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This is an archive of our past training offerings. We are looking to include workshops on topics not yet covered here. Is there something not currently on the list? Send us a proposal.
The R for Data Science workshop series is a four part course, designed to take novices in the R language for statistical computing and produce programmers who are competent in finding, displaying, analyzing, and publishing data in R.
Day 4 (functions and packages)
This is the second workshop in a 3-part Stata series offered at the D-Lab that includes: 1) Intro to Stata, 2) Data Analysis in Stata and 3) Stata Programming.
This workshop covers how to organize and analyze qualitative data in MaxQDA. Both researchers new to qualitative methods and experienced qualitative researchers are encouraged to attend.
Join the Qualitative Methods Group (QMG) for a conversation with Dr. Anna Hoffman about working with social media data. Dr. Hoffman will share her experiences using a discourse analysis approach.
Come learn how to turn your data into beautiful webmaps using R and Leaflet, one of the most popular libraries for creating web maps. We’ll cover how to build the entire workflow from raw data to interactive map all within R, so your analysis and mapping are entirely reproducible. We’ll also learn how to save this data as an HTML file so you can display your map on any website.
The R for Data Science workshop series is a four part course, designed to take novices in the R language for statistical computing and produce programmers who are competent in finding, displaying, analyzing, and publishing data in R.
Day 3 (analyzing data)
Git is a powerful tool for keeping track of changes you make to the files in a project. You can use it to synchronize your work across computers, collaborate with others, and even deploy applications to the cloud. In this workshop, we'll learn the basics of understanding and using Git, including working with the popular "social coding" website, GitHub.
This is the first workshop in a 3-part Stata series offered at the D-Lab that includes: 1) Intro to Stata, 2) Data Analysis in Stata and 3) Stata Programming.
This workshop will introduce methods and libraries for mapping spatial data in R.
This workshop will focus on how to quickly set up database-like structures in Google Sheets and then flexibly build various kinds of reports and displays on top of a sound data foundation. I come at this as a manager (of datavis and web teams) rather than as a stats person.
This introductory workshop will introduce attendees to the reason for using a qualitative data analysis package for the coding and analysis process. We will begin by discussing the distinction between coding and analysis, the benefits of using a QDA package, and then view a brief demonstration of a specific program.
An intro to the basics that instructors often assume you know, but that you probably never had good instruction on! After this course, you should be able to more easily start learning to program (e.g., in R or python), follow instructions and documentation online (e.g. StackExchange), and communicate better with your collaborators who are programming. This interactive workshop will cover:
Join the Qualitative Methods Group (QMG) for a conversation with Dr. Irene Bloemraad about in-depth interviewing. Dr. Bloemraad will talk about how interviews can be used to gather data about lived experiences, motivations, feelings, and aspirations for the future, including an examination of the benefits and drawbacks of in-depth interviewing as a research methodology.
Come learn how to build a map with your public health data! We’ll discus the basics of spatial data and talk about useful places to find health data. I’ll show examples of how simple (and complex) maps can improve your health-related research, advocacy and communication. Then we’ll spend the bulk of our time creating an example map together.
The R for Data Science workshop series is a four part course, designed to take novices in the R language for statistical computing and produce programmers who are competent in finding, displaying, analyzing, and publishing data in R.
Day 2 (clean and tidy data)
This introduction will discuss what data the Census collects, how it can be accessed and used, what the content and geographic coverage of the surveys are, and issues or concerns to think about when considering such data for your research needs.
The R for Data Science workshop series is a four part course, designed to take novices in the R language for statistical computing and produce programmers who are competent in finding, displaying, analyzing, and publishing data in R.
Day 1 (basics of R)
This workshop will provide an overview of resources on campus for getting started with geographic data, spatial analysis and mapping. Nancy Thomas will introduce the Geospatial Innovation Facility (GIF) and discuss the GIF workshop program and the GeoLunch speaker series.
This INTENSIVE offers an introduction to a range of Text Analysis approaches, including dictionary methods, classification and machine learning, TF-IDF, clustering, and topic modeling. Tutorials are performed in R with RStudio.
Day 1: Introduction to text analysis approaches. What is automated text analysis? What questions can we ask and answer using these approaches?
The R for Data Science workshop series is a four part course, designed for novices in the R language. Day 4 (functions and packages) Students will be introduced to the principles behind functional programming.
