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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.

E.g., 19-Dec-18
E.g., 19-Dec-18
March 11, 2016
Author:
Juan Shishido

This four part, interactive workshop series is your complete introduction to the capabilities of the Python language. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to collect data, process unstructured data, analyze tabular data, and automate the entire process.

March 11, 2016
Author:
Zawadi Rucks-Ahidiana

Coding is done, now what? You’ve finished coding your qualitative data and now it’s time to start the “analysis” process. Where do you start? Through this interactive workshop session, qualitative researchers will learn several approaches to transition from coding to analysis. Researchers in all phases of the qualitative research process are welcome to attend.

March 10, 2016
Author:
Juan Shishido

This workshop will introduce students to the SAS system. Participants will gain an understanding of the SAS environment as well as the functionality it provides, which includes data management and analysis. Students will learn the basics of the SAS programming language through examples, loading data sets and using built-in procedures.

March 9, 2016
Author:
Shinhye Choi

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)

March 4, 2016
Author:
Chris Kennedy

This is the third 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.

March 4, 2016
Author:
Kunal Marwaha

This four part, interactive workshop series is your complete introduction to the capabilities of the Python language. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to collect data, process unstructured data, analyze tabular data, and automate the entire process.

March 4, 2016
Author:
Zawadi Rucks-Ahidiana

This workshop covers how to organize and analyze qualitative data in Atlas.TI. Both researchers new to qualitative methods and experienced qualitative researchers are encouraged to attend.

March 3, 2016
Author:
Patty Frontiera, Susan Powell

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 like R or Python.

March 3, 2016 to March 31, 2016

New brown bag series, "How We Did It", a bi-weekly, hour-long presentation on specific topics from quantitative data cleaning to best practices for entering the field for participant observation.

March 3, 2016
Author:
David Eifler

Citation management software can help organize research results and make writing papers easier by quickly creating properly formatted bibliographies and footnotes.

March 2, 2016
Author:
Evan Muzzall

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)

March 2, 2016
Author:
Colleen Kohashi

This workshop provides a discussion of federal regulations overseeing human subjects research, levels of review and application submission guidance.

March 1, 2016
Author:
Michael Sholinbeck

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.

February 29, 2016
Author:
Andrew Bertoli

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)

February 26, 2016
Author:
Orestes "Pat" Hastings

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.

February 26, 2016
Author:
Summer Starling

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.

February 25, 2016

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.

February 24, 2016
Author:
Josh Pepper

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.

February 22, 2016
Author:
Evan Muzzall

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)

February 19, 2016
Author:
Kunal Marwaha

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.

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