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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., 21-Sep-19
E.g., 21-Sep-19
April 4, 2019
Author:
Patty Frontiera

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.

April 4, 2019
Author:
Evan Muzzall

R Fundamentals Part 2: Subsetting and Reshaping

April 3, 2019
Author:
Josué Meléndez Rodríguez

This session focuses on explaining the proces of creating, organizing, and applying codes within the context of qualitative research. An overview of qualitative data analysis (QDA) software will be provided, noting general advantages and disadvantages, as well as comparing popular programs. Participants will also be introduced to analysis using QDA software.

April 3, 2019
Author:
Samy Abdel-Ghaffar

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python.

In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

We plan to cover:

April 2, 2019
Author:
Leora Lawton

This workshop will be open to anyone interested in having the guidance, feedback and structure for writing a grant.

April 2, 2019
Author:
Evan Muzzall

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 for free at our R workshops!

April 1, 2019
Author:
Josué Meléndez Rodríguez

This session introduces qualitative research, and is recommended for folks who are new to research in general or to qualitative research in particular. 

The agenda will be as follows:

March 22, 2019
Author:
Evan Muzzall, Chris Kennedy

This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data.

March 22, 2019
Author:
Patty Frontiera

Since 1790, the US Census has been THE source of data about American people, providing valuable insights to social scientists and humanists.  Mapping these data by census geographies adds more value by allowing researchers to explore spatial trends and outliers.  This workshop will introduce three key packages for streamlining census data workflows in R: 

March 20, 2019
Author:
Samy Abdel-Ghaffar

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.

March 19, 2019
Author:
Drew Hart

Raster data are used to represent geographic phenomena that are present and can be measured anywhere in a study area, like elevation, temperature, rainfall, land cover, soil type, etc. These data are a valuable resource for social scientists, planners, and engineers as well as natural scientists. This workshop will introduce basic raster concepts and methods for working with raster data in R.

March 18, 2019
Author:
Evan Muzzall, Chris Kennedy

This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data.

March 18, 2019
Author:
Samy Abdel-Ghaffar

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:

March 15, 2019
Author:
Sean Freeder

This workshop will introduce students to the basics of designing a survey instrument using the Qualtrics platform, such as randomization and survey flow. We will also cover more advanced topics like implementing embedded data and using javascript, as well as tips and tricks on how to use your design to maximize the number of quality responses you get.

March 15, 2019
Author:
Chris Kennedy, Evan Muzzall

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.

March 14, 2019
Author:
Drew Hart

Geospatial data are an important component of social science and humanities data visualization and analysis. The R programming language is a great platform for exploring these data and integrating them into a research project. 

Geospatial Data in R, part 2: Geoprocessing and analysis

March 13, 2019
Author:
Evan Muzzall, Chris Kennedy

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.

March 13, 2019
Author:
Dr. Marla Stuart

Join Dr. Marla Stuart, a BIDS Data Science Fellow, to discuss using R for machine learning with qualitative research codes. See the process and results, learning how these are different from a purely qualitative thematic analysis. There will be abundant time for conversation with the presenter.

March 13, 2019
Author:
Samy Abdel-Ghaffar

Part 2 Topics:

Workshop: Weighting Data
March 12, 2019
Author:
Thomas L. Piazza

This workshop will cover the main types of weighting, to correct for bias in sample data.

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