Please note: This is a three-part workshop series. The first session will occur Monday, October 2 from 1:00-4:00pm. The second session will occur Monday, October 9 from 1:00-4:00pm. The third session will occur Monday, October 23 from 1:00-4:00pm.
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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.
This three-part series will cover the following materials:
Part 1: Introduction
Add data visualization to your communication toolbox without learning to code. Tableau cuts down the time you need to spend creating visualizations through an intuitive graphical user interface. Learn the basics in this hands-on workshop.
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.
In this hands-on workshop participants will create maps of geographic data. Participants will build on their knowledge of R to create static maps with ggplot2. We will then practice adding reference maps, or basemaps, to the static maps using ggmap. Finally, we introduce the leafletR package for creating interactive data maps.
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more. The focus will be on U.S.
Data are the foundations of the social and biological sciences. Familiarizing yourself with a programming language can help you better understand the roles that data play in your field. Learn to develop and train your data skills at the free D-Lab R workshops!
Please note: This is a three-part workshop series. The first session will occur Monday, October 2 from 1:00-4:00pm. The second session will occur Monday, October 9 from 1:00-4:00pm. The third session will occur Monday, October 23 from 1:00-4:00pm.
R FUN!damentals Part 4: For-loops and Functions
Students will learn how to write for-loops and functions in R. You will learn how to personalize functions via control structures such as ‘if’ and ‘else’. These learning objectives will be exemplified through introduction to the construction and graphical representation of Monte Carlo resampling simulation.
This workshop will focus on how to create publication-ready graphs using Stata. We will begin with a short demo of how to create standard graphs in Stata, and continue on to more high-quality formats for greater legibility and a more polished display.
This workshop will provide an overview of popular QDA software (i.e., NVivo, MaxQDA, Dedoose, and Atlas.TI), including a review of similarities and differences. This workshop is ideal for researchers who are new to QDA software. More advanced workshops will be offered throughout the semester.
This workshop will introduce geocoding--the process of determining the geographic location of a place name, zipcode, or address. The first part of this workshop will be an introduction to the process of geocoding and strategies for achieving good results. We will briefly discuss the pros and cons of the various software tools available for geocoding.
Add data visualization to your communication toolbox without learning to code. Tableau cuts down the time you need to spend creating visualizations through an intuitive graphical user interface. Learn the basics in this hands-on workshop.
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets.
R FUN!damentals Part 2: Subsetting and Reshaping
This three-part series will cover the following materials:
Part 1: Introduction (Thursday, September 7)
Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data.
This free webinar will provide an overview demonstration of key functionality of NVivo software for qualitative research. Topics to be covered in the webinar include: