Data Science Courses

Course Description

Interdisciplinary, discussion-based seminars that provide students with a foundation in the theory and methods of: science and mathematics; These interdisciplinary seminars offer students opportunities via discussion and/or writing to explore implications, connections, and other perspectives (e.g., philosophy, ethics, law, arts, policy, history, politics, medicine/health, the other sciences, etc.). By design they explicitly connect two areas of the 91传媒 curriculum, i.e., two disciplines within the CLAS, the CLAS with the SBL, or the CLAS with the Creative School. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of multiple disciplines; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of [problem-solving, laboratory experiments, projects] for deeper learning. These courses are open to all 91传媒 students, although priority is given to students enrolled in the Honor Scholar Program. Students in the Honor Scholar Program take one course each in AH, SM, and SS; each of these courses can be used to fulfill 91传媒's distribution requirements. May not be taken Pass/Fail.

Distribution Area

Science and Math

Credits

1 course

Course Description

Interdisciplinary, discussion-based seminars that provide students with a foundation in the theory and methods of the social sciences. These interdisciplinary seminars offer students opportunities via discussion and/or writing to explore implications, connections, and other perspectives (e.g., philosophy, ethics, law, arts, policy, history, politics, medicine/health, the other sciences, etc.). By design they explicitly connect two areas of the 91传媒 curriculum, i.e., two disciplines within the CLAS, the CLAS with the SBL, or the CLAS with the Creative School. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of multiple disciplines; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of [problem-solving, laboratory experiments, projects] for deeper learning. These courses are open to all 91传媒 students, although priority is given to students enrolled in the Honor Scholar Program. Students in the Honor Scholar Program take one course each in AH, SM, and SS; each of these courses can be used to fulfill 91传媒's distribution requirements. May not be taken Pass/Fail.

Distribution Area

Social Science

Credits

1 course

Course Description

An introduction to the concepts of discrete mathematics with an emphasis on problem solving and computation. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations and set theory. This course may have a laboratory component.

Distribution Area

Science and Math

Credits

1 course

Course Description

An introduction to the concepts of discrete mathematics with an emphasis on problem solving and computation. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations and set theory. This course may have a laboratory component.

Distribution Area

Science and Math

Credits

1 course

Course Description

Extensive review of topics from algebra, trigonometry, analytic geometry, graphing and theory of equations. A study of functions, limits, continuity and differentiability of algebraic and transcendental functions with applications. Not open to students with credit in MATH 151 or any higher level calculus course.

Credits

1 course

Course Description

A continuation of MATH 135. Topics include further study of differentiation, integration of algebraic and transcendental functions with applications, and techniques of integration. Completion of this course is equivalent to completing MATH 151 and is adequate preparation for any course requiring MATH 151.

Distribution Area

Science and Math

Prerequisites

MATH 135.

Credits

1 course

Course Description

A continuation of MATH 135. Topics include further study of differentiation, integration of algebraic and transcendental functions with applications, and techniques of integration. Completion of this course is equivalent to completing MATH 151 and is adequate preparation for any course requiring MATH 151.聽

Distribution Area

Science and Math

Prerequisites

MATH 135.

Credits

1 course

Course Description

This course introduces students to elementary probability and data analysis via visual presentation of data, descriptive statistics and statistical inference. Emphasis will be placed on applications with examples drawn from a wide range of disciplines in both physical and behavioral sciences and humanities. Topics of statistical inference include: confidence intervals, hypothesis testing, regression, correlation, contingency tales, goodness of fit and ANOVA. The course will also develop familiarity with the most commonly encountered tables for probability distributions: binomial, normal, chi-squared, student-t and F. Students who have completed or are concurrently enrolled in ECON 350 will only receive one-half credit for MATH 141.

Distribution Area

Science and Math

Credits

1 course

Course Description

This course introduces students to elementary probability and data analysis via visual presentation of data, descriptive statistics and statistical inference. Emphasis will be placed on applications with examples drawn from a wide range of disciplines in both physical and behavioral sciences and humanities. Topics of statistical inference include: confidence intervals, hypothesis testing, regression, correlation, contingency tales, goodness of fit and ANOVA. The course will also develop familiarity with the most commonly encountered tables for probability distributions: binomial, normal, chi-squared, student-t and F.聽Students who have completed or are concurrently enrolled in ECON 350 will only receive one-half credit for MATH 141.

