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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. 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.
Visualization in Excel: In Visualization in Excel, we will cover the fundamentals of visualization in Excel, including a checklist of considerations that should go into every visualization. We will also go through step by step instructions on how to make horizontal bar charts, slope graphs, butterfly charts, the good kind of pie charts, icon arrays, and how to graph confidence intervals.
R Fundamentals Part 2: Subsetting and Reshaping
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter (formerly IPython) notebook. The following plot types will be covered:
This class will cover the basics of Excel, from simple formulas (SUM, COUNTIF) to more complex Excel features like Macros and the Data Analysis ToolPak. By the end of both sections, students will be able to employ Excel skills to open source policy data sets. These skills are transferrable to any sector.
Topics Covered Will Include:
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.
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 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.
This class will cover the basics of Excel, from simple formulas (SUM, COUNTIF) to more complex Excel features like Macros and the Data Analysis ToolPak. By the end of both sections, students will be able to employ Excel skills to open source policy data sets. These skills are transferrable to any sector.
Topics Covered Will Include:
In this workshop we will cover the most common CTA task: supervised classification. Using the Python library scikit-learn, we will implement Logistic Regression and Random Forest methods to perform sentiment analysis. Optional: introduction to word vector representations with Word2Vec.
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:
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.
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.
Join David Harding, Professor of Sociology and Faculty Director of D-Lab at UC Berkeley, for a discussion on how to more successfully apply for qualitative research grants from funders with positivist inclinations. Prof.
This hands on workshop builds on part 1 by introducing the basics of Python's scikit-learn package to implement unsupervised text analysis methods. This workshop will cover a) vectorization and Document Term Matrices, b) weighting (tf-idf), and c) uncovering patterns using topic modeling.
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
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 3 Topics:
