MAT 235 - Introduction to Data Science

4 credit hours - 3 hours of lecture and 2 hours of lab weekly; one term.
This course meets the Mathematics General Education Requirement.

Apply standards and practices for collecting, organizing, managing, and exploring data, combining principles and skills from statistics and computer programming with a goal of using these tools to provide information to decision makers. Topics include causality, single and multivariable data manipulation, data visualization and generation, statistical inference, statistical modeling, and machine learning.

Prerequisite(s): A grade of C or better in any college level math course, or CTP 160 - Python , or permission from the Mathematics Assistant Dean. In addition to these course requirements, students must possess proficiency in basic computing tasks, including file storage and management and online communications.

Course Outcomes:

Identify and describe a method or technique commonly used in data science suitable for a given data-driven problem.
Import, manipulate, and organize large data sets using a programming language.
Develop, fit, and use statistical models to generate predictions.
Produce and interpret numerical summaries and visual representations to describe and understand data.
Formulate statistical claims in terms of null and alternative hypotheses; carry out techniques such as bootstrapping and interpret basic hypothesis tests.
Identify scenarios where a training and testing partition of a data set is appropriate, and partition the data set for predictive modeling (in the context of machine learning).
Identify potential ethical impact for disparate outcomes associated with the use of certain data analysis and machine learning techniques.