Instructor(s):  Staff

Required text(s):  Angel, Allen and Dennis Runde. Intermediate Algebra for College Students (packaged with MyMathLab). 9th ed. ISBN-10: 0321927370. ISBN-13: 9780321927378. Upper Saddle River, NJ: Pearson-Prentice Hall, 2010. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  None

Course description:  Fundamentals of algebra. Graphs of linear equations, polynomials and factoring, first and second-degree equations and inequalities, radicals and exponents, and systems of equations. Word problems emphasized throughout the course.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Consortium for Mathematics and Its Applications (COMAP), S. Garfunkel, ed. For All Practical Purposes: Mathematical Literacy in Today's World. 9th ed. ISBN-13: 978-1429243162. New York: W. H. Freeman, 2011. Print.

Prerequisites:  None

Course description:  An introduction to mathematical modeling. Topics chosen from linear programming, probability theory, Markov chains, scheduling problems, coding theory, social choice, voting theory, geometric concepts, game theory, graph theory, combinatorics, networks. Emphasis placed upon demonstrating the usefulness of mathematical models in other disciplines, especially the social science and business.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Axler, Sheldon. Precalculus: A Prelude to Calculus. Binder Ready Version w/ Wileyplus ISBN: 9781118562390 or Paperback w/ Wileyplus ISBN: 978-1-118-55625-2. 2nd ed. Hoboken, NJ: Wiley, 2012. Print.

Textbook notes:  Students are required to have access to WileyPLUS for this course. Students buying used textbooks should arrange to purchase WileyPLUS separately.

Prerequisites:  MATH 100 or Math Diagnostic Test

Course description:  Inverse functions, quadratic functions, complex numbers. Detailed study of polynomial functions including zeros, factor theorem, and graphs. Rational functions, exponential and logarithmic functions and their applications. Systems of equations, inequalities, partial fractions, linear programming, sequences and series. Word problems are emphasized throughout the course.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Axler, Sheldon. Precalculus: A Prelude to Calculus, 2nd Edition Paperback w/ Wileyplus ISBN: 978-1-118-55625-2. Hoboken, NJ: Wiley, 2012. Print.

Textbook notes:  Students who were enrolled in MATH 117 in Spring 2014 will not need to purchase this new edition of the textbook. (See course instructor for more details.)

Prerequisites:  MATH 117 or Math Diagnostic Test

Course description:  Functions and change with an emphasis on linear, quadratic, exponential, and logarithmic functions and their graphs. Specific geometric topics include concavity and how transformations affect graphs. Topics in trigonometry include radians, sinusoidal functions, identities, sum/difference formulas, double/half angle formulas, and trigonometric equations. Other topics include polar coordinates.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Hughes-Hallett, Deborah, et al. Applied & Single Variable Calculus for Loyola University Chicago with WebAssign Custom (packaged with WebAssign). 4th ed. ISBN-13: 9781118747476. Hoboken, NJ: Wiley, 2013. Print.

Textbook notes:  Students are required to have access to WebAssign for this course. Students buying used textbooks should arrange to purchase WebAssign separately.

Prerequisites:  MATH 118 or Math Diagnostic Test

Course description:  An introduction to differential and integral calculus, with an emphasis on applications. This course is intended for students in the life and social sciences, computer science, and business. Topics include: modeling change using functions including exponential and trigonometric functions, the concept of the derivative, computing the derivative, applications of the derivative to business and life, social, and computer sciences, and an introduction to integration. This course is not a substitute for MATH 161.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Hughes-Hallett, Deborah, et al. Applied & Single Variable Calculus for Loyola University Chicago with WebAssign Custom (packaged with WebAssign). 4th ed. ISBN-13: 9781118747476. Hoboken, NJ: Wiley, 2013. Print.

Textbook notes:  Students are required to have access to WebAssign for this course. Students buying used textbooks should arrange to purchase WebAssign separately. SPECIAL NOTE: this is a change from previous semesters, which required WileyPLUS.

Prerequisites:  MATH 131 or MATH 161

Course description:  A continuation of MATH 131. Topics include: properties of the integral, techniques of integration, numerical methods, improper integrals, applications to geometry, physics, economics, and probability theory, introduction to differential equations and mathematical modeling, systems of differential equations, power series. This course is not a substitute for MATH 162.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Thomas, George B., Maurice D. Weir, and Joel R. Hass. Thomas' Calculus: Early Transcendentals (Single Variable) (packaged with MyMathLab). 13th edition, ISBN-10: 0321952871. ISBN-13: 9780321952875

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  MATH 118 or Math Diagnostic Test

Course description:  A traditional introduction to differential and integral calculus. Functions, limits, continuity, differentiation, intermediate and mean-value theorems, curve sketching, optimization problems, related rates, definite and indefinite integrals, fundamental theorem of calculus, logarithmic and exponential functions. Applications to physics and other disciplines.

Syllabus:  Common

Instructor(s):  Staff

Required text(s):  Thomas, George B., Maurice D. Weir, and Joel R. Hass. Thomas' Calculus: Early Transcendentals (Single Variable) (packaged with MyMathLab). 13th ed. ISBN-10: 0321952871. ISBN-13: 9780321952875. Boston: Addison-Wesley, 2009. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  MATH 161

Course description:  A continuation of MATH 161. Calculus of logarithmic, exponential, inverse trigonometric, and hyperbolic functions. Techniques of integration. Applications of integration to volume, surface area, arc length, center of mass, and work. Numerical sequences and series. Study of power series and the theory of convergence. Taylor's theorem with remainder

Syllabus:  Common

Required text(s):  A Concise Introduction to Pure Mathematics, Third Edition (Chapman Hall/CRC Mathematics Series) 2010 by Martin Liebeck (Author) ISBN-13: 978-1439835982 ISBN-10: 1439835985 Edition: 3rd

Prerequisites:  Math 161

Course description:  This course uses topics from elementary number theory, ranging from induction to congruencies to prime numbers and cardinality, to provide students planning to take advanced 300 level courses in mathematics with an introduction to understanding and constructing proofs.

Instructor(s):  Alan Saleski

Required text(s):  Sarah-Marie Belcastro, Discrete Mathematics with Ducks Publisher: CRC Press; 1 edition (June 21, 2012) ISBN-10: 1466504994 ISBN-13: 978-1466504998

Prerequisites:  Math 161

Course description:  MATH 201 is an opportunity for students to learn how to read and write proofs. It forms the bridge between primarily non-theoretical courses (calculus, linear algebra) and 300-level math courses, particularly Math 313 (Abstract Algebra I) and Math 351 (Real Analysis I). There will be written homework, several tests and quizzes in addition to a cumulative final exam.

Instructor(s):  Dr. E.N. Barron

Required text(s):  David Lay, Linear Algebra and its Applications, 4th Edition, Pearson (packaged with MyMathLab). ISBN-10: 0321399145; ISBN-13: 9780321399144. Alternatively, a student may purchase MyMathLab access as a separate entity and use the online version of the book. The ISBN for the MML access only is: 032119991X.

Prerequisites:  MATH 162 Calculus II or MATH 132 Applied Calculus II

Course description:  An introduction to linear algebra in abstract vector spaces with particular emphasis on R^n. Topics include: Gaussian elimination, matrix algebra, linear independence, span, basis, linear transformations, determinants, eigenvalues, eigenvectors, and diagonalization. Some of the basic theorems will be proved rigorously; other results will be demonstrated informally. Homework will be assigned regularly in MyMathLab throughout the semester. There will be at least two midterms and a comprehensive final exam.

Instructor(s):  Dr. Joseph Mayne

Required text(s):  Strang, Gilbert. Introduction to Linear Algebra. 4th ed. ISBN-13: 978-0980232714. Wellesley, MA: Wellesley-Cambridge Press, 2009. Print. .

Prerequisites:  MATH 132 or MATH 162

Course description:  Linear algebra is widely used in mathematics, science, engineering, and the social sciences. For example, statisticians and economists often employ linear models when trying to analyze problems with many variables. And linear algebra is an important tool in many areas of mathematics itself. Much of functional analysis is devoted to the study of functions preserving linearity and field theory uses linear algebra in the proofs of many results. The course starts with the problem of solving simultaneous linear equations using the Gaussian elimination algorithm. The solution of this important practical problem motivates the definition of many linear algebra concepts: matrices, vectors and vector spaces, linear independence, dimension, and vector subspaces. The emphasis then shifts to general vector spaces and proofs using an axiom system. Most of the results will be for finite dimensional spaces and we will always attempt to visualize theorems in 2 or 3 dimensional Euclidean space. Topics to be covered include: linear transformations, change of basis, determinants, eigenvalues and eigenvectors, and diagonalization. Students will be encouraged to improve their skills at constructing mathematical proofs.

Instructor(s):  Staff

Required text(s):  Thomas, George B., Maurice D. Weir, and Joel R. Hass. Thomas' Calculus, Multivariable (packaged with MyMathLab), 13th ed. ISBN-13: 9780321953100. New York: Pearson, 2014. Print.

Textbook notes:  Students are required to have access to MyMathLab for this course. Students buying used textbooks should arrange to purchase MyMathLab separately.

Prerequisites:  MATH 162

Course description:  Vectors and vector algebra, curves and surfaces in space, functions of several variables, partial derivatives, the chain rule, the gradient vector, LaGrange multipliers, multiple integrals, volume, surface area, the Change of Variables theorem, line integrals, surface integrals, Green's theorem, the Divergence Theorem, and Stokes' Theorem.

Instructor(s):  Dr. John G. Del Greco

Required text(s):  Nagle, R. Kent, Saff, Edward B., and Edward B. Snider. Fundamentals of Differential Equations. 8th ed. ISBN-13: 978-0321747730. Boston: Addison-Wesley, 2012. Print.

Prerequisites:  MATH 263. Being able to differentiate and integrate functions is an essential skill when studying differential equations. Students will be assumed to be proficient in the techniques of differentiation and integration.

Course description:  This course will be a rigorous treatment of ordinary differential equations. The course will emphasize solutions techniques although some applications will be considered.

Topics will include first- and second-order linear differential equations and methods for their solution: separable of variables and exact equations, integrating factors for linear equations, substitutions and transformations, method of undetermined coefficients, variation of parameters, Laplace transformations, series solutions, systems of differential equations, and phase-plane analysis.

Written homework will be assigned at the end of every class. There should be about 20-25 written homework assignments given during the semester. Each assignment will require at least an hour or two to complete. Homework will count for a significant part of a student's grade.

In addition to written homework, there will be two midterm exams and a final exam. Calculators or any other types of electronic devices will not be permitted on the midterm exams or final exam.

Instructor(s):  Marian Bocea

Required text(s):  Sheldon Ross, A First Course in Probability, 9th edition, Pearson (2012); ISBN-10: 032179477X ISBN-13: 9780321794772

Prerequisites:  MATH 263

Course description:  This course will cover basic probability theory. Topics include combinatorics, random walks, conditional probability and independence, random variables, expectation, moment generating functions, and the central limit theorem. There will be at least two midterms and a comprehensive final exam.

Instructor(s):  Dr. E.N.Barron

Required text(s):  An Introduction to Stochastic Modeling, 4th Edition,Mark Pinsky,Samuel Karlin,ISBN9780123814166

Prerequisites:  Math/Stat 304 or 404

Course description:  This class is an introduction to random processes and modeling arising in finance, biology, physics, and gambling. Beginning with a review of probability we will thoroughly study Markov chains (like random walks) and their applications. Then we study continuous time processes like Poisson processes and Brownian Motion processes and their application to queueing theory and diffusions. The class will have two exams and a final.

Instructor(s):  Dr. Aaron Lauve

Required text(s):  Pinter, Charles C. A Book of Abstract Algebra. Dover ed. ISBN-13: 978-0486474175. New York: McGraw-Hill, 1990. Print.

Prerequisites:  MATH 201 and MATH 212

Course description:  The techniques and results of modern abstract algebra have found purchase in a wide variety of disciplines, ranging from physical chemistry and quantum physics to cryptography, coding theory (allowing Cassini to send you pictures of Saturn's moons) and even voting theory. At the root of it all, not surprisingly, is the centuries old study of roots of algebraic equations—so we are headed back to high school!

In this course, we study groups, the first pillar of abstract algebra, which may be viewed as the search for symmetry in objects. (Continuing the theme started in the preceding paragraph, as examples of such "objects" I might mention here algebraic equations, differential equations, geometric objects, …) Students will be introduced to permutation groups, matrix groups, subgroups, isomorphisms, equivalence relations, factor groups, and more. Over the course of the semester, we will consider some applications of abstract algebra, but the main focus will be on the "pure" study of the algebraic structures themselves. Examples motivating the theory will appear throughout.

Instructor(s):  Dr. W. Cary Huffman

Required text(s):  Friedbreg, Stephen H., Arnold J. Insel, and Lawrence E. Spence. Linear Algebra. 4th ed. ISBN-13: 978-0130084514. Upper Saddle River, NJ: Pearson Education, 2003. Print.

Prerequisites:  MATH 313, or the equivalent, or permission of the instructor.

Course description:  Many problems in applied mathematics, physics, and engineering involve systems of equations that may be very difficult to solve. Sometimes the first approximation to solving such systems is to linearize them and solve these linearized systems using various theoretical and applied techniques. This class will be a second course in linear algebra, where advanced topics will be considered. These topics will be chosen from inner product spaces, Gram-Schmidt orthogonalization, normal and self-adjoint operators, unitary and orthogonal operators, bilinear and quadratic forms, and Jordan and rational canonical forms. There will be applications of these ideas to least squares problems and regression, orthogonal polynomials, exponential functions with matrix exponents, time contraction in Einstein's Theory of relativity, and others. The course will begin with a review of some necessary material from MATH 212 and MATH 313.

This course is a combined undergraduate/graduate course. The requirements of the course for the graduate students will be different from the requirements for the undergraduates.

Instructor(s):  Dr. Stephen Doty

Required text(s):  Hoffstein, Pipher, and Silverman, An Introduction to Mathematical Cryptography, Second edition, Springer 2014. ISBN 978-1-4939-1710-5.

Recommended text(s):  Steven Levy, Crypto : how the code rebels beat the government, saving privacy in the digital age, Penguin 2001. ISBN 978-0140244328.

Prerequisites:  Mathematics (one of COMP 163, MATH 313 or MATH 201) and Programming (COMP 125, COMP 170, MATH 215, or equivalent). Basically, you MUST know how to program in some language, and you MUST have prior exposure to abstract mathematics beyond calculus.

Course description:  In 1976 Diffie and Hellman revolutionized cryptography by introducing public-key (also called asymmetric) cryptosystems, in order to adapt to the security demands of modern networks. This course studies some of the underlying mathematics of cryptography and the various algorithms used, focusing on the basics. Much of the mathematics is relatively simple, yet the applications are novel. We will briefly study some examples of classical private-key (symmetric) systems before moving on to public-key systems. The course will be a mixture of theory and practice, involving mathematical proofs as well as coding of algorithms based on the mathematics. All modern browsers use public-key cryptography. Every time you login to a secure website, make a secure credit card transaction, or ssh into a server, you are using public-key cryptography. Hashing is based on cryptography, as is Bitcoin and BitTorrent. Governments debate its proper use and regulation and journalists and others use it to protect their privacy. The course will involve graded projects, assigned homework, and at least one midterm exam as well as a final exam.

Instructor(s):  Dr. Brian Seguin

Required text(s):  TBA

Prerequisites:  MATH 201 and MATH 212

Course description:  This course provides a rigorous development of the differential calculus. Students are expected to have had experience understanding and writing proofs.

Topics covered include: numerical sequences, limit theorems for sequences, completeness property, nested intervals theorem, Bolzano-Weierstrass theorem, Cauchy sequences, infinite series, convergence tests, rearrangements, power series, functions, continuity, intermediate value theorem, compactness, uniform continuity, the derivative, mean value theorem, l'Hopital's rule, convexity, Taylor's theorem with Lagrange remainder.

Instructor(s):  Robert Jensen

Required text(s):  Complex Analysis (Undergraduate Texts in Mathematics)Aug 6, 2010 by Joseph Bak and Donald J. Newman ISBN-13: 978-1441972873 ISBN-10: 1441972870 Edition: 3rd ed. 2010

Prerequisites:  Math 351, Introduction to Real Analysis

Course description:  This is a traditional course on complex analysis. After a brief review of the complex numbers we will study complex valued functions of a complex variable. This includes differentiation and the connection between differentiable functions and analytic functions; and integration, where the line integral from multivariable calculus provides the connection between integration and differentiation. We will prove the Cauchy representation theorem and the residue theorem and examine their consequences and applications.

Instructor(s):  Dr. Peter Tingley

Required text(s):  TBA

Prerequisites:  Math314 or similar

Course description:  We will study the structure theory of classical Lie groups and Lie algebras, and consider some applications.

Instructor(s):  Dr. AdrianoZambom

Required text(s):  Mathematical Statistics with Applications by Wackerly, Mendenhall, and Scheaffer, 7th edition (2007), Duxbury/Brooks/Cole/Thomson. ISBN-10: 0-495-11081-7.

Textbook notes:  (This textbook will be used in both STAT-404 and STAT-405.)

Prerequisites:  Graduate student status or permission of the instructor.

Course description:  This is the first semester of a two-semester sequence. The first semester is essentially an exploration of probability as a mathematical model of chance phenomena; the second semester explores the statistical analyses based on these models. In the first semester class, topics to be covered include discrete and continuous random variables, transformations, multivariate distributions, correlation, independence, variance-covariance, special distributions (binomial, Poisson, gamma, chi-square, beta, normal, multivariable normal, t and F), expectations of functions, convergence in probability, convergence in distribution, moment generating functions, and the Central Limit Theorem. Grading will be based on quizzes, exams and homework assignments; homework will be assigned on a regular basis, and collected and graded in a timely manner to provide needed feedback.

Instructor(s):  Dr. W. Cary Huffman

Required text(s):  Friedbreg, Stephen H., Arnold J. Insel, and Lawrence E. Spence. Linear Algebra. 4th ed. ISBN-13: 978-0130084514. Upper Saddle River, NJ: Pearson Education, 2003. Print.

Prerequisites:  MATH 313, or the equivalent, or permission of the instructor.

Course description:  Many problems in applied mathematics, physics, and engineering involve systems of equations that may be very difficult to solve. Sometimes the first approximation to solving such systems is to linearize them and solve these linearized systems using various theoretical and applied techniques. This class will be a second course in linear algebra, where advanced topics will be considered. These topics will be chosen from inner product spaces, Gram-Schmidt orthogonalization, normal and self-adjoint operators, unitary and orthogonal operators, bilinear and quadratic forms, and Jordan and rational canonical forms. There will be applications of these ideas to least squares problems and regression, orthogonal polynomials, exponential functions with matrix exponents, time contraction in Einstein's Theory of relativity, and others. The course will begin with a review of some necessary material from MATH 212 and MATH 313.

This course is a combined undergraduate/graduate course. The requirements of the course for the graduate students will be different from the requirements for the undergraduates.

Instructor(s):  Dr. Stephen Doty

Required text(s):  Hoffstein, Pipher, and Silverman, An Introduction to Mathematical Cryptography, Second edition, Springer 2014. ISBN 978-1-4939-1710-5.

Recommended text(s):  Steven Levy, Crypto : how the code rebels beat the government, saving privacy in the digital age, Penguin 2001. ISBN 978-0140244328.

Prerequisites:  Mathematics (one of COMP 163, MATH 313 or MATH 201) and Programming (COMP 125, COMP 170, MATH 215, or equivalent). Basically, you MUST know how to program in some language, and you MUST have prior exposure to abstract mathematics beyond calculus.

Course description:  In 1976 Diffie and Hellman revolutionized cryptography by introducing public-key (also called asymmetric) cryptosystems, in order to adapt to the security demands of modern networks. This course studies some of the underlying mathematics of cryptography and the various algorithms used, focusing on the basics. Much of the mathematics is relatively simple, yet the applications are novel. We will briefly study some examples of classical private-key (symmetric) systems before moving on to public-key systems. The course will be a mixture of theory and practice, involving mathematical proofs as well as coding of algorithms based on the mathematics. All modern browsers use public-key cryptography. Every time you login to a secure website, make a secure credit card transaction, or ssh into a server, you are using public-key cryptography. Hashing is based on cryptography, as is Bitcoin and BitTorrent. Governments debate its proper use and regulation and journalists and others use it to protect their privacy. The course will involve graded projects, assigned homework, and at least one midterm exam as well as a final exam.

Instructor(s):  Dr. Peter Tingley

Required text(s):  TBA

Prerequisites:  Math314 or similar

Course description:  We will study the structure theory of classical Lie groups and Lie algebras, and consider some applications.

Instructor(s):  Staff

Required text(s):  Gould, Robert N. and Coleen N. Ryan. Introductory Statistics Plus NEW MyStatLab with Pearson eText -- Access Card Package, 2nd ed. ISBN-13: 978-0133956504. New York: Pearson, 2015. Print.

Prerequisites:  None

Course description:  An introduction to statistical reasoning. Students learn how statistics has helped to solve major problems in economics, education, genetics, medicine, physics, political science, and psychology. Topics include: design of experiments, descriptive statistics, mean and standard deviation, the normal distribution, the binomial distribution, correlation and regression, sampling, estimation, and testing of hypothesis. A TI-83 (or equivalent) calculator is required for this course.

Syllabus:  Common

Instructor(s):  Dr. Adriano Zambom

Required text(s):  Walpole, Robert E., Myers, R., Myers, S., and Ye, K. Essentials of Probability and Statistics for Engineers and Scientists. 1st ed. ISBN-13: 978-0321783738. New York: Pearson, 2015. Print.

Textbook notes:  Alternative text: Devore, Jay. Probability and Statistics for Engineering and the Sciences. 8th ed. ISBN 13-978-1-305-25190-9. Cengage: Boston, 2012. Print.

Prerequisites:  Math 132 or Math 162

Course description:  This is a one semester foundation class in probability and statistics. The class is meant to introduce the student to the basics of probability and statistics for science and engineering majors. It introduces the probability needed to form a foundation for the statistics methods which are the focus of the course. Statistics is the scientific method used in order to be able to reach a decision about the result of an experiment which has random outcomes. Such experiments arise in all areas of science and medicine. The scientific method involves controlled randomized experiments so that the method of comparison can be used to reach a conclusion about the compared groups. Statistics and probability is the mathematical basis for the method of comparison.

Instructor(s):  TBA

Required text(s):  TBA

Prerequisites:  STAT 103 or 203 or 335.

Course description:  This course is an introduction to writing and executing SAS programs under the Windows environment in the context of applied statistics problems. SAS procedures are used to read and analyze various types of data sets as they apply to t-tests, simple and multiple regressions, ANOVA, categorical analysis, and repeated measures.

Instructor(s):  Dr. Molly K. Walsh

Required text(s):  Mendenhall, William and Sincich, Terry. A Second Course in Statistics: Regression Analysis. 7th ed. ISBN-13: 978-0321691699. Boston: Prentice Hall, 2012. Print.

Prerequisites:  STAT 203 or STAT 335 (or permission of instructor)

Course description:  This course provides students with a thorough introduction to applied regression methodology. The concept of simple linear regression will be reviewed, and multiple linear regression, transformations, indicator variables, multicollinearity, diagnostics, model building, polynomial regression, logistic regression and nonparametric regression will be discussed. The course will focus on applications such as those from biometry and biostatistics (clinical trials, HIV studies, etc.), sports, engineering, agriculture and environmental science. Students are required to analyze real-life datasets using output from statistical packages such as Minitab and SAS, although no previous programming experience is assumed. Quizzes, exams, and take-home assignments will be used to determine the final grade in the course.

Required text(s):  TBA

Additional notes:  Students may not receive credit for both STAT 203 & 335.

Prerequisites:  MATH 132 or 162 and BIOL 102

Course description:  This course provides an introduction to statistical methods used in designing biological experiments and in data analysis. Topics include descriptive statistics, probability, discrete probability distributions, the normal distribution, sampling distributions, confidence intervals, hypothesis testing for one and two samples involving means and proportions, chi-square tests, one way ANOVA, and simple linear regression. The emphasis is on applications instead of statistical theory, and students are required to analyze real-life datasets using output from statistical packages such as Minitab and SAS, although no previous programming experience is assumed. Quizzes, exams, and take-home assignments will be used to determine the final grade in the course.

Required text(s):  Dr. Molly K. Walsh

Prerequisites:  STAT 335 or permission of instructor

Course description:  This course covers the basics of hypothesis testing, sample size and power calculations, categorical data techniques, experimental design and ANOVA, repeated measures ANOVA, randomized complete block design, two way ANOVA, multiple linear regression, analysis of covariance, logistic regression, and survival analysis. The emphasis is on applications instead of statistical theory, and students are required to analyze real-life datasets using output from statistical packages such as Minitab and SAS, although no previous programming experience is assumed. Quizzes, exams, a group project and take-home assignments will be used to determine the final grade in the course. Students (in small groups) will complete an original data analysis project using at least one of the methods covered during the course. Use of computer software such as Minitab and/or SAS is highly recommended for computations.

Instructor(s):  Dr. Molly K. Walsh

Required text(s):  TBA

Prerequisites:  STAT 203 or STAT 335 (or permission of instructor)

Course description:  This course focuses on the statistical aspects of collecting and analyzing data from a sample. After a review of probability concepts used in sampling, we will cover the fundamental concepts of simple random, systematic, stratified and cluster sampling, as well as ratio and regression estimation. More complex sampling methods will also be discussed, along with methods of dealing with nonresponse and measurement error. A familiarity with basic ideas of expectation, sampling distributions, confidence intervals and linear regression is assumed. The focus throughout this course will be on applications and real-life datasets; as such, theorems and proofs will not be emphasized. Experience with SAS or R is assumed. Quizzes, exams, a group project and take-home assignments will be used to determine the final grade in the course.

Instructor(s):  Dr. Tim O'Brien

Required text(s):  An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie & Rob Tibshirani, 2013, Springer. ISBN-978-1-4614-7137-0

Recommended text(s):  Supplementary: The Elements of Statistical Learning: Data Mining, Inference and Prediction by Trevor Hastie, Rob Tibshirani & Jerome Friedman, 2nd Edition (2009), ISBN-978-0-387-84857-0

Textbook notes:  Important Notes: Both of these books can be freely (and legally) downloaded in PDF (format); the first book from http://www-bcf.usc.edu/~gareth/ISL/, and the second book from http://statweb.stanford.edu/~tibs/ElemStatLearn/. Also, solutions to both texts’ exercises are available on the Web.

Prerequisites:  STAT-203 or STAT-335. Students should review the basics of simple linear regression.

Course description:  This course will cover the basics of statistical learning –modelling and regression techniques designed to analyze complex datasets. In the world of “big data,” often no theoretical construct exists to model, estimate, test and/or predict, and analysis relies on data analytics methods such as regression and classification, resampling methods, tree-based methods, support vector machines, and unsupervised learning – methods covered in this course. The emphasis of the course will be on applications and projects, and MS students will be required to delve into some methods. Students are required to analyze real-life datasets using the R (freeware) statistical package (although no previous programming experience is assumed). Grading will be based on quizzes, student participation, extensive course projects analyzing real-world data, and a final exam or project.

Instructor(s):  Dr. Earvin Balderama

Required text(s):  None. See below.

Recommended text(s):  Bivand, R. S., Pebesma, E., Gomez-Rubio, V., (2013). Applied Spatial Data Analysis with R, Second Edition. Springer. ISBN-10: 1461476178.

Prerequisites:  STAT 203 or STAT 335 (or permission of instructor).

Course description: 

The idea that observations physically close together may be more similar while observations far apart may be more different is the idea behind Spatial Statistics, a branch of Statistics that deals with spatial data (those data with observed location coordinates such as longitude/latitude). The analysis of spatial data has become increasingly important in many fields, such as the environmental, ecological, and biological sciences, public health and medicine, social sciences, business, and sports.

This course will cover fundamental concepts and techniques for analyzing the three main flavors of spatial data: geostatistical, areal, and point-pattern data. Topics include spatial data visualization and description, kriging, variograms, spatial autocorrelation, spatial regression, space-time models, estimation, predictive modeling, and disease mapping. The emphasis is on the practical application of methods to spatial data and interpretation of results, using existing software (primarily R) for analyses.

This is a project-based course; There will be biweekly assignments, a take-home midterm, and a final data-analysis project to be showcased during a public poster presentation symposium at the end of the semester. Project topic can be from any field the student chooses. Previous exposure to computing (e.g., R) is helpful, but not required.

Instructor(s):  Dr. Gregory J. Matthews

Required text(s):  The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century, by David Salsburg, 2001: Holt Paperbacks, ISBN-10: 0805071342, ISBN-13: 978-0-8050-7134-4

Prerequisites:  Senior Standing, including completion of STAT-304

Course description:  This seminar will help students to develop skills in communicating statistical topics of interest in the larger context and to an audience outside of academe and math/statistics. As such, students will be required to actively participate in discussions and blog-posts, and give at least one presentation to their peers on material beyond the standard curriculum. As opportunities arise, students will also attend a number of lectures on a range of topics, some given by their peers, some by Loyola faculty, and some by outside faculty (as part of the undergraduate colloquium series). Students will be expected to interact with speakers and ask questions as appropriate, and will write short expositions recapping some of the talks. Grading will be based on the quality of presentations, writing and oral communication of statistical concepts.

Instructor(s):  Dr. AdrianoZambom

Required text(s):  Mathematical Statistics with Applications by Wackerly, Mendenhall, and Scheaffer, 7th edition (2007), Duxbury/Brooks/Cole/Thomson. ISBN-10: 0-495-11081-7.

Textbook notes:  (This textbook will be used in both STAT-404 and STAT-405.)

Prerequisites:  Graduate student status or permission of the instructor.

Course description:  This is the first semester of a two-semester sequence. The first semester is essentially an exploration of probability as a mathematical model of chance phenomena; the second semester explores the statistical analyses based on these models. In the first semester class, topics to be covered include discrete and continuous random variables, transformations, multivariate distributions, correlation, independence, variance-covariance, special distributions (binomial, Poisson, gamma, chi-square, beta, normal, multivariable normal, t and F), expectations of functions, convergence in probability, convergence in distribution, moment generating functions, and the Central Limit Theorem. Grading will be based on quizzes, exams and homework assignments; homework will be assigned on a regular basis, and collected and graded in a timely manner to provide needed feedback.

Instructor(s):  Dr. Gregory J. Matthews

Required text(s):  Faraway, Julian J. Linear Models with R. ISBN-13: 978-1584884255. Chapman & Hall/CRC Texts in Statistical Science. 2014. Print.

Prerequisites:  Some background in basic statistical methods or biostatistics, or permission of instructor.

Course description:  This course provides students with a thorough introduction to applied regression methodology. The concept of simple linear regression will be reviewed and discussed using matrices, and multiple linear regression, transformations, diagnostics, polynomial regression, indicator variables, model building and multicollinearity will be discussed, as will be nonlinear and generalized linear regression. The course will focus on applications such as those from biometry and biostatistics (clinical trials, HIV studies, etc.), sports, engineering, agriculture and environmental science.

Instructor(s):  Dr. Molly K. Walsh

Required text(s):  TBA

Prerequisites:  STAT 203 or STAT 335 (or permission of instructor)

Course description:  This course focuses on the statistical aspects of collecting and analyzing data from a sample. After a review of probability concepts used in sampling, we will cover the fundamental concepts of simple random, systematic, stratified and cluster sampling, as well as ratio and regression estimation. More complex sampling methods will also be discussed, along with methods of dealing with nonresponse and measurement error. A familiarity with basic ideas of expectation, sampling distributions, confidence intervals and linear regression is assumed. The focus throughout this course will be on applications and real-life datasets; as such, theorems and proofs will not be emphasized. Experience with SAS or R is assumed. Quizzes, exams, a group project and take-home assignments will be used to determine the final grade in the course.

Instructor(s):  Dr. Gregory J. Matthews

Required text(s):  Statistical Consulting by Javier Cabrera and Andrew McDougall, Springer-Verlag, 2002; ISBN: 0-387-98863-7. One year of MS full-time study in the Applied Statistics program (previous coursework in regression, SAS/programming and experimental design; categorical data analysis and survival analysis also recommended), or obtain the permission of the instructor.

Recommended text(s):  This course serves as a program capstone course for the MS program in Applied Statistics; as such it synthesizes the course material in the context of actual statistical consulting sessions. Students are required to assist in analyzing real-life data sets using SAS and R statistical packages. Students also learn to sharpen their verbal, written and non-verbal communication skills. Grading is based on in-class presentations and consulting sessions and practicum, quizzes and a course project.

Prerequisites:  One year of MS full-time study in the Applied Statistics program (previous coursework in regression, SAS/programming and experimental design; categorical data analysis and survival analysis also recommended), or obtain the permission of the instructor.

Course description:  This course serves as a program capstone course for the MS program in Applied Statistics; as such it synthesizes the course material in the context of actual statistical consulting sessions. Students are required to assist in analyzing real-life data sets using SAS and R statistical packages. Students also learn to sharpen their verbal, written and non-verbal communication skills. Grading is based on in-class presentations and consulting sessions and practicum, quizzes and a course project.

Instructor(s):  Dr. Tim O'Brien

Required text(s):  An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie & Rob Tibshirani, 2013, Springer. ISBN-978-1-4614-7137-0

Recommended text(s):  Supplementary: The Elements of Statistical Learning: Data Mining, Inference and Prediction by Trevor Hastie, Rob Tibshirani & Jerome Friedman, 2nd Edition (2009), ISBN-978-0-387-84857-0

Textbook notes:  Important Notes: Both of these books can be freely (and legally) downloaded in PDF (format); the first book from http://www-bcf.usc.edu/~gareth/ISL/, and the second book from http://statweb.stanford.edu/~tibs/ElemStatLearn/. Also, solutions to both texts’ exercises are available on the Web.

Prerequisites:  STAT-203 or STAT-335. Students should review the basics of simple linear regression.

Course description:  This course will cover the basics of statistical learning –modelling and regression techniques designed to analyze complex datasets. In the world of “big data,” often no theoretical construct exists to model, estimate, test and/or predict, and analysis relies on data analytics methods such as regression and classification, resampling methods, tree-based methods, support vector machines, and unsupervised learning – methods covered in this course. The emphasis of the course will be on applications and projects, and MS students will be required to delve into some methods. Students are required to analyze real-life datasets using the R (freeware) statistical package (although no previous programming experience is assumed). Grading will be based on quizzes, student participation, extensive course projects analyzing real-world data, and a final exam or project.

Instructor(s):  Dr. Earvin Balderama

Required text(s):  None. See below.

Recommended text(s):  Bivand, R. S., Pebesma, E., Gomez-Rubio, V., (2013). Applied Spatial Data Analysis with R, Second Edition. Springer. ISBN-10: 1461476178.

Prerequisites:  STAT 203 or STAT 335 (or permission of instructor).

Course description: 

The idea that observations physically close together may be more similar while observations far apart may be more different is the idea behind Spatial Statistics, a branch of Statistics that deals with spatial data (those data with observed location coordinates such as longitude/latitude). The analysis of spatial data has become increasingly important in many fields, such as the environmental, ecological, and biological sciences, public health and medicine, social sciences, business, and sports.

This course will cover fundamental concepts and techniques for analyzing the three main flavors of spatial data: geostatistical, areal, and point-pattern data. Topics include spatial data visualization and description, kriging, variograms, spatial autocorrelation, spatial regression, space-time models, estimation, predictive modeling, and disease mapping. The emphasis is on the practical application of methods to spatial data and interpretation of results, using existing software (primarily R) for analyses.

This is a project-based course; There will be biweekly assignments, a take-home midterm, and a final data-analysis project to be showcased during a public poster presentation symposium at the end of the semester. Project topic can be from any field the student chooses. Previous exposure to computing (e.g., R) is helpful, but not required.