OS3160 Practical Data Analysis

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 software tools that are widely available in their follow-on organizations.

Prerequisite

College algebra. No prior probability or programming experience.

Lecture Hours

4

Lab Hours

1

Course Learning Outcomes

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

1. Define and distinguish types of variables (nominal, ordinal, quantitative)
2. Compute and interpret descriptive statistics and appropriate graphical summaries for univariate and bivariate data to communicate patterns, trends, and relationships.
3. Apply the rules of probability, including conditional probability and Bayes’ rule, to compute and interpret probabilities in applied decision-making settings.
4. Explain the concept of a sampling distribution and describe the implications of the Central Limit Theorem for estimation and inference.
5. Estimate population parameters, such as means and proportions, calculate and interpret confidence intervals.
6. Conduct and critique hypothesis tests, identifying common pitfalls and misconceptions in classical testing frameworks.
7. Distinguish among sampling error, sample selection error, and measurement error to determine how each affects statistical conclusions
8. Identify potential confounding variables within a study design that may obstruct the determination of causal relationships.
9. Interpret and critically evaluate data-derived claims and data-driven conclusions in professional contexts by identifying potential limitations in study design, data quality, or model interpretation.