or MATH 1371 or MATH 1571H], honors student.
CSCI 2021 - Machine Architecture and Organization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Introduction to hardware/software components of computer system. Data representation, boolean algebra, machine-level programs, instruction set architecture, processor organization, memory hierarchy, virtual memory, compiling, linking. Programming in C. prereq: 1913 or 1933 or instr consent
CSCI 2041 - Advanced Programming Principles
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Principles/techniques for creating correct, robust, modular programs. Computing with symbolic data, recursion/induction, functional programming, impact of evaluation strategies, parallelism. Organizing data/computations around types. Search-based programming, concurrency, modularity. prereq: [1913 or 1933], 2011
CSCI 3003 - Introduction to Computing in Biology
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Emphasizes computing tasks common in biology. Programming techniques: variables, flow control, input/output, strings, pattern matching, arrays, hash tables, functions, subroutines. Concepts in computing: algorithms, complexity, documentation, regular expressions/grammars, local variables, encapsulation. Students complete lab projects in Perl language. prereq: 1002H or Biol 1002 or 1009H or Biol 1009 or equiv or instr consent
CSCI 3081W - Program Design and Development (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00728
Typically offered: Every Fall & Spring
Principles of programming design/analysis. Concepts in software development. Uses C/C++ language to illustrate key ideas in program design/development, data structures, debugging, files, I/O, state machines, testing, coding standards. prereq: [2021, 2041]; CS upper div, CS grad, Univ. honors student, or dept. permission
CSCI 4011 - Formal Languages and Automata Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Logical/mathematical foundations of computer science. Formal languages, their correspondence to machine models. Lexical analysis, string matching, parsing. Decidability, undecidability, limits of computability. Computational complexity. prereq: 2041 or instr consent
CSCI 4041 - Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: 02015
Typically offered: Every Fall & Spring
Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables and hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees. prereq: [(1913 or 1933) and 2011] or instr consent; cannot be taken for grad CSci cr
CSCI 4041H - Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: 02015
Grading Basis: A-F only
Typically offered: Every Fall
Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables/hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees. prereq: [(1913 or 1933) and 2011, honors student] or instr consent. Cannot be taken for Grad CSci cr
CSCI 4061 - Introduction to Operating Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Processes/threads, process coordination, interprocess communication, asynchronous events, memory management/file systems. Systems programming projects using operating system interfaces and program development tools. prereq: 2021 or EE 2361; CS upper div, CS minor, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci.
CSCI 4131 - Internet Programming
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4131/5131
Typically offered: Every Fall & Spring
Issues in internet programming. Internet history, architecture/protocols, network programming, Web architecture. Client-server architectures and protocols. Client-side programming, server-side programming, dynamic HTML, Java programming, object-oriented architecture/design, distributed object computing, Web applications. prereq: 4061, 4211 recommended, cannot be taken for grad CSci cr
CSCI 4211 - Introduction to Computer Networks
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4211/5211
Typically offered: Every Fall & Spring
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, routing, transport, network programming interfaces, networked applications. Examples from Ethernet, Token Ring, TCP/IP, HTTP, WWW. prereq: 4061 or instr consent; basic knowledge of [computer architecture, operating systems] recommended, cannot be taken for grad CSci cr
CSCI 4511W - Introduction to Artificial Intelligence (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 01666 - CSci 4511W/CSci 5511
Prerequisites: 2041 or #
Typically offered: Every Spring
Problem solving, search, inference techniques. Knowledge representation. Planning. Machine learning. Robotics. Lisp programming language. Cannot be taken for grad CSci credit. prereq: 2041 or instr consent
CSCI 4611 - Programming Interactive Computer Graphics and Games
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Tools/techniques for programming games/interactive computer graphics. Event loops, rendering/animation, polygonal models, texturing, physical simulation. Modern graphics toolkits. History/future of computer games technology. Social impact of interactive computer graphics. prereq: 2021 or instr consent
CSCI 4707 - Practice of Database Systems
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4707/5707/INET 4707
Typically offered: Every Fall & Spring
Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications. prereq: 4041 or instr consent
CSCI 4950 - Senior Software Project