Distribution Area

Science and Math

Credits

1 course

Course Description

This interdisciplinary course will be an engaging and lively look into modeling of phenomena (like voting theory, game theory, traveling salesman problem, population growth/decay etc.) in natural and social sciences. This course will emphasize relationships between the world in which we live and mathematics and is aimed to develop one's mathematical and problem-solving skills in the process. Topics covered will include Modeling Change, Modeling Process and Proportionality, Model Fitting, Probabilistic Modeling, Modeling with Decision Theory, Optimization of Discrete Models, Game Theory and Modeling Using Graph Theory. It will be beneficial for the student to have knowledge in Algebra and Trigonometry for this course.

Distribution Area

Science and Math

Credits

1 course

Course Description

A study of functions, limits, continuity, differentiation and integration of algebraic and transcendental functions with elementary applications.

Distribution Area

Science and Math

Credits

1 course

Course Description

A study of functions, limits, continuity, differentiation and integration of algebraic and transcendental functions with elementary applications.

Distribution Area

Science and Math

Credits

1 course

Course Description

Techniques of integration, parametric equations, infinite series and an introduction to the calculus of several variables.

Distribution Area

Science and Math

Prerequisites

MATH 136 or MATH 151.

Credits

1 course

Course Description

Techniques of integration, parametric equations, infinite series and an introduction to the calculus of several variables.聽

Distribution Area

Science and Math

Prerequisites

MATH 136 or MATH 151.

Credits

1 course

Course Description

An on-campus course offered during the Winter or May term. May be offered for .5 course credits or as a co-curricular (0 credit). Counts toward satisfying the Extended Studies requirement.

Credits

0.5 course

Course Description

Student-initiated independent project under faculty guidance. Offered as a co-curricular (0 credit) Extended Studies experience.

Course Description

The basic approach in this course will be to present mathematics in a more humanistic manner and thereby provide an environment where students can discover, on their own, the quantitative ideas and mathematical techniques used in decision-making in a diversity of disciplines. Students work with problems obtained from industry and elsewhere.

Credits

1 course

Course Description

An introduction to concepts and methods that are fundamental to the study of advanced mathematics. Emphasis is placed on the comprehension and the creation of mathematical prose, proofs, and theorems. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations, and set theory.

Distribution Area

Science and Math

Prerequisites

MATH 123 or MATH 136 or MATH 151.

Credits

1 course

Course Description

An introduction to concepts and methods that are fundamental to the study of advanced mathematics. Emphasis is placed on the comprehension and the creation of mathematical prose, proofs, and theorems. Topics are selected from Boolean algebra, combinatorics, functions, graph theory, matrix algebra, number theory, probability, relations, and set theory.聽

Distribution Area

Science and Math

Prerequisites

MATH 123 or MATH 136 or MATH 151.

Credits

1 course

Course Description

This course introduces students to the theory behind standard statistical procedures. The course presumes a working knowledge of single-variable calculus on the part of the student. Students are expected to derive and apply theoretical results as well as carry out standard statistical procedures. Topics covered will include moment-generating functions, Gamma distributions, Chi-squared distributions, t-distributions, and F-distributions, sampling distributions and the Central Limit Theorem, point estimation, confidence intervals, and hypothesis testing.

Distribution Area

Science and Math

Prerequisites

MATH 136 or MATH 151.

Credits

1 course

Course Description

This course introduces students to the theory behind standard statistical procedures. The course presumes a working knowledge of single-variable calculus on the part of the student. Students are expected to derive and apply theoretical results as well as carry out standard statistical procedures. Topics covered will include moment-generating functions, Gamma distributions, Chi-squared distributions, t-distributions, and F-distributions, sampling distributions and the Central Limit Theorem, point estimation, confidence intervals, and hypothesis testing.聽

Distribution Area

Science and Math

Prerequisites

MATH 136 or MATH 151.

Credits

1 course

Course Description

An introduction to the calculus of several variables. Topics include vectors and solid analytic geometry, multidimensional differentiation and integration, and a selection of applications.

Distribution Area

Science and Math

Prerequisites

MATH 152.

Credits

1 course

Course Description

An introduction to the calculus of several variables. Topics include vectors and solid analytic geometry, multidimensional differentiation and integration, and a selection of applications.聽

Distribution Area

Science and Math

Prerequisites

MATH 152.

