OS3170 Foundations of Data Analysis with Computational Methods

This course introduces students to the fundamental concepts of data analysis, with an emphasis on developing statistical thinking and practical skills for operational and managerial decision making. Students learn how to summarize and interpret quantitative and qualitative data and communicate results. Topics include foundational probability rules and distributions, sampling theory, and study design. Students learn how to construct and critique basic statistical inferences, including confidence intervals and hypothesis tests. The course also develops students’ ability to evaluate real‑world data analyses, identify sources of error and bias, and recognize issues of confounding and over‑interpretation in modern data contexts. Hands-on data analysis will be done using computational tools that permit automating workflows and building repeatable analytical scripts.

Prerequisite

College algebra. Programming experience at the level of OA2801, CS2020, or equivalent

Lecture Hours

4

Lab Hours

1

Course Learning Outcomes

By the end of this course, students will be able to:

1. Define and operationalize variable types (nominal, ordinal, quantitative) within structured datasets, including appropriate encoding and transformations in a computational environment.
2. Compute, visualize, and interpret descriptive statistics using programmatic workflows to generate scalable graphical and numerical summaries for univariate and multivariate data, emphasizing reproducibility and automation.
3. Implement probability models computationally, including conditional probability and Bayesian updating, and apply them to complex, data-driven decision contexts
4. Construct and analyze sampling distributions through analytical derivations and simulation, and evaluate the implications of the Central Limit Theorem for estimation, uncertainty quantification, and large-sample approximation.
5. Estimate population parameters, including construction of confidence intervals via analytic formulas and resampling procedures, and interpret uncertainty measures in applied settings.
6. Design, execute, and critically evaluate hypothesis tests, including assessment of assumptions, robustness checks, and simulation-based validation of classical testing frameworks.
7. Diagnose and quantify sources of statistical error and potential bias—including sampling variability, sample selection error, and measurement error—using data diagnostics, sensitivity analysis, and computational modeling techniques.
8. Critically evaluate statistical and data-driven claims in professional contexts by examining model assumptions, computational implementation choices, data quality, and reproducibility of results.