The CSSR offers a wide variety of free training, workshops, and guest lectures to Notre Dame faculty, staff, and students, covering techniques and methods ranging from basic to advanced levels. Current course offering includes:
- Introduction to R
- Advanced R
- Introduction to Stata
- Open Data Kit
- Machine Learning for Social Science
- Machine Learning Classification
- Text Mining
- Building Effective Models
Attend a Course
To learn more about trainings scheduled throughout the semester, please visit: cssr.nd.edu/news-events
In addition to these courses, the CSSR can develop custom workshops and also offers one-on-one or small group training. The CSSR is willing to provide training sessions to research or class groups as guest lectures or outside of class as resources allow.
Please contact the CSSR with such inquiries.
Introduction to R provides a tutorial of the scripting language and what it can offer as an analytical tool.
Advanced R builds upon the topics introduced in the introductory R offering by focusing on specific packages that can be used for a variety of data wrangling and analysis needs.
Introduction to Stata covers the basics including entering, importing and exporting data, managing data sets, performing basic statistical analyses, graphing and .do file programming.
Open Data Kit (ODK) teaches the basics of mobile data collection utilizing ODK. Many research studies continue to rely largely on paper-based data-collection methods. Such methods are often slow and inaccurate when compared to electronic-based methods. The emergence of affordable, powerful, mobile devices (e.g., phones, tablets) and easy-to-use, readily-available open-source software have notably lowered the barriers to electronic-based data-collection. Researchers all over the world can now easily build and deploy high-quality, low-cost mobile electronic-based data collection and information services in days rather than months or years.
The GitHub workshop will guide you step by step to set up GitHub directly on your computer with the official GitHub desktop app, so that you can easily push and pull files without having to use the command line.
Machine Learning for Social Science covers the basics of what machine learning (and data science as a whole) is and how different algorithms in Machine Learning can be applied to cover a wide variety of topics.
Machine Learning Classification will introduce you to numerous classification algorithms such as trees, random forest, SVM, and LDA. We will also discuss Discriminant Analysis as a subset of classification algorithms in detail. In addition to the lecture, we also offer a tutorial featuring R code for comparing trees and random forest one on one.
Text Mining covers the basics of text mining from cleaning your data to basic text analysis. We offer a tutorial containing R code to follow along with the lecture.
Building Effective Models will introduce you to numerous techniques that can be implemented in addition to basic modeling techniques to better your predictive results. These techniques range from creating basic ensembles to resampling techniques to combat imbalanced data.