Credits

1 course

Course Description

This course provides an introduction to the field of data science from data to knowledge and gives students' hands-on experience with tools and methods. This course focuses on using computational, statistical, and mathematical tools for data acquisition, exploration, manipulation, visualization, analysis, modeling, and classification, as well as the communication of results.

Prerequisites

MATH 141 (or equivalent) or permission of instructor.

Credits

1 course

Course Description

This course provides an introduction to the field of data science from data to knowledge and gives students' hands-on experience with tools and methods. This course focuses on using computational, statistical, and mathematical tools for data acquisition, exploration, manipulation, visualization, analysis, modeling, and classification, as well as the communication of results.聽

Prerequisites

MATH 141 (or equivalent) or permission of instructor.

Credits

1 course

Course Description

Vector spaces, linear transformations, matrices, determinants, eigenvalues and eigenvectors and applications.

Prerequisites

MATH 152 or permission of instructor.

Credits

1 course

Course Description

Vector spaces, linear transformations, matrices, determinants, eigenvalues and eigenvectors and applications.聽This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, or projects for deeper learning.

Prerequisites

MATH 152 or permission of instructor.

Credits

1 course

Course Description

Vector spaces, linear transformations, matrices, determinants, eigenvalues and eigenvectors and applications.聽 This course will encourage the logical development of argument, clear and precise diction, and a coherent prose style; the development of expository writing skills as they apply to mathematics; and the responsible, appropriate, and effective use of sources and special or technical language.

Prerequisites

MATH 152 or permission of instructor.

Credits

1 course

Course Description

Selections from advanced plane, differential, non-Euclidean or projective geometry.

Prerequisites

either MATH 223 or MATH 270.

Credits

1 course

Course Description

Algorithmic Graph Theory is that branch of Mathematics that deals with mathematical structures that are used to model pairwise relations between objects from a certain collection, together with algorithms used to manipulate these models. Algorithmic Graph Theory is used to model many types of relations and process dynamics in physical, biological and social systems. This course helps students develop the mathematical underpinnings of the theory of graphs and algorithms, a branch of discrete mathematics. This course provides an excellent background to an exciting area of mathematics that has applications in fields like computer science, economics, and engineering.

Distribution Area

Science and Math

Prerequisites

CSC 233, foundations of computation or MATH 270, linear algebra or MATH 223, foundations of advanced mathematics. It will be beneficial for the student to be fluent in a programming language for this course.

Credits

1 course

Course Description

Dive into the world of financial mathematics and unlock the power of money over time. This course equips students with advanced mathematical skills to navigate complex financial landscapes. Master the art of calculating the time value of money, analyzing investment opportunities, and understanding the intricacies of loans and bonds. Explore yield curves, portfolio management, and asset-liability matching techniques used by financial professionals. Gain hands-on experience with real-world applications in investment analysis, capital budgeting, and risk management. Upon completing this course, students will be able to navigate financial decisions confidently and strategically in an ever-evolving economic landscape.

Prerequisites

ECON 100 and MATH 136 or MATH 151 or ECON 375

Credits

1 course

Course Description

This problem-solving seminar emphasizes professional development in actuarial science and risk management. Students engage with complex actuarial problems, enhancing their understanding of the field through practical applications and exam preparation resources. The seminar introduces financial instruments, explores determinants of interest rates, and examines methods to approximate the effects of interest rate changes. Participants also analyze and discuss actuarial risk management articles. This course is particularly beneficial for students preparing for the Financial Mathematics (FM) actuarial exam.

Prerequisites

MATH/BUSA 331 which may be taken concurrently.

Credits

0.5 course

Course Description

This course in Quantitative Risk Analysis provides students with a comprehensive interdisciplinary foundation in quantitative techniques for financial risk assessment and management. The curriculum encompasses the principles of fixed-income securities, Modern Portfolio Theory (MPT) and advanced topics like the Black-Scholes formula and the Binomial Tree method for derivatives pricing. The course emphasizes practical skills in identifying, calculating, and mitigating risks associated with fixed-income securities, equities, options, and futures. Students will work on projects that simulate real-world scenarios,gaining experience in managing the interest rate risk of fixed-income securities by controlling durations, applying MPT to create efficient portfolios, using stock index futures to manage market exposure, and leveraging stock options to hedge individual stock risks.

