INFO 2951 - Introduction to Data Science with R

Spring. 4 credits. Letter grades only.

Prerequisite: One course in core statistics (MATH 1710 or equivalent) and one course in core programming (CS 1110 or CS 1112), or permission of instructor.

B. Soltoff.

This is an applied introductory course for students who wish to harness growing digital and computational resources. The focus of the course is on using data to identify patterns, evaluate the strength and significance of relationships, and generate predictions using data, carrying students through the entire data science workflow from data collection to communication of results. These techniques are implemented using a reproducible workflow, programmatic techniques, and version control software. Students will learn how to use data to make effective arguments, in a way that promotes the ethical usage of data.

Outcome 1: Conduct exploratory data analysis through data wrangling and munging as well as visualizations and summary statistics.

Outcome 2: Identify patterns in data to make predictions or to identify associations between variables.

Outcome 3: Evaluate the strength of patterns using statistical and substantive significance.

Outcome 4: Implement data science workflows using common, reproducible methods and software tools.