Credits: 3.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Student teams develop a software system, distribute system to users, and extend/maintain it in response to their needs. Software engineering techniques. Software development, team participation, leadership. prereq: Upper div CSci, instr consent
CSCI 4970W - Advanced Project Laboratory (WI)
Credits: 3.0 [max 9.0]
Typically offered: Every Fall & Spring
Formulate and solve open-ended project: design, implement, interface, document, test. Team work strongly encouraged. Arranged with CSci faculty. prereq: Upper div CSci, 4061, instr consent; cannot be taken for grad cr
CSCI 5103 - Operating Systems
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples. prereq: 4061 or instr consent
CSCI 5105 - Introduction to Distributed Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling. prereq: [5103 or equiv] or instr consent
CSCI 5106 - Programming Languages
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages. prereq: 4011 or instr consent
CSCI 5115 - User Interface Design, Implementation and Evaluation
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project. prereq: 4041 or instr consent
CSCI 5117 - Developing the Interactive Web
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Hands-on design experience using modern web development tools. Students work in teams to develop software programs using each of four toolkits. Analyze developments in forum posts and classroom discussions. prereq: 4131 or 5131 or instr consent; upper div or grad in CSci recommended
CSCI 5125 - Collaborative and Social Computing
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Introduction to computer-supported cooperative work, social computing. Technology, research methods, theory, case studies of group computing systems. Readings, hands-on experience. prereq: 5115 or instr consent
CSCI 5143 - Real-Time and Embedded Systems
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Periodic Spring
Real-time systems that require timely response by computer to external stimulus. Embedded systems in which computer is part of machine. Increasing importance of these systems in commercial products. How to control robots and video game consoles. Lecture, informal lab. prereq: [4061 or instr consent], experience with C language
CSCI 5161 - Introduction to Compilers
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Techniques for translating modern programming languages to intermediate forms or machine-executable instructions/their organization into compiler. Lexical analysis, syntax analysis, semantic analysis, data flow analysis, code generation. Compiler project for prototypical language. prereq: [2021, 5106] or instr consent
CSCI 5211 - Data Communications and Computer Networks
Credits: 3.0 [max 3.0]
Course Equivalencies: 00495 - CSci 4211/5211
Typically offered: Every Fall
Cconcepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW. prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5221 - Foundations of Advanced Networking
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Design principles, protocol mechanisms. Network algorithmics, implementation techniques. Advanced network architectures, state-of-art/emerging networking technologies/applications, network modeling. Simulation, experiments. prereq: 4211 or 5211 or equiv; intro course in computer networks recommended
CSCI 5231 - Wireless and Sensor Networks
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Enabling technologies, including hardware, embedded operating systems, programming environment, communication, networking, and middleware services. Hands-on experience in programming tiny communication devices. prereq: 4211 or 5211 or instr consent
CSCI 5271 - Introduction to Computer Security
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Concepts of computer, network, and information security. Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls. prereq: 4061 or equiv or instr consent
CSCI 5302 - Analysis of Numerical Algorithms
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques. prereq: 2031 or 2033 or instr consent
CSCI 5304 - Computational Aspects of Matrix Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods. prereq: 2031 or 2033 or instr consent
CSCI 5403 - Computational Complexity
Credits: 3.0 [max 3.0]
Prerequisites: 4041 or #
Typically offered: Periodic Fall & Spring
Computational models, complexity measures in each model, and related complexity classes. prereq: 4041 or instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads. prereq: 4041 or instr consent
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Computational methods for analyzing, integrating, and deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets. prereq: 3003 or 4041 or instr consent
CSCI 5471 - Modern Cryptography
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Introduction to cryptography. Theoretical foundations, practical applications. Threats, attacks, and countermeasures, including cryptosystems and cryptographic protocols. Secure systems/networks. History of cryptography, encryption (conventional, public key), digital signatures, hash functions, message authentication codes, identification, authentication, applications. prereq: [2011, 4041, [familiarity with number theory or finite fields]] or instr consent
CSCI 5481 - Computational Techniques for Genomics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks. prereq: 4041 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits: 3.0 [max 3.0]
