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Quantitative UX Research Course

R Programming for UX Research

Zero Risk Enrollment: Receive a full refund through the end of the first class day.
Course features

  • Class Size: 15 Students

  • Dates TBD
  • 5-7pm EDT
  • 6 weeks
  • Format: Live Online via Google Meet

Course Description

Modern product and UX teams generate large volumes of data, but extracting meaningful insights from that data requires the right tools and analytical confidence. R Programming for UX Researchers is designed to help researchers build practical skills using R and RStudio to clean, analyze, and visualize research data in ways that drive clear product decisions.

In this six-week foundations course, you’ll learn how to use R to unpack datasets, uncover patterns, and communicate statistical findings through compelling visualizations. Rather than focusing on advanced statistical theory, the course emphasizes applied data analysis skills researchers actually use in their day-to-day work.

Through guided exercises and realistic research scenarios, you’ll learn how to organize analysis projects, clean messy datasets, conduct core statistical tests, and design visuals that clearly communicate insights to stakeholders. By the end of the course, you’ll feel confident navigating RStudio, writing and understanding core syntax, and translating statistical findings into meaningful product insights.

Course Format: Hands-On Learning in a Realistic Product Scenario

Many courses teach statistics or coding concepts in isolation. Learners may understand theory, but struggle when faced with real research datasets and the pressure to communicate results clearly to stakeholders.

This course takes a different approach. R Programming for UX Researchers uses realistic product and UX research scenarios to help you apply R in practical contexts. Through a mix of live instruction, collaborative labs, and individual assignments, you’ll practice the exact workflows researchers use when preparing data, running analyses, and communicating insights.

Each live weekly session introduces key concepts and techniques, followed by hands-on lab exercises using example research datasets. Students work through real analytical tasks—cleaning messy data, running statistical tests, and building visualizations—while receiving guidance from the instructor.

Between sessions, learners reinforce concepts through take-home exercises and optional office hours for additional support. Over six weeks, activities build progressively toward the ability to independently conduct basic analyses and present results clearly using R.

Students will work primarily in R and RStudio, using commonly used packages for data wrangling, analysis, and visualization.

What new skills will I learn from this course?

By completing this course, you will gain practical data analysis capabilities that many researchers struggle to build on their own.

You will learn how to confidently navigate the RStudio environment, organize projects for reproducibility, and leverage statistical packages that streamline the research workflow. These skills help ensure your analysis is structured, transparent, and easy to share with teammates.

You’ll also develop strong data preparation and wrangling skills, including identifying duplicates, handling missing values, recoding variables, and constructing composite measures such as satisfaction or usability scores. These steps are essential for ensuring the integrity of research insights.

Beyond preparation, you will learn to run and interpret foundational statistical tests used in UX research, including correlations, regression models, t-tests, ANOVAs, and chi-square tests. Importantly, you’ll also learn how to test assumptions and evaluate effect sizes so your conclusions are statistically sound.

Finally, you will develop the ability to translate statistical findings into meaningful visualizations—charts and figures that clearly communicate the “so what” of your research to stakeholders and product teams.

How Learning R Programming Accelerates Your UX Research Career

Many researchers rely on spreadsheets or limited analytics tools to analyze their data. While these tools can work for small projects, they often become restrictive when datasets grow larger or analyses become more complex.

Learning R provides researchers with a powerful, flexible analytical toolkit used across industry and academia. With R skills, you can clean large datasets more efficiently, conduct more rigorous analyses, and automate repetitive tasks that would otherwise take hours.

Equally important, this course focuses on translating analysis into stakeholder-ready insights. You’ll learn how to present statistical findings clearly and confidently, ensuring that your work drives product decisions rather than remaining buried in technical reports.

These capabilities make researchers more versatile, credible, and effective—helping you stand out in research roles that increasingly require both qualitative and quantitative skills.

Who is this course for?

This course is designed for professionals who want to strengthen their data analysis capabilities using R.

