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MATH 105 and 106 are designed for students whose background and algebraic skills are inadequate for the fast pace of MATH 111. In addition to covering the usual material of beginning calculus, these courses will have an extensive review of algebra and trigonometry. There will be a special emphasis on solving word problems.

MATH 105 starts with a quick review of algebraic manipulations, inequalities, absolute values and straight lines. Then the basic ideas of calculus—limits, derivatives, and integrals—are introduced, but only in the context of polynomial and rational functions. As various applications are studied, the algebraic techniques involved will be reviewed in more detail. When covering related rates and maximum-minimum problems, time will be spent learning how to approach, analyze and solve word problems. Four class meetings per week, one of which is an 80-minute group-work day.

Note: While MATH 105 and 106 are sufficient for any course with a MATH 111 requisite, MATH 105 alone is not. However, students who plan to take MATH 121 should consider taking MATH 105 and then MATH 111, rather than MATH 106. Students cannot register for both MATH 105 and CHEM 151 in the same semester.

Fall semester. The Department.

MATH 106 is a continuation of MATH 105. Trigonometric, logarithmic and exponential functions will be studied from the point of view of both algebra and calculus. The applications encountered in MATH 105 will reappear in problems involving these new functions. The basic ideas and theorems of calculus will be reviewed in detail, with more attention being paid to rigor.

Requisite: MATH 105. Spring semester. Professor D. Benedetto.

Basic concepts of limits, derivatives, anti-derivatives; applications, including max/min problems and related rates; the definite integral, simple applications; trigonometric functions; logarithms and exponential functions. Four class hours per week.

Limited to 30 students per section. Fall and spring semesters. In the fall semester, the intensive section (Section 01) is open only to students listed as eligible on the Mathematics placement list. The intensive section replaces one weekly class hour with a 90-to-120-minute group work day. The Department.

A continuation of MATH 111. Inverse trigonometric and hyperbolic functions; methods of integration, both exact and approximate; applications of integration to volume and arc length; improper integrals; l’Hôpital’s rule; infinite series, power series and the Taylor development; and polar coordinates. Four class hours per week.

Requisite: A grade of C or better in MATH 111 or placement into MATH 121 or consent of the Department. Limited to 30 students per section. Fall and spring semesters.The Department.

(Offered as STAT 135 and MATH 135) This course is an introductory statistics course that uses modeling as a unifying framework. The course provides a basic foundation in statistics with a major emphasis on constructing models from data. Students learn important concepts of statistics by mastering powerful and relatively advanced statistical techniques using computational tools. Topics include descriptive and inferential statistics, visualization, probability, study design, and multiple regression.

Students who have taken a semester of calculus (MATH 111 or higher, or equivalent placement) or who are majoring or planning to major in mathematics and/or statistics should take this course instead of STAT 111. (Students who have taken STAT 111, STAT136, or PSYC 122 may not also receive credit for STAT/MATH 135. Students who have taken ECON 360/361 will be admitted only with consent of the instructor.) No prior experience with statistical software is expected.

Requisite:

Student has completed or is in process of completing MATH 111 or has placement in MATH 121 or above, or has statistics placement of STAT135 or has consent of the instructor. Limited to 24 students per section. Fall and spring semesters. The Department.

Mathematical modeling is the process of translating a real world problem into a mathematical expression, analyzing it using mathematical tools and numerical simulations, and then interpreting the results in the context of the original problem. Discussion of basic modeling principles and case studies will be followed by several projects from areas including biology and the social sciences (e.g., flocking and schooling behavior, disease spread in populations, generation of artificial societies). This course has no requisites; projects will be tailored to each student’s level of mathematical preparation. Four class hours per week, with occasional in-class computer labs.

Limited to 24 students. Spring semester. Professor Leise.

The outcomes of many elections, whether to elect the next United States president or to rank college football teams, can displease many of the voters. How can perfectly fair elections produce results that nobody likes? We will analyze different voting systems, including majority rule, plurality rule, Borda count, and approval voting, and assess a voter’s power to influence the election under each system, for example, by calculating the Banzhaf power index. We will prove Arrow’s Theorem and discuss its implications. After exploring the pitfalls of various voting systems through both theoretical analysis and case studies, we will try to answer some pressing questions: Which voting system best reflects the will of the voters? Which is least susceptible to manipulation? What properties should we seek in a voting system, and how can we best attain them?

Limited to 24 students. Omitted 2021-22.

