This three-part series will cover the following materials:
Part 1: Introduction
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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
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:
This three-part series will cover the following materials:
Part 1: Introduction
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.
R Fundamentals Part 4: Putting it all together
In the final part, we will review data importation, subsetting, and visualization. Students will then be given the majority of time to reproduce a workflow on two different datasets, ask questions, and review the solutions as a group.
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
R Fundamentals Part 3: Data Exploration and Analysis
Students will be introduced to data exploration and analysis in R. You will learn how to summarize data and explore it with histograms, scatterplots, and boxplots. You will also be introduced to coding statistical data analysis via t-tests, analyses of variance, correlation, and linear regression.
Geospatial data are an important component of social science and humanities data visualization and analysis. This workshop will introduce basic methods for working with geospatial data in Python using GeoPandas, a relatively new Python library for working with geospatial data that has matured and stabilized in the last few years.
Qualitative Data Analysis (QDA) software is used to organize and structure data, codes, memos, and other components of a qualitative study.
This workshop is a two-part series for qualitative researchers, new and established, interested in learning about MAXQDA, a QDA software for which D-Lab provides substantive support.
R Fundamentals Part 2: Subsetting and Reshaping
Qualitative Data Analysis (QDA) software is used to organize and structure data, codes, memos, and other components of a qualitative study.
This workshop is a two-part series for qualitative researchers, new and established, interested in learning about MAXQDA, a QDA software for which D-Lab provides substantive support.
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 your research.
Geospatial Data in R, part I: Getting started with spatial data objects
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.
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!
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:
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.
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 our R or Python Fundamentals series), follow instructions and documentation online (e.g., StackExchange), and communicate better with your collaborators who are programming.
Join Nikki Jones, Associate Professor in the Department of African American Studies, to discuss the importance of Black feminist ethnography. You'll learn from her work, which focuses on the experiences of Black women, men, and youth with the criminal justice system, policing, and with various forms of violence.
Qualitative Data Analysis (QDA) software is used to organize and structure data, codes, memos, and other components of a qualitative study.
This workshop is a two-part series for qualitative researchers, new and established, interested in learning about MAXQDA, a QDA software for which D-Lab provides substantive support.
This interactive workshop goes into the nuts and bolts of effective execution of research process. The goal is to lay out best practices in implementing a research project to make the process more seamless, productive and enjoyable. Through experiential learning exercises, participants will have the opportunity to practice some of the concepts in a supportive setting.