Data Science (DATA)

DATA 100  Introduction to Data Science  Credits: 3  

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.

Goal: Goal: 04- Mathematical/Logical Reasoning  
Fall: All Years  Spring: All Years  
DATA 250  Computational Data Science  Credits: 3  

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.

Pre-Requisite : DATA 100 OR COMP 165 AND MATH 200  
Fall: All Years  
DATA 435  Predictive Analytics & Modeling  Credits: 3  

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.

Pre-Requisite : MATH 202 AND MATH 430  
Fall: Department Discretion  Spring: Department Discretion  
DATA 468  Big Data Analytics  Credits: 3  

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.

Pre-Requisite : MATH 200 AND COMP 368 AND DATA 250 OR COMP 166  
DATA 486  Special Topics in Data Science  Credits: 1-4  

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

Fall: Department Discretion  Spring: Department Discretion  
DATA 494  Independent Study  Credits: 1-3  

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

Fall: Department Discretion  Spring: Department Discretion  
DATA 495  Senior Capstone  Credits: 2  

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

Pre-Requisite : DATA 250 AND COMP 368 and senior status.  
Fall: All Years  Spring: All Years  
DATA 499  Internship in Data Science  Credits: 1-12  

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

Fall: Department Discretion  Spring: Department Discretion  Summer Department Discretion  
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