CS 345 - Foundations of Data Science and Analytics

The course provides an overview of Data Science and Analytics, covering a broad selection of interdisciplinary challenges in and methodologies for working with data. Topics covered include data collection, data cleaning, integration, management, modeling, analysis, visualization, prediction and informed decision making. The introductory course integrates across the major disciplines of data science and analytics, including databases, statistics, mathematics, data mining, data visualization, cloud computing, and business intelligence. Cross disciplinary skills, such as communication, presentation, and storytelling with data, are emphasized. Students will acquire a broad breadth of data science principles and techniques through hands-on projects and case studies in a variety of business, engineering, social sciences, or life sciences domains. Themes centered around ethical use of data, protection of data and privacy, and teamwork are woven throughout the fabric of the course. This course does not count as a required 300-level computer science elective. Cross-listed as MA 345

Four credits.

Prerequisite(s): CS111 and one of the following: BU121, PY301, MA330, SO212, BI345, CJ200, or other approved statistics course.