Course Equivalencies: 01666
Prerequisites: [2041 or #], grad student
Typically offered: Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 5512W/5512
Typically offered: Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods. prereq: [STAT 3021, 4041] or instr consent
CSCI 5521 - Introduction to Machine Learning
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. prereq: [[2031 or 2033], STAT 3021] or instr consent
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
CSCI 5551 - Introduction to Intelligent Robotic Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Transformations, kinematics/inverse kinematics, dynamics, control. Sensing (robot vision, force control, tactile sensing), applications of sensor-based robot control, robot programming, mobile robotics, microrobotics. prereq: 2031 or 2033 or instr consent
CSCI 5552 - Sensing and Estimation in Robotics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Bayesian estimation, maximum likelihood estimation, Kalman filtering, particle filtering. Sensor modeling and fusion. Mobile robot motion estimation (odometry, inertial,laser scan matching, vision-based) and path planning. Map representations, landmark-based localization, Markov localization, simultaneous localization/mapping (SLAM), multi-robot localization/mapping. prereq: [5551, Stat 3021] or instr consent
CSCI 5561 - Computer Vision
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: 5511 or instr consent
CSCI 5607 - Fundamentals of Computer Graphics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Fundamental algorithms in computer graphics. Emphasizes programming projects in C/C++. Scan conversion, hidden surface removal, geometrical transformations, projection, illumination/shading, parametric cubic curves, texture mapping, antialising, ray tracing. Developing graphics software, graphics research. prereq: concurrent registration is required (or allowed) in 2033, concurrent registration is required (or allowed) in 3081
CSCI 5608 - Fundamentals of Computer Graphics II
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Advanced topics in image synthesis, modeling, rendering. Image processing, image warping, global illumination, non-photorealistic rendering, texture synthesis. Parametric cubic surfaces, subdivision surfaces, acceleration techniques, advanced texture mapping. Programming in C/C++. prereq: 5607 or instr consent
CSCI 5609 - Visualization
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent
CSCI 5611 - Animation & Planning in Games
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
Theory behind algorithms used to bring virtual worlds to life. Computer animation topics. Real-time, interactive techniques used in modern games. Physically-based animation, motion planning, character animation, simulation in virtual worlds. prereq: 4041 or 4611 or instr consent
CSCI 5707 - Principles of Database Systems
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4707/5707/INET 4707
Typically offered: Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends. prereq: [4041 or instr consent], grad student
CSCI 5708 - Architecture and Implementation of Database Management Systems
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules. prereq: 4707 or 5707 or instr consent
CSCI 5715 - From GPS and Virtual Globes to Spatial Computing
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Mathematical concepts, geo-information, representations, algorithms, data-structures/access methods, analysis, architectures, interfaces, reasoning, time. prereq: Familiarity with Java, C++, or Python
CSCI 5801 - Software Engineering I
Credits: 3.0 [max 3.0]
Prerequisites: 2041 or #
Typically offered: Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent
CSCI 5802 - Software Engineering II
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801. prereq: 5801 or instr consent
MATH 2283 - Sequences, Series, and Foundations
Credits: 3.0 [max 3.0]
Course Equivalencies: Math 2283/3283W
Typically offered: Every Fall & Spring
Mathematical reasoning. Elements of logic. Mathematical induction. Real number system. General, monotone, recursively defined sequences. Convergence of infinite series/sequences. Taylor's series. Power series with applications to differential equations. Newton's method. prereq: [concurrent registration is required (or allowed) in 2243 or concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2373 or concurrent registration is required (or allowed) in 2374] w/grade of at least C-
MATH 3283W - Sequences, Series, and Foundations: Writing Intensive (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 2283/3283W
Typically offered: Every Fall & Spring
Introduction to reasoning used in advanced mathematics courses. Logic, mathematical induction, real number system, general/monotone/recursively defined sequences, convergence of infinite series/sequences, Taylor's series, power series with applications to differential equations, Newton's method. Writing-intensive component. prereq: [concurrent registration is required (or allowed) in 2243 or concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2373 or concurrent registration is required (or allowed) in 2374] w/grade of at least C-
MATH 4065 - Theory of Interest
