Data Practicum: a Sequence of Courses on AI-First Applied Data Analysis

Barton Poulson, PhD, Professor of Psychological Science

Psychology and Counseling Department, Utah Valley University

barton.poulson@uvu.edu

Revised 2026-11-09

I propose a series of 3 special topics courses that focus on applied data analysis, or data work that is designed to give actionable insights to identifiable stakeholders in support of their practical goals (as opposed to theory-building research that is generally addressed to a field as a whole). Each course would be 1-credit, with instruction delivered online through videos and downloadable materials that I will create. (These materials would be embedded in Canvas but would also be generally available as OER on YouTube and elsewhere with Creative Commons attribution-only licenses, or CC-BY, as I have done with other materials.) I would be available to meet with students for in-person office hours or a group meeting 1 hour per week (Mondays at 2:00 pm) or at other times through Teams.

The courses are designed to help students with “AI-first data analysis,” or data work in which the majority of the technical work is conducted by AI, while the students are responsible for all of the non-coding, durable skills that gives data projects value:

• Exploring goals with stakeholders

• Framing actionable questions

• Engaging with the AI to choose appropriate analyses

• Auditing the analytical process

• Exploring and interpreting results

• Deriving actionable insights

• Communicating recommendations with stakeholders

These courses will use the following tools:

• The Python programming language, which is currently the most common tool in applied data science and the foundational language of AI

• Google Colab, which is an online programming environment that is based on the user-friendly Jupyter Notebooks. Colab has both a free tier for the general public and, for students, 12 months of free access to Google AI Pro. Colab is integrated into Google Drive accounts and greatly facilitates the management of Python environments. In addition, it is accessible from any web-connected device, such as Chromebooks, university computers, and even mobile devices like tablets and smartphones, which promotes equal access with few resource restrictions. Colab also has excellent capabilities for sharing work with collaborators, instructors, and stakeholders.

• Google Gemini AI, a generative AI model that is built into Colab (and many other Google services). Gemini can create notebook sections and write code in response to prompts from students, as well as collaborate in the interpretation and application of results.

• Qualtrics, a proprietary online form/survey application that is used by organizations globally. UVU students have free access while enrolled.

• Google Forms is an online form/survey application that has the major advantages of (a) being built into Google Drive; and (b) being free, so it is affordable by students and stakeholders when they no longer have access to paid Qualtrics accounts.

• Integrated Google tools such as Google Drive, Google Sheets, and Google Slides, all of which are free and familiar to students and stakeholders of all kinds. 

(Note: No proprietary or sensitive data will be used in the first course in this sequence. Instead, all datasets used will be either sample datasets that are included with Python or simulated datasets that I create to illustrate topics like data cleaning, missing data, or joining datasets.)

Data Practicum 1: Actionable Insights with AI (to be offered spring of 2027, potentially again in fall of 2027)

This course introduces an end-to-end, AI-assisted workflow for turning raw data into clear, actionable recommendations. Designed as a practical bridge from introductory statistics, students use Google Colab, Python, and Gemini to formulate stakeholder questions, design surveys in Google Forms and Qualtrics, clean and score psychometric data, and run foundational analyses. To safeguard privacy and establish ethical habits early, all coursework uses simulated datasets and standard open-source libraries rather than real client data. Emphasis is placed on practical data ethics – including FERPA, HIPAA, and client privacy – as well as auditing AI decisions, documenting analysis pipelines with verification notes, clearly communicating insights and limitations, estimating return-on-investment (ROI), and preparing presentation-ready reports and visualizations.

Weekly schedule

Week 1: Intro to applied analysis

Applied analysis vs theory-building research. Set up Colab and Gemini. Demonstrate a quick, end-to-end project on simulated data.

Week 2: Actionable insights, ROI, and ethics

Asking questions that can be answered with data. Actionable insights and ROI. The client contract. Ethics. Intuitive priors as decision aids.

Week 3: Python, Colab, and simulated data

Python and Colab basics. File structure, comments, data dictionaries. Privacy and AI: HIPAA, FERPA, CPRA, GDPR, etc. Methods for verifying AI output.

Week 4: Google Forms and Qualitrics

Designing forms in Google Forms and Qualtrics. Honoring the “social contract” in research. Designing forms that allow flexibility but are easy to analyze.

Week 5: Importing "tidy data"

Defining “tidy data.” Importing and tidying from Qualtrics and Google Forms with Python code. Naming variables. Simple version control.

