From Data to Decisions: Finding Patterns with AI
This course is part of From Data To Decision With AI.
This course teaches students how to leverage AI tools to analyze data and identify patterns for better decision-making. Students learn to generate univariate graphics like histograms and box plots, explore conditional means through statistical methods including ANOVA and t-tests, and create interpretive visualizations such as bar charts and dot plots. The course emphasizes using AI not only to perform analyses but also to generate clear, data-driven explanations of results. Through hands-on assignments and practical examples, students develop skills in writing effective prompts for AI tools to streamline the analysis process. By the end of the course, participants will have gained confidence in utilizing AI to enhance both their analytical capabilities and their ability to communicate insights effectively, making this course ideal for beginners looking to improve their data-driven decision-making.
Instructors:
English
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What you'll learn
Effectively create and interpret univariate graphics like histograms and box plots using AI tools
Generate visualizations for categorical data and understand distribution patterns
Master the concept of conditional means and apply statistical methods like ANOVA and t-tests
Analyze conditional variation to identify relationships between variables
Craft effective AI prompts to automate and enhance data analysis processes
Create compelling data visualizations that clearly communicate findings
Skills you'll gain
This course includes:
52 Minutes PreRecorded video
10 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 2 modules in this course
This comprehensive course is designed to teach students how to effectively analyze data and identify patterns using AI tools. In the first module, students learn to visualize and understand single variables through various graphic representations, including histograms, box plots, and categorical variable displays. The second module builds on this foundation by exploring relationships between two variables, focusing on conditional means, variation analysis, and creating effective visualizations to represent these relationships. Throughout both modules, students learn to craft effective AI prompts that generate clear, insightful analyses and visualizations, enabling them to make data-driven decisions with confidence.
Understanding Patterns for Single Variables
Module 1 · 1 Hours to complete
Understanding Patterns for Two Variables
Module 2 · 3 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: From Data To Decision With AI
Instructor
Department Chair, Department of Leadership, Policy, and Organizations
Will Doyle is a professor of public policy and higher education in the department of Leadership, Policy and Organizations at Peabody College of Vanderbilt University. Doyle serves as Editor-in-Chief of Research in Higher Education. His research includes evaluating the impact of higher education policy, the antecedents and outcomes of higher education policy at the state level and the study of political behavior as it affects higher education. Prior to joining the faculty at Vanderbilt, he was Senior Policy Analyst at the National Center for Public Policy and Higher Education. Doyle received a Master’s degree in Political Science and a PhD in Higher Education Administration from Stanford University in 2004. Doyle's recent work has explored the link between geographic opportunity for higher education and its impact on both earnings and civic outcomes. His recent policy-related work has examined the status of college affordability in every sector of higher education in all 50 states.
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Frequently asked questions
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