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Time value of money, compound interest and general annuities, loans, bonds, general cash flows, basic financial derivatives and their valuation. Primarily for students who are interested in actuarial mathematics. prereq: 1272 or 1372 or 1572
MATH 4067W - Actuarial Mathematics in Practice (WI)
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
Real world actuarial problems that require integration of mathematical skills with knowledge from other disciplines such as economics, statistics, and finance. Communication and interpersonal skills are enhanced by teamwork/presentations to the practitioner actuaries who co-instruct. prereq: 4065, ACCT 2050, ECON 1101, ECON 1102
MATH 4151 - Elementary Set Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Basic properties of operations on sets, cardinal numbers, simply and well-ordered sets, ordinal numbers, axiom of choice, axiomatics. prereq: One soph math course or instr consent
MATH 4152 - Elementary Mathematical Logic
Credits: 3.0 [max 3.0]
Course Equivalencies: Math 4152/5165
Typically offered: Every Spring
Propositional logic. Predicate logic: notion of a first order language, a deductive system for first order logic, first order structures, Godel's completeness theorem, axiom systems, models of formal theories. prereq: one soph math course or instr consent
MATH 4281 - Introduction to Modern Algebra
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall
Equivalence relations, greatest common divisor, prime decomposition, modular arithmetic, groups, rings, fields, Chinese remainder theorem, matrices over commutative rings, polynomials over fields. prereq: 2283 or 3283 or instr consent
MATH 4428 - Mathematical Modeling
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Modeling techniques for analysis/decision-making in industry. Optimization (sensitivity analysis, Lagrange multipliers, linear programming). Dynamical modeling (steady-states, stability analysis, eigenvalue methods, phase portraits, simulation). Probabilistic methods (probability/statistical models, Markov chains, linear regression, simulation). prereq: 2243 or 2373 or 2573
MATH 4512 - Differential Equations with Applications
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Laplace transforms, series solutions, systems, numerical methods, plane autonomous systems, stability. prereq: 2243 or 2373 or 2573
MATH 4567 - Applied Fourier Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Fourier series, integral/transform. Convergence. Fourier series, transform in complex form. Solution of wave, heat, Laplace equations by separation of variables. Sturm-Liouville systems, finite Fourier, fast Fourier transform. Applications. Other topics as time permits. prereq: 2243 or 2373 or 2573
MATH 4603 - Advanced Calculus I
Credits: 4.0 [max 4.0]
Course Equivalencies: 01072 - Math 4606/Math 5615/Math 5616
Typically offered: Every Fall, Spring & Summer
Axioms for the real numbers. Techniques of proof for limits, continuity, uniform convergence. Rigorous treatment of differential/integral calculus for single-variable functions. prereq: [[2243 or 2373], [2263 or 2374]] or 2574 or instr consent
MATH 4604 - Advanced Calculus II
Credits: 4.0 [max 4.0]
Course Equivalencies: 01776
Typically offered: Every Spring
Sequel to MATH 4603. Topology of n-dimensional Euclidean space. Rigorous treatment of multivariable differentiation and integration, including chain rule, Taylor's Theorem, implicit function theorem, Fubini's Theorem, change of variables, Stokes' Theorem. prereq: 4603 or 5615 or instr consent
MATH 4707 - Introduction to Combinatorics and Graph Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Existence, enumeration, construction, algorithms, optimization. Pigeonhole principle, bijective combinatorics, inclusion-exclusion, recursions, graph modeling, isomorphism. Degree sequences and edge counting. Connectivity, Eulerian graphs, trees, Euler's formula, network flows, matching theory. Mathematical induction as proof technique. prereq: 2243, [2283 or 3283]
MATH 5067 - Actuarial Mathematics I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Future lifetime random variable, survival function. Insurance, life annuity, future loss random variables. Net single premium, actuarial present value, net premium, net reserves. prereq: 4065, [one sem [4xxx or 5xxx] [probability or statistics] course]
MATH 5068 - Actuarial Mathematics II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Multiple decrement insurance, pension valuation. Expense analysis, gross premium, reserves. Problem of withdrawals. Regulatory reserving systems. Minimum cash values. Additional topics at instructor's discretion. prereq: 5067
MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Mathematical background (e.g., partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods--including Monte Carlo simulation). Interest-rate derivative securities, exotic options, risk theory. First course of two-course sequence. prereq: Two yrs calculus, basic computer skills
MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Mathematical background such as partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods (including Monte Carlo simulation), interest-rate derivative securities, exotic options, risk theory. prereq: 5075
MATH 5165 - Mathematical Logic I
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4152/5165
Typically offered: Every Fall