Week 6: Data cleaning

Cleaning data with reproducible Python code. Data types, missing values, duplicates, outliers and unusual values. Documenting work.

Week 7: Data coding and scoring

Scoring. Reverse-scored items, scale scores, scale reliability (alpha, etc.), recoding categories, creating scale scores.

Week 8: Graphs for insight

Graphs as analyst tools for exploratory data analysis (EDA) as opposed to graphs for presentation. Univariate, bivariate, multivariate graphs for insight.

Week 9: Estimating means and effect sizes

Estimating means, percentages, ratios, and effect sizes with confidence intervals. Emphasis on justifiable interpretations for frequentist methods.

Week 10: Comparing groups with t-tests and ANOVA

Group comparisons with t-tests and ANOVA. Checking the AI's choices, such as tails, equal variances, and which test it picked.

Week 11: Correlation and regression

Correlations, correlation matrices, and simple versions of multiple regression. Begin work on a semester mini-project.

Week 12: Presenting results

Best practices for presenting results. Lead with actionable insights, put labels and todo items on each graph. Address limits, include appendix of analyses.

Week 13: Documenting and archiving work

Documenting. Random seeds, package versions, AI-use statements, methods to save and share data while preserving privacy and facilitating future work.

Week 14: Overview of advanced topics

An overview of topics for Data Practicum 2 and 3, and other resources. Address "what is it, when would I need it, what do I ask the AI, what do I do?"

Week 15: Project presentations

Mini-project presentations.

Data Practicum 2: Open Data & Decision Support (to be offered fall of 2027)

Building on foundational AI-assisted workflows, this course engages the messy reality of open and public data to solve real community and organizational problems. Students locate, evaluate, and merge open datasets such as Google Trends and municipal records, addressing issues of data provenance, joining datasets, and missingness before conducting multivariable analyses beyond introductory statistics. Topics include multiple regression, model validation (holdouts and cross-validation), Bayesian perspectives, and an introduction to advanced methods like text mining and multilevel models. The semester culminates in an archived, reproducible analysis and an executive decision-support report delivered directly to a nonprofit stakeholder.

Weekly schedule

Week 1: Intro to open data and applied work

Review the workflow template and Practicum 1 content. Choose project questions.

Week 2: Open data

Finding and judging open data. Provenance, what each variable measures, and a stakeholder profile built from public material.

Week 3: Google Trends

Google Trends in depth. Relative scale, sampling, saved downloads, and the challenges of time series data.

Week 4: Combining data

Combining sources. Merges, keys, and silent row loss. Matching towns across datasets.

Week 5: Planning analyses

Planning analyses before looking at results. Precision and sample size, and a short written analysis plan.

Week 6: Regression models

Multiple regression, including block-entry regression, LASSO, and hierarchical models. Effect sizes.

Week 7: Interpreting models

Exploring multiple potential causes. Confounding, the ecological fallacy, and careful causal language.

Week 8: Validating models

Validation. Holdout, cross-validation, and seeds, taught as "will this hold up on new data?"

Week 9: In-depth comparisons

Group comparisons in depth. Strategies to reduce false positive errors. Post-hoc tests and multiple comparisons.

Week 10: Bayesian methods

The Bayesian lens. Priors and sensitivity to priors, with the costs named and compared with intervals.

Week 11: Data mining overview

Overview of LinkedIn Learning data mining courses that are free to UVU students covering topics such as clustering, classification, or text mining.

Week 12: Research synthesis and meta-analysis

Synthesis and multilevel models. Theoretical and practical methods for dealing with nested data structures.

Week 13: Creating reports

Drafting the stakeholder report. Graphs first.

Week 14: Standards for reporting

Peer review, revision, archiving (OSF or GitHub), and APA-style reporting of methods and AI use.

Week 15: Project presentations

Final presentations. Send the reports to the nonprofits.

Data Practicum 3: Independent Consulting (TBD)

In this capstone experience, students operate as independent data consultants, taking complete ownership of an external client engagement from project scoping to final delivery. Working directly with community partners, businesses, or research labs, students define the analytical problem, navigate data constraints, choose appropriate methods, and produce a polished, professional deliverable tailored to the client's needs. With a focus on professional autonomy, reproducible archiving, and persuasive data storytelling, this course prepares students to confidently lead applied analytical projects in industry, non-profit, or graduate settings.

[Topics and schedule to be determined.]