Elementary vector calculus; introduction to partial derivatives; multiple integrals in two and three dimensions; line integrals in the plane; Green’s theorem; the Taylor development and extrema of functions of several variables; implicit function theorems; Jacobians. Four class hours per week.

Requisite: A grade of C or better in MATH 121 or placement into MATH 211 or consent of the Department. Limited to 30 students per section. Fall and spring semesters. The Department.

This course serves as an introduction to mathematical reasoning and pays particular attention to helping students learn how to write proofs. The topics covered may include logic, elementary set theory, functions, relations and equivalence relations, mathematical induction, sequences, and quantifiers. Additional topics may vary from semester to semester. Four class hours per week.

Limited to 25 students. Spring and fall semesters. The Department.

This course is a mathematical treatment of fractal geometry, a field of mathematics partly developed by Benoit Mandelbrot (1924–2010) that continues to be actively researched in the present day. Fractal geometry is a mathematical examination of the concepts of self-similarity, fractals, and chaos, and their applications to the modeling of natural phenomena. In particular, we will develop the iterated function system (IFS) method for describing fractals, examine Julia sets, Mandelbrot sets, and study the concept of fractal dimension, among other things. Through the teaching of these concepts, the course will also lend itself to familiarizing students with some of the formalisms and rigor of mathematical proofs.

Requisite: MATH 211 or consent of the instructor. Limited to 35 students. Professor Folsom.

An introduction to the theory of rational integers; divisibility, the unique factorization theorem; congruences, quadratic residues. Selections from the following topics: cryptology; Diophantine equations; asymptotic prime number estimates; continued fractions; algebraic integers. Four class hours per week. Offered in alternate years.

Requisite: MATH 121 or consent of the instructor. Limited to 25 students. Spring semester. Professor Call.

Many security problems arise when two computers must communicate on a channel with eavesdroppers or malicious attackers. Public-key cryptography applies ideas from number theory and abstract algebra to address these problems. This course concerns the mathematical theory and algorithms needed to construct the most commonly-used public-key ciphers and digital signature schemes, as well as the attacks that must be anticipated when designing such systems. Several topics from number theory, abstract algebra, and algorithms will be introduced, including discrete logarithms, integer factorization algorithms, and elliptic curves. Depending on time and student interest, we may cover some newer systems that are believed to be secure against attacks by quantum computers but not yet commonly implemented in practice. Students will write short programs to implement the systems and to break badly implemented systems. No prior programming experience is expected; basic aspects of programming in Python will be taught in class. Four class hours per week.

Requisite: Experience writing proofs, such as MATH 220/221 or 271/272, or consent of the instructor. Spring semester. Professor Pflueger.

The study of differential equations is an important part of mathematics that involves many topics, both theoretical and practical. The course will cover first- and second-order ordinary differential equations, basic theorems concerning existence and uniqueness of solutions and continuous dependence on parameters, long-term behavior of solutions and approximate solutions. Four class hours per week.

Requisite: MATH 211 or consent of the instructor. Limited to 25 students. Spring semester. Professor Folsom.

The study of vector spaces over the real and complex numbers, introducing the concepts of subspace, linear independence, basis, and dimension; systems of linear equations and their solution by Gaussian elimination; matrix operations; linear transformations and their representations by matrices; eigenvalues and eigenvectors; and inner product spaces. MATH 271 will feature both proofs and applications, with special attention paid to the theoretical development of the subject. Four class meetings per week.

Requisite: MATH 211 or 220, or consent of the instructor. This course and MATH 272 may not both be taken for credit. Limited to 25 students. Fall and spring semesters. The Department.

The study of vector spaces over the real and complex numbers, introducing the concepts of subspace, linear independence, basis, and dimension; systems of linear equations and their solution by Gaussian elimination; matrix operations; linear transformations and their representations by matrices; eigenvalues and eigenvectors; and inner product spaces. This course will feature both proofs and applications, with special attention paid to applied topics such as least squares and singular value decomposition. Four class hours per week, with occasional in-class computer labs.

Requisite: MATH 211 or 220, or consent of the instructor. This course and MATH 271 may not both be taken for credit. Limited to 25 students. Fall and Spring semester: The Department.

This course is a continuation of the material in MATH 271 and 272, providing more insight into abstract vector spaces and operator theory. Topics may include least squares estimates, singular value decompositions, Jordan canonical forms, inner product spaces, linear functionals and duals, orthogonal polynomials, vector and matrix norms, the spectral theorem, eigenvalue inequalities, and error-correcting codes. Time permitting, applications to graph theory and discrete dynamical systems may be explored. Four class hours per week.

Requisites: MATH 271, MATH 272, or consent of the instructor. Spring semester. Limited to 25 students.

A graph is a collection of points with edges drawn between them. Graph theory was first introduced by Leonhard Euler in his solution to the Königsberg bridge problem in 1736. Since then, graph theory has become an active area of study in mathematics due both to its wide array of real life applications in biology, chemistry, social sciences and computer networking, and to its interactions with other branches of mathematics.

The course will start with an overview of the fundamental concepts and general results in graph theory, followed by explorations of a variety of topics in graph theory and their applications, including: connectivity, planar graphs, directed graphs, greedy algorithms, matchings, vertex and edge colorings. The course will end with the introduction of a more advanced topic. Four class hours per week.

Requisite: MATH 271 or 272 or consent of the instructor. MATH 220 or other prior experience with mathematical proofs is recommended. Limited to 30 students. Fall semester. Professor Contreras.

Optimization is a branch of applied mathematics focused on algorithms to determine maxima and minima of functions, often under constraints. Applications range from economics and finance to machine learning and information retrieval. This course will first develop advanced linear algebra tools, and then will study methods of convex optimization. Possible topics include linear, quadratic, second-order cone, and semidefinite models. Several applications will be explored, and algorithms will be implemented using mathematical software to aid numerical experimentation.

Requisite: MATH 211 and 271 or 272, or consent of the instructor. Limited to 30 students. Omitted 2021-22.

An introduction to analytic functions; complex numbers, derivatives, conformal mappings, integrals. Cauchy’s theorem; power series, singularities, Laurent series, analytic continuation; special functions.

Requisite: MATH 211 and prior experience with mathematical proofs, or consent of the instructor. Fall semester. Professor Rasheed.

A brief consideration of properties of sets, mappings, and the system of integers, followed by an introduction to the theory of groups and rings including the principal theorems on homomorphisms and the related quotient structures; integral domains, fields, polynomial rings. Four class hours per week.

Requisite: MATH 211 and either MATH 271 or 272, or consent of the instructor. Students with a grade of B+ or lower in linear algebra are encouraged to take another 200-level course with proofs before taking MATH 350.

Limited to 25 students. Fall semester: Professor Call. Spring semester: Professor Contreras.

Completeness of the real numbers; topology of n-space including the Bolzano-Weierstrass and Heine-Borel theorems; sequences, properties of continuous functions on sets; infinite series, uniform convergence. The course may also study the Gamma function, Stirling’s formula, or Fourier series. Four class hours per week.

Requisite: MATH 211 and either MATH 271 or 272, or consent of the instructor. Students with a grade of B+ or lower in linear algebra are encouraged to take another 200-level course with proofs before taking MATH 355.

Limited to 25 students. The Department.

(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.

Requisite:

Student has completed or is in the process of completing MATH 121, or has MATH 211 placement, or has consent of the instructor. Limited to 24 students. Fall semester. Professor Horton.

(Offered as STAT 370 and MATH 370) This course examines the theory underlying common statistical procedures including visualization, exploratory analysis, estimation, hypothesis testing, modeling, and Bayesian inference. Topics include maximum likelihood estimators, sufficient statistics, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models.

Requisite:

Student has completed or is in the process of completing: STAT 111 or MATH/STAT 135 or STAT136 or has STAT 230 placement, and STAT/MATH 360, or has consent of the instructor. Limited to 25 students. Spring semester. Professor Donges.

Independent reading course.

Fall and spring semesters. The Department.

Lie groups and Lie algebras appear naturally in the study of symmetries of geometric objects. Lie algebras carry local information and can be studied using tools from linear algebra. Finite dimensional Lie groups can be constructed using techniques from calculus and group theory.

This course serves as a first introduction to Lie groups and Lie algebras. We will examine the structure of finite dimensional matrix Lie groups, the exponential and differentiation maps, as well as compact Lie groups. We will also study Lie algebras, ideals and homomorphisms, nilpotent and solvable Lie algebras, Cartan subalgebras, semisimplicity and root systems, and the classification of semisimple Lie algebras. This classification is not only a fundamental result in Lie Theory, but is also an archetype of classifications that appear in other areas of math. More amazingly, this classification is embodied in simple combinatorial pictures called Dynkin diagrams, which underlie surprisingly disparate fields, such as geometric group theory, quiver representation theory, and string theory. Four class meetings per week.

Requisite: MATH 350 or consent of the instructor. Fall semester.