Theory of computability: notion of algorithm, Turing machines, primitive recursive functions, recursive functions, Kleene normal form, recursion theorem. Propositional logic. prereq: 2283 or 3283 or Phil 5201 or CSci course in theory of algorithms or instr consent
MATH 5166 - Mathematical Logic II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
First-order logic: provability/truth in formal systems, models of axiom systems, Godel's completeness theorem. Godel's incompleteness theorem: decidable theories, representability of recursive functions in formal theories, undecidable theories, models of arithmetic. prereq: 5165
MATH 5248 - Cryptology and Number Theory
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Classical cryptosystems. One-time pads, perfect secrecy. Public key ciphers: RSA, discrete log. Euclidean algorithm, finite fields, quadratic reciprocity. Message digest, hash functions. Protocols: key exchange, secret sharing, zero-knowledge proofs. Probablistic algorithms: pseudoprimes, prime factorization. Pseudo-random numbers. Elliptic curves. prereq: 2 sems soph math
MATH 5251 - Error-Correcting Codes, Finite Fields, Algebraic Curves
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Information theory: channel models, transmission errors. Hamming weight/distance. Linear codes/fields, check bits. Error processing: linear codes, Hamming codes, binary Golay codes. Euclidean algorithm. Finite fields, Bose-Chaudhuri-Hocquenghem codes, polynomial codes, Goppa codes, codes from algebraic curves. prereq: 2 sems soph math
MATH 5285H - Honors: Fundamental Structures of Algebra I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Review of matrix theory, linear algebra. Vector spaces, linear transformations over abstract fields. Group theory, including normal subgroups, quotient groups, homomorphisms, class equation, Sylow's theorems. Specific examples: permutation groups, symmetry groups of geometric figures, matrix groups. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]
MATH 5286H - Honors: Fundamental Structures of Algebra II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Ring/module theory, including ideals, quotients, homomorphisms, domains (unique factorization, euclidean, principal ideal), fundamental theorem for finitely generated modules over euclidean domains, Jordan canonical form. Introduction to field theory, including finite fields, algebraic/transcendental extensions, Galois theory. prereq: 5285
MATH 5335 - Geometry I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Advanced two-dimensional Euclidean geometry from a vector viewpoint. Theorems/problems about triangles/circles, isometries, connections with Euclid's axioms. Hyperbolic geometry, how it compares with Euclidean geometry. prereq: [2243 or 2373 or 2573], [concurrent registration is required (or allowed) in 2263 or concurrent registration is required (or allowed) in 2374 or concurrent registration is required (or allowed) in 2574]
MATH 5336 - Geometry II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Projective geometry, including: relation to Euclidean geometry, finite geometries, fundamental theorem of projective geometry. N-dimensional Euclidean geometry from a vector viewpoint. Emphasizes N=3, including: polyhedra, spheres, isometries. prereq: 5335
MATH 5345H - Honors: Introduction to Topology
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Rigorous introduction to general topology. Set theory, Euclidean/metric spaces, compactness/connectedness. May include Urysohn metrization, Tychonoff theorem or fundamental group/covering spaces. prereq: [2263 or 2374 or 2573], [concurrent registration is required (or allowed) in 2283 or concurrent registration is required (or allowed) in 2574 or concurrent registration is required (or allowed) in 3283]
MATH 5378 - Differential Geometry
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Basic geometry of curves in plane and in space, including Frenet formula, theory of surfaces, differential forms, Riemannian geometry. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]; [2283 or 3283] recommended]
MATH 5385 - Introduction to Computational Algebraic Geometry
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Geometry of curves/surfaces defined by polynomial equations. Emphasizes concrete computations with polynomials using computer packages, interplay between algebra and geometry. Abstract algebra presented as needed. prereq: [2263 or 2374 or 2573], [2243 or 2373 or 2574]
MATH 5445 - Mathematical Analysis of Biological Networks
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations
MATH 5447 - Theoretical Neuroscience
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Nonlinear dynamical system models of neurons and neuronal networks. Computation by excitatory/inhibitory networks. Neural oscillations, adaptation, bursting, synchrony. Memory systems. prereq: 2243 or 2373 or 2574
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended
MATH 5485 - Introduction to Numerical Methods I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Solution of nonlinear equations in one variable. Interpolation, polynomial approximation. Methods for solving linear systems, eigenvalue problems, systems of nonlinear equations. prereq: [2243 or 2373 or 2573], familiarity with some programming language
MATH 5486 - Introduction To Numerical Methods II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Numerical integration/differentiation. Numerical solution of initial-value problems, boundary value problems for ordinary differential equations, partial differential equations. prereq: 5485
MATH 5525 - Introduction to Ordinary Differential Equations
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Ordinary differential equations, solution of linear systems, qualitative/numerical methods for nonlinear systems. Linear algebra background, fundamental matrix solutions, variation of parameters, existence/uniqueness theorems, phase space. Rest points, their stability. Periodic orbits, Poincare-Bendixson theory, strange attractors. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283]
MATH 5535 - Dynamical Systems and Chaos
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Dynamical systems theory. Emphasizes iteration of one-dimensional mappings. Fixed points, periodic points, stability, bifurcations, symbolic dynamics, chaos, fractals, Julia/Mandelbrot sets. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]
MATH 5583 - Complex Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 3574/5583
Typically offered: Every Fall, Spring & Summer
Algebra, geometry of complex numbers. Linear fractional transformations. Conformal mappings. Holomorphic functions. Theorems of Abel/Cauchy, power series. Schwarz' lemma. Complex exponential, trig functions. Entire functions, theorems of Liouville/Morera. Reflection principle. Singularities, Laurent series. Residues. prereq: 2 sems soph math [including [2263 or 2374 or 2573], [2283 or 3283]] recommended
MATH 5587 - Elementary Partial Differential Equations I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Emphasizes partial differential equations w/physical applications, including heat, wave, Laplace's equations. Interpretations of boundary conditions. Characteristics, Fourier series, transforms, Green's functions, images, computational methods. Applications include wave propagation, diffusions, electrostatics, shocks. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]
MATH 5588 - Elementary Partial Differential Equations II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Heat, wave, Laplace's equations in higher dimensions. Green's functions, Fourier series, transforms. Asymptotic methods, boundary layer theory, bifurcation theory for linear/nonlinear PDEs. Variational methods. Free boundary problems. Additional topics as time permits. prereq: [[2243 or 2373 or 2573], [2263 or 2374 or 2574], 5587] or instr consent
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
MATH 5652 - Introduction to Stochastic Processes
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101
MATH 5654 - Prediction and Filtering
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Markov chains, Wiener process, stationary sequences, Ornstein-Uhlenbeck process. Partially observable Markov processes (hidden Markov models), stationary processes. Equations for general filters, Kalman filter. Prediction of future values of partially observable processes. prereq: 5651 or Stat 5101
MATH 5705 - Enumerative Combinatorics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Basic enumeration, bijections, inclusion-exclusion, recurrence relations, ordinary/exponential generating functions, partitions, Polya theory. Optional topics include trees, asymptotics, listing algorithms, rook theory, involutions, tableaux, permutation statistics. prereq: [2243 or 2373 or 2573], [2263 or 2283 or 2374 or 2574 or 3283]
MATH 5707 - Graph Theory and Non-enumerative Combinatorics
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Basic topics in graph theory: connectedness, Eulerian/Hamiltonian properties, trees, colorings, planar graphs, matchings, flows in networks. Optional topics include graph algorithms, Latin squares, block designs, Ramsey theory. prereq: [2243 or 2373 or 2573], [2263 or 2374 or 2574]; [2283 or 3283 or experience in writing proofs] highly recommended; Credit will not be granted if credit has been received for: 4707
MATH 5711 - Linear Programming and Combinatorial Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Simplex method, connections to geometry, duality theory, sensitivity analysis. Applications to cutting stock, allocation of resources, scheduling problems. Flows, matching/transportation problems, spanning trees, distance in graphs, integer programs, branch/bound, cutting planes, heuristics. Applications to traveling salesman, knapsack problems. prereq: 2 sems soph math [including 2243 or 2373 or 2573]
PUBH 3415 - Introduction to Clinical Trials - Online
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall & Summer
Phases of trials, hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis strategies. Protocol development/implementation, interactive discussion boards. prereq: PUBH 3415 enrollees must have one semester of undergraduate level introductory biostatistics or statistics (STAT 3011, EPSY 3264, SOC 3811, BIOL 3272, or instr consent) AND junior or senior standing or instr consent.
PUBH 6420 - Introduction to SAS Programming
Credits: 1.0 [max 1.0]
Typically offered: Periodic Fall & Summer
Use of SAS for analysis of biomedical data. Data manipulation/description. Basic statistical analyses (t-tests, chi-square, simple regression).
PUBH 6431 - Topics in Hierarchical Bayesian Analysis
Credits: 1.0 [max 1.0]
Grading Basis: OPT No Aud
Typically offered: Every Summer
Hierarchical Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing.
