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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.

In this course students will study discrete and continuous probability distributions including the Binomial, Poisson, Hypergeometric, Exponential, Chi-squared, and Normal distributions. Topics include conditional distributions, confidence intervals, chi-square goodness of fit distribution, tests for independence and randomness, and an application to queuing theory.

In this course students will study discrete and continuous probability distributions including the Binomial, Poisson, Hypergeometric, Exponential, Chi-squared, and Normal distributions. Topics include conditional distributions, confidence intervals, chi-square goodness of fit distribution, tests for independence and randomness, and an application to queuing theory.

Topics for the course include sample spaces and probabilities, discrete distributions (Binomial, Negative Binomial, Geometric, Hypergeometric, Poisson, and Gamma), continuous distributions (Uniform, Normal, Chi-squared), expectation and variance, hypothesis testing, interval estimation and confidence intervals. There will be extensive use of MS Excel and R, a statistical software program. At the end of the course, students should be able make meaningful connections between statistics and other areas of study, including and social sciences.

Topics for the course include sample spaces and probabilities, discrete distributions (Binomial, Negative Binomial, Geometric, Hypergeometric, Poisson, and Gamma), continuous distributions (Uniform, Normal, Chi-squared), expectation and variance, hypothesis testing, interval estimation and confidence intervals. There will be extensive use of MS Excel and R, a statistical software program. At the end of the course, students should be able make meaningful connections between statistics and other areas of study, including and social sciences.