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Instructor: Wendy Martin
Suggested prior knowledge: Algebra
Prerequisites: None
Semester(s) Offered: See course list

Course Description

This comprehensive course bridges the gap between foundational statistical reasoning and practical applications related to business and engineering decision-making. Throughout the course, we’ll explore descriptive statistics, the basics of probability, random variables, probability distributions (both discrete and continuous), and sampling theory. You will become proficient in statistical inference through topics like point and interval estimation, hypothesis testing for one and two samples, and advanced hypotheses concerning means, variances, proportions, counts, ordinal data, and correlations in various research scenarios.

Through hands-on practice, you will learn how to categorize the data that you have (or want to generate), then describe it with numbers and graphs to communicate with your audience. You’ll practice using probability and distributions to understand the underlying nature of your data to make decisions and solve problems in a way that increases the likelihood of a desired outcome. You will learn the steps to create a plan to answer business and engineering questions and reduce risk when making decisions. You’ll study how to determine best- and worst-case scenarios using data. Finally, you’ll acquire data analysis skills to answer business and engineering questions that will help you make informed, data-supported decisions.

Skills and Knowledge Gained 

• Data-Informed Decision Making
• Probability and Statistics
• Problem Solving
• Data Analysis
• Data Visualization
• R Statistical Programming Language

Why should you take this course? 

This course will empower you to make data-driven decisions in business and engineering with confidence.

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