The quadratic formula shows us that the roots of a quadratic polynomial possess a certain symmetry. Galois Theory is the study of the corresponding symmetry for higher degree polynomials. We will develop this theory starting from a basic knowledge of groups, rings, and fields. One of our main goals will be to prove that there is no general version of the quadratic formula for a polynomial of degree five or more. Along the way, we will also show that a circular cake can be divided into 17 (but not 7) equal slices using only a straight-edged knife.

Requisite: MATH 350 or consent of the instructor. Spring semester. Prof. Daniels.

An introduction to general topology: the topology of Euclidean, metric and abstract spaces, with emphasis on such notions as continuous mappings, compactness, connectedness, completeness, separable spaces, separation axioms, and metrizable spaces. Additional topics may be selected to illustrate applications of topology in analysis or to introduce the student briefly to algebraic topology. Four class hours per week. Offered in alternate years.

Requisite: MATH 355. Spring semester. Prof. Rasheed.

Open to seniors with the consent of the Department. Fall semester. The Department.

(Offered as STAT 108 and ECON 108) This course will provide a rigorous presentation of fundamental statistical principles and ethics. We will discuss standards for relationships between statisticians and policymakers, researchers, the press, and other institutions, as well as the standards for interactions between statisticians and their employers/clients, colleagues and research subjects. The course will explore how the interplay of institutions (e.g., organizations, systems, laws, codes of professional ethics) and the broader sociopolitical culture affect the production of reliable, high quality statistics. Students will also explore the implications of statistical principles and ethics for the operation of national, regional, and international official statistical systems. In addition, we will investigate the proper place of official statistics within a government system that operates with separate branches. Students will gain a strong foundation in international statistical principles and professional ethics as well as an understanding and the tools to assess the quality of the statistics they use. The course is designed to make students responsible and effective supporters of reliable, high quality statistics in their professions. Students will particularly learn how to assess the quality of official statistics produced by governments and how to identify areas for improvement. Examples, case studies, readings from statistical practice, and discussion will provide a full appreciation of real world applications. The course is also intended for non-majors interested in an introduction to quantitative social science and the use of data in public policy.

Limited to 30 students. Visiting Scholar Andreas Georgiou.

This is an interactive course designed to help students understand inequities in mental health issues via statistics. We will begin the course by examining mental health stigmas and practice self-care exercises to train our “happy muscles” together. We will discover the scientific evidence behind those self-care practices and explore existing racial disparities in mental health care systems, while learning about important statistical concepts and mastering our data analysis skills using R (a popular statistical software package). Statistical topics covered include descriptive statistics, visualization, study design, simulation-based inferences, and multiple regression. Students are expected to play an active role in co-creating the course and co-building an inclusive learning community with their peers and the professor. Course components include weekly reading and discussion, regular self-reflections and problem sets, and collaborative work in groups. We will use an OER (Open-Educational-Resources) textbook in this course. No prior experience with statistical software is expected.

This course is an alternative to STAT135 (Introduction to Statistics via Modeling) with a special focus on mental health issues. Students may not receive credit for both this course and STAT 111, STAT 135, or PSYC122. Limited to 24 students.

Fall semester. Prof. Liao.

This course will focus on the use of text analytics to explore the rich history of Holyoke, MA. Holyoke has been a site of rapid industrialization, multiple waves of immigration and migration, urban development, rapid changes in its workforce, and ongoing creativity, activism, and innovation. Students will develop the skills to mine textual data from archives at the Wistariahurst Museum, the Holyoke Public Library, Holyoke Community College, and other repositories to address important questions regarding the development and history of this planned community. Topics include sentiment analysis, regular expressions, document-term-matrices, named entity recognition, and Latent Dirichlet analysis. Requisite:

Student has completed or is in the process of completing: STAT 111 or MATH/STAT 135 or STAT136 or PSYC 122 or has a placement of STAT230.

Recommended requisite: HIST 351 or COSC 111.

Professor Horton.

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite:

Student has completed or is in process of completing any of the following course(s): STAT 111 or MATH/STAT 135 or STAT 136 or PSYC 122 or has a STAT 230 placement or has consent of the instructor. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Spring semester: The Department.

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools.

Requisite:

STAT 111 or STAT 135 or STAT136 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department.

Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering). Additional methods covered may include factor analysis, dimension reduction methods, or network analysis at instructor discretion. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.

The course is expected to include small group work, interactive labs, peer interactions such as peer review and short presentations, and a personal project, to foster student engagement in the course and with each other.

Requisite:

Requisite: STAT 111 or 135 or 136 or PSYC 122. Limited to 24 students. Fall semester. Omitted 2021-22. Professor Wagaman.

Statistical Communication is an important component of the capacity to "think with data." This course will integrate theoretical and practical aspects of statistics with a focus on communicating results and their implications. Students will gain experience clearly synthesizing and explaining complex data using diverse predictive and explanatory models. Learning objectives include: understanding the role of a statistician, developing communications skills, working collaboratively on group projects, designing studies to collect information, acquiring existing data resources, utilizing publications in statistics, creating reproducible research and developing oral arguments, relevant project reports, and dynamic graphical displays. Emphasis will be placed on the use of statistical software, data management, visual presentation, and oral and written communication skills that are necessary for communicating technical content.

Requisite:

Student has completed or is in the process of completing STAT 230 or has consent of the instructor. Limited to 24 students. Fall semester. Omitted 2021-22. Professor Matheson.

Epidemiology is the study of the distribution and determinants of disease and health in human populations. It typically involves the analysis of multivariate observational data that pose challenges when trying to make causal conclusions. The course will focus on reasoning about cause and effect, study design, bias and missing data, models and analysis of risk, detection and classification, and modern approaches to confounding and causal inference. Topics include: Measures of disease (incidence and prevalence); Measures of association (relative risk, odds ratio, relative hazard, excess risk, attributable risk); Study designs (exposure and disease base sampling); Assessing significance in a 2x2 table; Assessing significance in a 2x2x2 table; Missing data; Introduction to confounding; Matching; Propensity score adjustment; Unmeasured confounding; Introduction to causal inference and counterfactuals; Causal graphs; and D-separation.

Requisite: STAT-230 (or PSYC 122 and PSYC 200 and consent of the instructor). Omitted 2021-22.. Professor Horton.

Competitions, which can include individual and team sports, eSports, tabletop gaming, preference formation, and elections, produce data dependent on interrelated competitors and the decision, league, or tournament format. In this course, students will learn to think about the ways a wide variety of statistical methodologies can be applied to the complex and unique data that emerge through competition, including paired comparisons, decision analysis, rank-based and kernel methods, and spatio-temporal methods. The course will focus on the statistical theory relevant to analyzing data from contests and place an emphasis on simulation and data visualization techniques. Students will develop data collection, wrangling,combination, exploration, analysis, and interpretation skills individually and in groups. Applications may include rating players and teams, assessing shot quality, animating player tracking data, roster construction, comparing alternative voting systems, developing optimal strategies for games, and predicting outcomes. Prior experience with probability such as STAT 360 may be helpful, but is not required.

Requisite: STAT 230 and STAT 231. Limited to 24 students. Fall semester. Professor McShane.

Real world datasets are plagued by missing observations. Statistical software packages often ignore these cases by default, but there are better ways to approach the problem. This course will introduce students to the different missing data mechanisms and explore naive and modern methods for handling missing data. It will prepare students to read the current literature in this area and have broad appreciation for the implications of missing data.

This course is intended for students who have experience with standard statistical methods for complete data and want to extend them to handle missing data in practice.

Requisite:

Student has completed or is in the process of completing STAT 230 and STAT/MATH 370, or has consent of the instructor. Fall semester: Prof. Correia.

Linear regression and logistic regression are powerful tools for statistical analysis, but they are only a subset of a broader class of generalized linear models. This course will explore the theory behind and practical application of generalized linear models for responses that do not have a normal distribution, including counts, categories, and proportions. We will also delve into extensions of these models for dependent responses such as repeated measures over time.

Requisite:

Student has completed or is in the process of completing: STAT 230 and STAT/MATH 360. Limited to 20 students. Spring semester. Professor Bailey.

Fall and spring semesters. The Department.

Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems. Course topics will vary from year to year depending on the instructor and selected case studies but will include static and dynamic visualization techniques to summarize and display high dimensional data, advanced topics in design and linear regression, ethics, and selected topics in data mining. Other topics may vary but might include nonparametric analysis, spatial data, and analysis of network data. Through a series of case studies, students will develop the capacity to think and compute with data, undertake and assess analyses, and effectively communicate their results using written and oral presentation.

Requisite:

Student has completed or is in process of completing all of the following course(s): STAT 230, STAT 231, STAT/MATH 370, and at least one COSC course numbered 111 or higher, or has consent of the instructor. Limited to 20 students. Fall semester. Professor Wagaman.

Fall semester. The Department.