PUBH 6432 - Biostatistical Methods in Translational and Clinical Research
Credits: 1.0 [max 1.0]
Grading Basis: OPT No Aud
Typically offered: Periodic Summer
This short course on translational and clinical research will focus on the topics of diagnostic medicine and designing clinical research methods, application of regression models and early phase clinical trials. prereq: Students will benefit from having taken one or two semester courses in biostatistics or applied statistics covering up to and including multiple regression and introductory logistic regression.
PUBH 6470 - SAS Procedures and Data Anaysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
SAS procedures, how they are used in various health-related datasets to answer specific problems regarding estimation, testing, or prediction. prereq: [6450, 6451] or [7405, 7406] or [Stat 5101, Stat 5102]
PUBH 7415 - Introduction to Clinical Trials
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Summer
Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory biostatistics or statistics or instr consent
WRIT 3562W - Technical and Professional Writing (WI)
Credits: 4.0 [max 4.0]
Course Equivalencies: 01235 - Writ 3562V/Writ 3562W
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Written/oral communication in professional settings, gathering research, analyzing audience, assessing/practicing multiple genres. Draft, test, revise present findings in oral presentation. prereq: [Jr or sr or instr consent], [1301 or 1401 or equiv]
CSCI 4203 - Computer Architecture
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 4203/EE 4363
Typically offered: Every Fall & Spring
Introduction to computer architecture. Aspects of computer systems, such as pipelining, memory hierarchy, and input/output systems. Performance metrics. Examins each component of a complicated computer system. prereq: 2021 or instr consent
EE 4363 - Computer Architecture and Machine Organization
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 4203/EE 4363
Typically offered: Every Fall & Spring
Introduction to computer architecture. Aspects of computer systems, such as pipelining, memory hierarchy, and input/output systems. Performance metrics. Examines each component of a complicated computer system. prereq: 2361
CSCI 4921 - History of Computing (TS, HIS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 00497 - CSci 4921/HSci 4321
Typically offered: Fall Even Year
Developments in last 150 years; evolution of hardware and software; growth of computer and semiconductor industries and their relation to other businesses; changing relationships resulting from new data-gathering and analysis techniques; automation; social and ethical issues.
HSCI 4321 - History of Computing (TS, HIS)
Credits: 3.0 [max 3.0]
Course Equivalencies: 00497 - CSci 4921/HSci 4321
Typically offered: Fall Even, Spring Odd Year
Developments in the last 150 years; evolution of hardware and software; growth of computer and semiconductor industries and their relation to other business areas; changing relationships resulting from new data-gathering and analysis techniques; automation; social and ethical issues.
CSCI 5204 - Advanced Computer Architecture
Credits: 3.0 [max 3.0]
Course Equivalencies: 00579
Typically offered: Every Fall
Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design. prereq: 4203 or EE 4363
EE 5364 - Advanced Computer Architecture
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 5204/EE 5364
Typically offered: Every Fall
Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design. prereq: [[4363 or CSci 4203], CSE grad student] or dept consent
STAT 4893W - Consultation and Communication for Statisticians (WI)
Credits: 3.0 [max 3.0]
Prerequisites: Senior Statistics Major
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This course focuses on how to interact and collaborate as a statistician on a multidisciplinary team. Students will learn about all aspects of statistical consulting by performing an actual consultation. This includes: understanding the needs of the researcher, designing a study to investigate the client's needs, and communicating study results through graphs, writing, and oral presentations in a manner that a non-statistician can understand. Students will also discuss how to design research ethically (respecting the rights of the subjects in the research), how to analyze data without manipulating results, and how to properly cite and credit other people's work. Students will also be exposed to professional statisticians as a means of better understanding careers in statistics. prereq: Senior Statistics Major
STAT 4893W - Consultation and Communication for Statisticians (WI)
Credits: 3.0 [max 3.0]
Prerequisites: Senior Statistics Major
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This course focuses on how to interact and collaborate as a statistician on a multidisciplinary team. Students will learn about all aspects of statistical consulting by performing an actual consultation. This includes: understanding the needs of the researcher, designing a study to investigate the client's needs, and communicating study results through graphs, writing, and oral presentations in a manner that a non-statistician can understand. Students will also discuss how to design research ethically (respecting the rights of the subjects in the research), how to analyze data without manipulating results, and how to properly cite and credit other people's work. Students will also be exposed to professional statisticians as a means of better understanding careers in statistics. prereq: Senior Statistics Major