INFO 2950

Introduction to Data Science

Course Description

Course information provided by the 2026-2027 Catalog.

INFO 2950 is an applied introductory course on the foundations of data science, focusing on using data to identify patterns, evaluating the strength and significance of relationships, and generating predictions using data. Topics covered include the core principles of statistical programming (such as data frames, Python/R packages, reproducible workflows, and version control), univariate and multivariate statistical analysis of small and medium-size datasets, regression methods, hypothesis testing, probability models, basic supervised and unsupervised machine learning, data visualization, and network analysis. Students will learn how to use data to make effective arguments in a way that promotes the ethical usage of data. Students who complete the course will be able to produce meaningful, data-driven analyses of real-world problems and will be prepared to begin more advanced work in data-intensive domains.

Prerequisites MATH 1710 or equivalent; CS 1110 or CS 1112 or permission of instructor.

Distribution Requirements (DLG-AG, OPHLS-AG), (SDS-AS), (STA-IL)

Last 4 Terms Offered 2026FA, 2026SU, 2025FA, 2024FA

Learning Outcomes

• Identify, acquire and clean project-relevant data from diverse sources. Analyze data fitness for purpose and evaluate solutions for data quality issues such as missing records.
• Use data ethically, including by applying best practices for data sourcing, documentation, and privacy preservation.
• Create data science projects using reproducible methods, common tools, and standard software libraries.
• Apply methods to transform tabular data through operations such as filtering, aggregation, and pivoting.
• Analyze data-generating processes via simulation.
• Apply exploratory data analysis through visualization, summary statistics and outlier identification. Use exploratory results to generate research hypotheses.
• Identify, measure, and evaluate the significance of patterns in large datasets.
• Select and apply common learning algorithms to make predictions about unobserved data.
• Create compelling, reliable arguments informed by data.

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