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This course introduces students to concepts of problem solving using constructs of logic inherent in computer programming languages. Students study the nature of problems, common solution approaches and analysis techniques. Students use a flowchart interpreter to diagram problem solutions. Students learn the basics of computer programming by learning Python 2.7. Both Python scripts and flowcharts enable students to construct solutions to common algorithmic problems. The major emphasis is on teaching the student to identify solutions to a problem and translate them into various forms that will enable the computer to perform some of the steps in a solution of an actual problem instance. These forms include flowcharting tool, viewing generated software code and the basics of debugging the code.

This course introduces students to concepts of problem solving using constructs of logic inherent in computer programming languages. Students study the nature of problems, common solution approaches and analysis techniques. Students use a flowchart interpreter to diagram problem solutions. Students learn the basics of computer programming by learning Python 2.7. Both Python scripts and flowcharts enable students to construct solutions to common algorithmic problems. The major emphasis is on teaching the student to identify solutions to a problem and translate them into various forms that will enable the computer to perform some of the steps in a solution of an actual problem instance. These forms include flowcharting tool, viewing generated software code and the basics of debugging the code.

Students in this class write a video game narrative and program a trailer for that game using object-oriented programming.

Students in this class write a video game narrative and program a trailer for that game using object-oriented programming.

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 R supplemented by some use of MS Excel.

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 R supplemented by some use of MS Excel.