# Exploring, Visualizing, and Modeling Big Data with R

*2021-10-26*

# 1 Preface

## 1.1 Summary

Working with **BIG DATA** requires a particular suite of data analytics tools and advanced techniques, such as machine learning (ML). Many of these tools are readily and freely available in R. This full-day session will provide participants with a hands-on training on how to use data analytics tools and machine learning methods available in R to explore, visualize, and model big data.

The first half of our training session will focus on organizing (manipulating and summarizing) and visualizing (both statically and dynamically) big data in R. The second half will involve a series of short lectures on ML techniques (decision trees, random forests, and support vector machines), as well as hands-on demonstrations applying these methods in R. Examples will be drawn from the OECD’s Programme for International Student Assessment (PISA). Participants will get opportunities to work through several hands-on lab sessions throughout the day.

## 1.2 Who we are

**Okan Bulut – University of Alberta**- Associate Professor of educational measurement and psychometrics at the University of Alberta
- 10+ years using the R programming language for data analysis and visualization
- Specialized in the analysis and visualization of big data (mostly from large-scale assessments)
- 6+ years teaching courses and workshops on statistics, psychometrics, and programming with R
- https://github.com/okanbulut and https://sites.ualberta.ca/~bulut/
**E-mail:**bulut@ualberta.ca

**Christopher D. Desjardins – University of Minnesota**- Research Associate at the Research Methodology Consulting Center.
- R user since 2006 and contributer since 2009.
- https://github.com/cddesja and https://cddesja.github.io/
**E-mail:**cdesjard@umn.edu

We also co-authored:

- Two R packages – profileR for profile analysis of multivariate data and hemp for psychometric analysis of assessment data
- A recent book titled Handbook of Educational Measurement and Psychometrics Using R