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A project-based introduction to computational thinking and problem solving. Covers a wide range of topics, including data visualization, statistical techniques, simulations of dynamical systems, computational techniques to understand data, using regression to fit models to data, as well as an introduction to some more advanced topics: Monte Carlo simulations, optimization, dynamic programming, image processing, natural language processing, geospatial data analysis and current data science.

A project-based introduction to computational thinking and problem solving. Covers a wide range of topics, including data visualization, statistical techniques, simulations of dynamical systems, computational techniques to understand data, using regression to fit models to data, as well as an introduction to some more advanced topics: Monte Carlo simulations, optimization, dynamic programming, image processing, natural language processing, geospatial data analysis and current data science.

An introduction to solving ordinary differential equations. Applications to various problems are discussed.

An introduction to solving ordinary differential equations. Applications to various problems are discussed.

This course continues the discussion of discrete mathematical structures and algorithms introduced in MAT2440. Topics in the second course include predicate logic, recurrence relations, graphs, trees, digital logic, computational complexity and elementary computability.

This course continues the discussion of discrete mathematical structures and algorithms introduced in MAT2440. Topics in the second course include predicate logic, recurrence relations, graphs, trees, digital logic, computational complexity and elementary computability.

The use of discrete and continuous distributions to construct deterministic and stochastic simulation models. Stochastic simulations may include Markov Processes, M/G/1 Queuing Systems, Monte Carlo Simulation and Analytic Simulation.

The use of discrete and continuous distributions to construct deterministic and stochastic simulation models. Stochastic simulations may include Markov Processes, M/G/1 Queuing Systems, Monte Carlo Simulation and Analytic Simulation.

A 3 credit but 4 hour introductory course. Topics include sample space, expectation and variance, binomial, Poisson, normal, student and chi-square distributions, confidence interval, hypothesis testing, correlation and regression. Students do a group project comparing 2 numeric variables and present at the end of the semester. The extra classroom hour is designed to facilitate a hands-on feel to the course including a heavy use of MS Excel and/or R.

A 3 credit but 4 hour introductory course. Topics include sample space, expectation and variance, binomial, Poisson, normal, student and chi-square distributions, confidence interval, hypothesis testing, correlation and regression. Students do a group project comparing 2 numeric variables and present at the end of the semester. The extra classroom hour is designed to facilitate a hands-on feel to the course including a heavy use of MS Excel and/or R.