Data Science (DATA)

An introductory overview of the tools and techniques for extracting knowledge from data. Topics to be covered include Python basics, visualization, sampling, hypothesis testing, estimation, prediction, certainty assessment, and informed decision making. The necessary preparation is three years of high-school mathematics including algebra 2.

An intermediate course combining data, computation, and inferential thinking. Topics to be covered include data collection and cleaning, visualization, statistical inference, predictive modeling, and distributed computing.

This course covers a collection of statistical learning models, algorithms, tools and techniques that can be applied to solve data driven decision making problems. Topics include linear regression, classification, resampling methods, and hands on machine learning applications.

This course extends the ideas of linear models to data sets used in professional settings. Topics includes linear and non-linear regression, logistic regression, discriminant analysis, principle component analysis, cross validation, and related topics. This course will use appropriate statistical software.

This course covers methodologies and algorithms to transform big data into meaningful insights. Topics include Hadoop Ecosystem, Hadoop MapReduce, MongoDB, Spark basics, SparkSQL and hands on real world applications.

A study of data science topics not ordinarily covered in the established courses. Prerequisite: consent of Data Science faculty.

An independent study of a data science topic not covered elsewhere.

Students will design, develop, implement, and effectively communicate an original data science project.

On-the-job supervised experience and study dealing with applications of data science.