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Course Criteria
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1.50 Credits
This course introduces the principles of software design and development using the object-oriented paradigm and the Java programming language. Design techniques covered are programming by contract and Unified Modeling Language (UML) class diagrams. Students will build graphical user interfaces and learn to develop and use abstract data types (ADTs) such as lists, trees, sets, and graphs. Students will study the use of these data structures in applications such as simulation, computational science, and networks. For each ADT, students will analyze their advantages and disadvantages to determine which one works best for a given application. There is a required 1.5 hour laboratory section associated with this course. Prerequisite: Any one of the three introductory courses Computer Science 120, 121, or 123, or consent of instructor. Every semester. (4 credits)
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4.00 Credits
This introductory-level course focuses on those aspects of calculus that are particularly useful in applied work in the natural and social sciences. There is a strong emphasis on developing mathematical modeling skills. The topics include differential calculus of functions of one and several variables, differential and difference equations, and the geometry of high-dimensional space. Case studies are drawn from varied areas, including biology, economics, and physics. The course is designed both for students with no previous calculus, and students who have had one or two semesters of AP calculus (but who do not intend directly to take Mathematics 236 or 237). Every semester. (4 credits)
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4.00 Credits
An introduction to the basic techniques and methods used in combinatorial problem-solving. Includes basic counting principles, induction, logic, recurrence relations, and graph theory. Every semester. (4 credits)
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3.00 Credits
Differentiation and integration of functions of a single variable, with applications. Main topics: Limit definition of the derivative and integral, exponential growth, chain rule, Riemann sums, numerical integration, integration by substitution and parts, improper integrals, geometric series, Taylor polynomials. This is a more in-depth course than Mathematics 135, and should be taken instead of Mathematics 135 by students intending to continue in mathematics. Prerequisites: High school calculus or Mathematics 135. Every semester. (4 credits)
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3.00 Credits
An introduction to basic concepts of data analysis and statistics in the spirit of the liberal arts. Emphasis on data analysis, model assumptions, and interpreting results. Examples and techniques drawn primarily from the social sciences. Major topics: uncertainty/variation, data acquisition, graphical techniques, descriptive statistics, exploratory versus confirmatory analysis, statistical inference. Recommended for students in humanities/fine arts/social sciences and/or those not planning to pursue careers in quantitative analysis; prospective economics majors are encouraged to take Mathematics 155. Students who successfully complete this course cannot receive credit for Mathematics 154. Prerequisite: High school algebra. Every semester. (4 credits)
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3.00 Credits
An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, experiment and study design, probability, hypothesis testing, multivariate regression, single and multiway analysis of variance, logistic regression. Prerequisites: Mathematics 135 or Mathematics 236 or Mathematics 237 or permission of instructor. Every semester. (4 credits)
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3.00 Credits
An in-depth introduction to the design and analysis of algorithms. Topics may include algorithmic paradigms and structures, including recursion, divide and conquer, dynamic programming, greedy methods, branch and bound, randomized, probabilistic, and parallel algorithms, non-determinism and NP completeness. Applications to searching and sorting, graphs and optimization, geometric algorithms, and transforms. Prerequisites: Computer Science 124, Mathematics 136, or consent of instructor. Every fall. (4 credits)
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3.00 Credits
This course builds upon the software design foundation started in Computer Science 124. Students will design and implement medium-sized software projects using modern software design principles such as design patterns, refactoring, fault tolerance, stream-based programming, and exception handling. The concept of a distributed computing system will be introduced, and students will develop multithreaded and networked applications using currently available software libraries. Advanced graphical user interface methods will be studied with an emphasis on appropriate human-computer interaction techniques. Students will use operating systems services and be introduced to methods of evaluating the performance of their software. Prerequisite: Computer Science 124, or consent of instructor. Every fall. (4 credits)
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3.00 Credits
This course blends mathematical computation, theory, abstraction, and application. It starts with systems of linear equations and grows into the study of matrices, vector spaces, linear independence, dimension, matrix decompositions, linear transformations, eigenvectors, and their applications. Prerequisite: Mathematics 136 or Mathematics 137 or, with permission of instructor, Mathematics 135. Every semester. (4 credits)
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3.00 Credits
Differentiation and integration of functions of two and three variables. Applications of these, including optimization techniques. Also includes introduction to vector calculus, with treatment of vector fields, line and surface integrals, and Green's Theorem. Prerequisite: Mathematics 137. Every semester. (4 credits)
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