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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 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.
Part 3 Topics:
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.
Part 3: Analyzing Data
This workshop will provide an introduction to digital geographic data--or geospatial data--and prepare you to work with these data in any software tool, whether it is desktop GIS like ArcGIS or QGIS or a programming language such as R or Python. We will cover three main topics: 1) referencing locations with geographic coordinates; 2) common file formats for geospatial data; and 3) so
Join the Qualitative Methods Group for a conversation with Dr. Laura Nelson on large-scale qualitative research. Dr. Nelson will share from her experiences using computational data analysis to analyze large works of text.
This workshop will provide an introduction to digital geographic data--or geospatial data--and prepare you to work with these data in any software tool, whether it is desktop GIS like ArcGIS or QGIS or a programming language such as R or Python. We will cover three main topics: 1) referencing locations with geographic coordinates; 2) common file formats for geospatial data; and 3) so
This two-part workshop provides an introduction to both quantitative and qualitative social science methods.
Zotero is an open-source citation manager that allows you to save information while searching and browsing the Web. With a single click, Zotero saves citations (as well as screen shots and documents) and enables you to create customized bibliographies in multiple citation styles, including MLA, Chicago, and APA.
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.
Part 2 Topics:
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.
Part 2: Clean and Tidy Data
Please note: This is a two-part workshop series. The first session will occur Monday, September 19 from 10:00am to 12:00pm. The second session will occur Wednesdsay, September 21 from 10:00am to 12:00pm. If you are registered for the September 19 session, your registration is valid for both days.
This workshop provides an overview of computational text analysis methods and tools. No experience in this area is expected or required. The goal is to provide an orientation for those wishing to go further with text analysis and interpret results of these methods.
Please note: This is a two-part workshop series. The first session will occur Thursday, September 15 from 2:00pm to 4:00pm. The second session will occur Friday, September 16 from 2:00pm to 4:00pm. If you are registered for the September 15 session, your registration is valid for both days.
This workshop will cover how to access databases via RESTful APIs using the Python programming language. Our main example will involve collecting data on news articles through the New York Times Article API.
Join us for a free, practical, hands-on workshop to create more rigorous, open, impactful, and reproducible research. This workshop is co-hosted by the Center for Open Science and D-Lab. Using an example study, we will introduce concrete steps any researcher can take to create more rigorous, impactful, and reproducible research.
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.
Part 1 Topics:
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.
Part 1: Basics of R
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:
This workshop will provide a comprehensive overview of graphics in R, including base graphics and ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data.
Students will learn the basics of cleaning, transforming, and formatting text data. They will build corpora, pull specific elements out of text strings, and pull simple metrics from text data, such as word counts and sentiment polarity. Students will be introcued to document classification. This workshop intoduces the basics of NLTK and gensim.
It is often said that 80% of data analysis is spent on the process of cleaning and preparing the data. This workshop will introduce tools (notably dplyr and tidyr) that makes data wrangling and manipulation much easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful stuff.