Prerequisites

MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.

Credits

1 course

Course Description

(Cross-listed with BUSA 336) This course in Quantitative Risk Analysis provides students with a comprehensive interdisciplinary foundation in quantitative techniques for financial risk assessment and management. The curriculum encompasses the principles of fixed-income securities, Modern Portfolio Theory (MPT) and advanced topics like the Black-Scholes formula and the Binomial Tree method for derivatives pricing. The course emphasizes practical skills in identifying, calculating, and mitigating risks associated with fixed-income securities, equities, options, and futures. Students will work on projects that simulate real-world scenarios, gaining experience in managing the interest rate risk of fixed-income securities by controlling durations, applying MPT to create efficient portfolios, using stock index futures to manage market exposure, and leveraging stock options to hedge individual stock risks.聽This course will encourage the logical development of argument, clear and precise diction, and a coherent prose style; the development of expository writing skills as they apply to business analytics; and the responsible, appropriate, and effective use of sources and special or technical language.

Prerequisites

MATH 136 or MATH 151 or ECON 375, ECON 100, and either MATH 141 or ECON 350.

Credits

1 course

Course Description

Topics in statistics.

Credits

1 course

Course Description

Topics in statistics. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, and/or projects for deeper learning.

Credits

1 course

Course Description

Topics in statistics. This course will encourage the understanding of quantitative and mathematical concepts, representational formats, and methodologies of mathematics; the evaluation of quantitative evidence and arguments; the use of quantitative information to make decisions; and the use of problem-solving, laboratory experiments, and/or projects for deeper learning.

Credits

1 course

Course Description

This course is designed to provide students with a solid overview of basic and advanced topics in regression analysis. This course mainly covers the simple and multiple linear regression models--method of least squares, model and assumptions; testing hypotheses; estimation of parameters and associated standard errors; correlations between parameter estimates; standard error of predicted response values; inverse prediction; regression through the origin; matrix approach; extra sum of squares principle as used in model building; partial F-tests and sequential F-tests. More advanced topics in regression analysis, such as selecting the 'best' regression equation, classical approaches: all possible regressions; backward elimination; forward selection; stepwise regression; indicator (dummy) variables in regression also introduces in this course. Additionally, nonlinear (binary) logistic regression model with qualitative independent variables discusses in this course. A statistical computing package, such as R, is used throughout the course.

Distribution Area

Science and Math

Prerequisites

MATH 141 or ECON 350 or PSY 214 or BIO 275

Credits

1 course

Course Description

This course is designed to provide students with a solid overview of basic and advanced topics in regression analysis. This course mainly covers the simple and multiple linear regression models--method of least squares, model and assumptions; testing hypotheses; estimation of parameters and associated standard errors; correlations between parameter estimates; standard error of predicted response values; inverse prediction; regression through the origin; matrix approach; extra sum of squares principle as used in model building; partial F-tests and sequential F-tests. More advanced topics in regression analysis, such as selecting the 'best' regression equation, classical approaches: all possible regressions; backward elimination; forward selection; stepwise regression; indicator (dummy) variables in regression also introduces in this course. Additionally, nonlinear (binary) logistic regression model with qualitative independent variables discusses in this course. A statistical computing package, such as R, is used throughout the course.聽

Distribution Area

Science and Math

Prerequisites

MATH 141 or ECON 350 or PSY 214 or BIO 275

Credits

1 course

Course Description

This course is designed to provide students with an introduction to statistical computing using RStudio. This course will have two components. In the first part of the course, students will learn data manipulations, data structures, matrix manipulation, database operation, and functions. In the second part of the course, students will learn statistical computing topics including simulation studies and Monte Carlo methods, numerical optimization, Bootstrap resampling methods, and visualization. Students will be introduced to some packages and technologies that are useful for statistical computing. Through producing numerical summaries and creating customized graphs, students will be able to discuss the results obtained from their analyses and to generate dynamic and reproducible documents.

Distribution Area

Science and Math

Prerequisites

Math 141 (or ECON 350/BIO 375/PSY 214) and Math 151 (or MATH 135-136).

Credits

1 course

Course Description

This course is designed to provide students with an introduction to statistical computing using RStudio. This course will have two components. In the first part of the course, students will learn data manipulations, data structures, matrix manipulation, database operation, and functions. In the second part of the course, students will learn statistical computing topics including simulation studies and Monte Carlo methods, numerical optimization, Bootstrap resampling methods, and visualization. Students will be introduced to some packages and technologies that are useful for statistical computing. Through producing numerical summaries and creating customized graphs, students will be able to discuss the results obtained from their analyses and to generate dynamic and reproducible documents.聽

Distribution Area

Science and Math

Prerequisites

Math 141 (or ECON 350/BIO 375/PSY 214) and Math 151 (or MATH 135-136).

Credits

1 course

Course Description

A study of the theory of limits, continuity, differentiation, integration, sequences and series.

Prerequisites

MATH 152 and either MATH 223 or MATH 270.

Credits

1 course

Course Description

Equations of the first degree, linear differential equations, systems of equations with matrix methods and applications. Selected topics from power series solutions, numerical methods, boundary-value problems and non-linear equations.

Prerequisites

MATH 152 and MATH 270.

Credits

1 course

Course Description

Analysis of algorithms frequently used in mathematics, engineering and the physical sciences. Topics include sources of errors in digital computers, fixed point iteration, interpolation and polynomial approximation, numerical differentiation and integration, direct and iterative methods for solving linear systems, and iterative methods for nonlinear systems. Numerical experiments will be conducted using FORTRAN, C, or another appropriate high-level language.

Prerequisites

MATH 270 and CSC 121 or permission of instructor.

Credits

1 course

Course Description

Analysis of algorithms frequently used in mathematics, engineering and the physical sciences. Topics include sources of errors in digital computers, fixed point iteration, interpolation and polynomial approximation, numerical differentiation and integration, direct and iterative methods for solving linear systems, and iterative methods for nonlinear systems. Numerical experiments will be conducted using FORTRAN, C, or another appropriate high-level language. This course will help students develop the presentation of logical arguments and refutation; the ability to distinguish and identify important substantive arguments; the ability to skillfully analyze, evaluate, and integrate supporting material; the selection and implementation of effective presentation style; the ability to adapt the manner of delivery to specific audiences and situations; the demonstration of critical listening skills; the demonstration of effective and reflective listening; and the knowledge of the ethical obligations of speakers, discussants, and listeners.

Prerequisites

MATH 270 and CSC 121聽or permission of instructor.

Credits

1 course

Course Description

The structure of groups, group homomorphisms and selected topics from other algebraic structures, such as rings, fields and modules.

Prerequisites

MATH 270.

Credits

1 course

Course Description

Divisibility and factorization of integers, linear and quadratic congruences. Selected topics from diophantine equations, the distribution of primes, number-theoretic functions, the representation of integers and continued fractions.

Prerequisites

MATH 270 or permission of instructor.

Credits

1 course

Course Description

A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Applied Mathematics; G. Special Topics.

Credits

1 course

Course Description

A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Applied Mathematics; G. Special Topics.

Credits

1 course

Course Description

Topics selected from linear and dynamic programming, network analysis, game theory and queueing theory are applied to problems in production, transportation, resource allocation, scheduling and competition.

Prerequisites

MATH 270.

Credits

1 course

Course Description

This advanced probability course integrates rigorous mathematical theory with practical applications. Students will explore combinatorial analysis, conditional probability, discrete and continuous univariate and multivariate distributions, random variables, moments, covariance, independence, order statistics, and the Central Limit Theorem. The course emphasizes developing quantitative tools for risk assessment applicable across various fields.

Prerequisites

MATH 152

Credits

1 course

Course Description

The seminar will include the topics of multivariate distributions, order statistics, the law of large numbers, basic insurance policies, frequency of loss, frequency distribution, severity, severity distribution, characteristics of an insurable risk, measurement of risk, economics risk, expected value of loss, loss distribution, premium payment, claim payment distribution, limits on policy benefit (deductible, maximum, benefit limits) and role of actuaries. After studying, students will be able to demonstrate a solid foundation in probability by their ability to solve a variety of basic and advanced actuarial practical problems.

Prerequisites

MATH 441 which may be taken concurrently.

Credits

0.5 course

Course Description

A. Actuarial Mathematics; B. Algebra; C. Analysis; D. Foundations of Mathematics; E. Geometry; F. Probability and Statistics; G. Applied Mathematics; H. Special Topics.