It is particularly valuable for:

  • UX Researchers who want to analyze survey or behavioral data more rigorously
  • Product Researchers and Analysts looking to build reproducible analysis workflow
  • Early-career researchers seeking practical statistical tools for industry research
  • Designers or product professionals who work closely with research data and want to better understand analysis
  • Anyone new to R who wants a structured, hands-on introduction to using it for research

No prior experience with R is required. A basic familiarity with research data and statistics is helpful but not necessary.

Instructor TBD

Bio
Instructor bio coming soon!

Learning Outcomes

By course completion, you will confidently:
  • Navigate the RStudio environment and organize research projects using reproducible workflows
  • Clean and prepare research datasets using programmatic data wrangling techniques
  • Conduct foundational statistical analyses including correlations, regressions, t-tests, ANOVAs, and chi-square tests
  • Evaluate statistical assumptions and interpret effect sizes beyond simple significance testing
  • Create professional data visualizations that highlight actionable research insights
  • Communicate statistical findings clearly to product stakeholders and cross-functional teams
Overview

R Programming for UX Research: Course Syllabus

Week 1: Introduction to R and R

Learning Objective: Navigate the R environment and understand the benefits of open-source software.

For our first week, researchers will be taught the basics of the software that we will be using for the course. This includes navigating panels, understanding various packages, importing data, creating project files, and more. Researchers will explore various R-related terminology (e.g. vectors, arrays, etc.) and utilize example data to get a hands-on experience of what R can offer them. 

Lab: Researchers will be able to import data files into R and run the provided syntax to obtain the example data output. 

Week 2: Data Cleaning and Wrangling

Learning Objective: Researchers will be able to prepare data for analysis, focusing on various approaches to cleaning and preparing data.

After learning the basics of R and RStudio, researchers will work on becoming familiar with navigating the data itself. Before we can make impacts through analysis, we first have to ensure that our data is properly cleaned and prepared. Here, we will focus on identifying and removing duplicate data, recoding values, creating composite variables, and similar actions in R. 

Lab:  Researchers will go through their example dataset in order to identify and remedy issues with data quality. Additionally, researchers will create new variables to represent composite scores.

Week 3: Basic Analyses and Assumptions

Learning Objective: Researchers will be able to conduct basic analyses and identify and check key assumptions for statistical testing.

In this section, we will focus on running basic analyses in R. Values like mean, median, and mode help tell the story that the data is providing, but we also will utilize these analyses to test the assumptions of more thorough statistical testing. For example, is our data normally distributed? Do we have extreme outliers? By testing for these assumptions, we can ensure that we are conducting the proper analyses and that our findings will be statistically sound. 

Lab: Researchers will run descriptive statistics on their example dataset and identify any violations of assumptions for statistical testing. 

Week 4: Applied Statistics in R - Part 1

Learning Objective: Researchers will be able to execute core statistical tests in R

Here, researchers will learn to conduct statistical analyses in R. We will focus on correlations and basic regression models. Researchers will understand the importance of p values and effect sizes and develop skills for reporting statistical findings in ‘layman's terms’ for stakeholder interactions and presentations. 

Lab:  Researchers will practice conducting correlation and regression models, and will accurately differentiate between significant and non-significant statistical findings. 

Week 5: Applied Statistics in R - Part 2

Learning Objective: Researchers will be able to execute core statistical tests in R

Building on part 1, researchers will learn how to conduct statistical analyses for group comparisons. Specifically, we will cover t-tests/ANOVAs as well as chi-square tests of independence. Researchers will be able to determine the appropriate tests for UX constructs such as A/B testing.

Lab:  Researchers will practice conducting t-tests, ANOVAs, and chi-square models and create a mock stakeholder report. 

Week 6: Data Visualization

Learning Objective: Create research-specific visuals that bring statistical findings to life.

In our wrap-up session, researchers will learn how to create impactful visuals to aid in their research findings. Discussion will center around what makes visuals helpful and what types of data visualization to avoid in effective presentations. We will also highlight how to effectively translate statistical findings into actionable recommendations.

Lab: Participants will create data visualizations for their findings from weeks 4 and 5, using guidelines from the lecture to create high impact visuals. 

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