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  • 3.00 Credits

    In this course we discuss teaching methods for secondary classroom, including appropriate use of technology. Important components are developing topics across the curriculum, connections between mathematical concepts, as well as other important issues in running one's classroom: setting norms, goals, assessing students work and knowledge, providing practice and direction, managing diverse populations. This is a hands on class with ample opportunities for teaching. First session course. Prerequisites: "C" or better in (MATH 3100 AND MATH 4030). Corequisites: "C" or better in MATH 4095.
  • 2.00 Credits

    This is a practicum course that will parallel MATH 4090. One purpose of this course is to help students develop an awareness of meaningful ways to teach geometry and algebra to students in the 7th through 12th grades. Another purpose of this course is to help students become reflective teachers who can look critically at textbooks, teaching materials, assessments and their own teaching for the purpose of better meeting the needs of students. This course will have a strong student teaching component. Prerequisites: "C" or better in MATH 3100 AND MATH 4030. Co-requisite: MATH 4090.
  • 3.00 Credits

    The course begins by bootstrapping student's coding skills in the programming language Python, followed by a review of the relevant concepts from statistics. After that, we will move through a series of data science methods using real-life, project-based, lectures and computer labs. The major goals of this course are to learn how to use tools for acquiring, cleaning, analyzing, exploring, and visualizing data; making data-driven inferences and decisions; and effectively communicating results. These will be accomplished through an in-depth sequence of topics which will introduce students to the following data preparation and analysis methods: Acquiring data through web-scraping and data APIs, Cleaning and reshaping messy datasets using methods such as data frames, regular expressions or dedicated tools, Exploratory data analysis and visualization, Hypothesis testing, Clustering and classification, Rating and ranking, Recommendations, Network analysis, Regression and statistical inference, Natural language processing, Working with large data: databases, parallel programming. A major component of this course will be learning how to use python-based programming tools to apply these methods to real-life datasets. Students should have a basic-level of programming experience before taking this course. Prerequisites: 'C' or better in (MATH 1170 OR 1210 OR MATH 1215 OR MATH 1250 OR 1310 OR 1311) OR AP AB Calculus score of 4+ OR AP Calculus BC score of 3+ OR IB Math score of 5+.
  • 1.00 - 3.00 Credits

    Attend Intermediate Algebra course, assist in grading, teach a weekly review session, meet weekly to discuss pedagogy and evaluations of teaching. Prerequisites: Department Consent.
  • 3.00 Credits

    Complex functions and their differentiability, complex integrals, power series, the Cauchy theorem and formulas, residues and applications to evaluating integrals, conformal mappings and applications. Graduate students who need this course should consult the instructor. Prerequisites: "C" or better in MATH 3220.
  • 3.00 Credits

    Introduction to the geometry and calculus of surfaces in 3-dimensional Euclidean space. Topics include regular surfaces, differentiable functions on surfaces, first fundamental form, area, orientability, the Gauss map, curvature, the hyperbolic plane including geodesics and isometries, Gauss-Bonnet, differential forms, surface integrals, Stokes's Theorem, and Green's Theorem. Prerequisites: 'C' or better in MATH 3220
  • 3.00 Credits

    An overview of algebraic number theory, covering factorization and primes, modular arithmetic, quadratic residues, continued fractions, quadratic forms, and diophantine equations. Prerequisites: 'C' or better in MATH 2250 OR MATH 2270 OR MATH 2271
  • 4.00 Credits

    The goals of the class are (i) to introduce the students to a range of modern mathematical tools; (ii) to teach the students the skill of building tractable mathematical models of biological processes; (iii) to show how to combine the mathematical knowledge, the numerical simulations (in Matlab) and biological intuition to derive new insights into the functioning of living systems. Mathematical topics include introduction to linear algebra, complex numbers, geometric dynamical systems, bifurcation theory, probability, Markov chain, partial differential equations. Biological topics may include modeling heart and circulation, kidneys, circadian clocks, brain rhythms, HIV, antibiotic resistance in bacteria, regulation of gene expression, biological pattern formation. Prerequisites: 'C' or better in MATH 2250 OR MATH 2280 OR MATH 2281
  • 3.00 Credits

    Explore a topic of significant mathematical interest, or an application of mathematics to a significant problem in science, engineering, or business. Students help to present the material or the results of their own investigations, and write a report on their findings. Prerequisites vary depending on the topic. Prerequisites: Instructor Consent.
  • 1.00 - 4.00 Credits

    Mathematics-related work in industry, business, or government. Prerequisites: Instructor Consent.