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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.
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. 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 is a two-part series for qualitative researchers interested in learning about MAXQDA, a qualitative data analysis (QDA) software program for which D-Lab provides substantive support.
This session introduces the basic features of MAXQDA, including how to add data sources, set up the coding scheme, and apply codes.
This three-part series will cover the following materials:
Part 1: Introduction
R Fundamentals Part 2: Subsetting and Reshaping
This session focuses on teaching how to create, organize, and apply codes for different kinds of qualitative research projects and will introduce qualitative data analysis (QDA) software, noting benefits and providing an overview of the four most popular programs (ATLAS.ti, Dedoose, NVivo, and MAXQDA).
This three-part series will cover the following materials:
Part 1: Introduction
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 provides a brief introduction to qualitative research, including an overview of the general process and review of methodologies. Participants will also develop an understanding of coding within the context of qualitative research.
The presenter will remain available after the workshop to provide consultations on a first-come, first-served basis.
This three-part series will cover the following materials:
Part 1: Introduction
This workshop introduces Artificial Neural Networks (ANNs), a group of popular machine learning algorithms. No prior knowledge is required, though previous experience with other machine learning algorithms would be helpful. The workshop will be divided into three parts:
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.
In this workshop we will cover two main supervised text analysis methods, the dictionary method, and supervised classification. We will use list comprehension to implement the dictionary method, using sentiment analysis as our example.
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.
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.
Crowdsourcing is a method increasingly used in qualitative, quantitative, and mixed-methods research. However, many researchers remain unclear about what this method is, when it may be appropriate to use, and how it could be implemented.
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.
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.
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.
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.
