We've posted some additional April workshops, check them out!
- Upcoming Workshops -

Introduction to Bash + Git
April 9 | 9:00 AM to 12:00 PM | Register for Zoom Link

An introduction to programming basics in Bash and GitHub that are often assumed, but that you might have never had good instruction on! The first half of this workshop will introduce you to navigating your computer’s filesystem and basic Bash commands...The second half of this workshop will introduce you to Git, a powerful tool for keeping track of changes you make to the files in a project. 

Qualtrics Fundamentals
April 9 | 1:00 PM to 4:00 PM | Register for Zoom Link

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.

R Visualization
April 9 | 1:00 PM to 4:00 PM | Register for Zoom Link

This workshop will provide an introduction to graphics in R with ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data. We will also explore the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups. You will learn how to make histograms, boxplots, scatterplots, lineplots, and heatmaps as well as how to make compound figures. The bonus challenge walks you though how to make geographic maps using the Google Maps API. 

Python Text Analysis Fundamentals: Parts 1-3
April 12, 14, 16 | 9:00 AM to 12:00 PM | Register for Zoom Link

This workshop is one of a three-part series that will prepare participants to move forward with text analysis research, with a special focus on humanities and social science applications. Part 1: Basic Tools and Techniques, Part 2: Unsupervised Approaches
, Part 3: Supervised Methods

R Data Wrangling and Manipulation
April 12 | 1:00 PM to 4:00 PM | Register for Zoom Link

It is often said that 80% of data analysis is spent on the process of cleaning and preparing the data. This R 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.

Python Data Wrangling and Manipulation with Pandas
April 12 | 2:00 PM to 5:00 PM | Register for Zoom Link

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.

HTML/CSS Toolkit for Digital Projects
April 12 | 3:00 PM to 4:30 PM | Register for Zoom Link

If you've tinkered in WordPress, Google Sites, or other web publishing tools, chances are you've wanted more control over the placement and appearance of your content. With a little HTML and CSS under your belt, you'll know how to edit "under the hood" so you can place an image exactly where you want it, customize the formatting of text, or troubleshoot copy & paste issues. By the end of this workshop, interested learners will be well prepared for a deeper dive into the world of web design.

R Advanced Data Wrangling: Parts 1-2
April 13, 15 | 1:00 PM to 4:00 PM | Register for Zoom Link

The Advanced Data Wrangling Workshop aims to help students to learn powerful data wrangling tools and techniques in R to wrangle data with less pain and more fun. The workshop will show how R can make your data wrangling process faster, more reliable, and interpretable. The workshop focuses on introducing new package developments in the tidyverse, particularly dplyr 1.0.0, and it has something new and exciting even for experienced R users. The workshop will first examine how to reshape and manipulate data (Part 1) and discuss how to summarise data using the tidyverse packages (Part 2). 

Python Visualization
April 14 | 2:00 PM to 5:00 PM | Register for Zoom Link

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: line, bar, scatter, boxplot. We'll also learn about styles and customizing plots. Throughout the workshop, we'll discuss the plot types best suited for particular kinds of data. Basic familiarity with the Python programming language is assumed.

R Introduction to Machine Learning Tidymodels: Parts 1-2
April 19, 21 | 1:00 PM to 4:00 PM | Register for Zoom Link

We will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework.

Python Introduction to Machine Learning: Parts 1-2
April 19, 21 | 2:00 PM to 5:00 PM | Register for Zoom Link

This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets.

R Introduction to Deep Learning: Parts 1-2
April 22, 23 | 1:00 PM to 4:00 PM | Register for Zoom Link

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. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.

Python Introduction to Artificial Neutral Networks
April 23 | 9:00 AM to 12:00 PM | Register for Zoom Link

A brief history of ANNs (Artificial Neural Networks) and an explanation of the intuition behind them.  Step-by-step construction of a very basic ANN. Although the code will be written in Python, it will be intuitive enough for programmers of other languages to follow along. Using the popular Python library scikit-learn, an ANN will be implemented on a classification problem. High-level libraries reduce the work for a researcher implementing ANN down to tuning a set of parameters, which will be explained in this part.

To see a calendar view of our upcoming April 2021 Workshops, click here!

- Computational Text Analysis Working Group -

Mining For Conspiracies: A Pipeline For The Discovery Of Conspiracy Theories On Social Media
April 16 | 1:00 PM to 2:00 PM | Register for Zoom Link

Speaker: Tim Tangherlini, a Professor in the Dept of Scandinavian at UC Berkeley.

"In this brief presentation, I explain some of the challenges in developing a largely automated machine learning pipeline for working with social media posts. The goal of our work is to discover the underlying generative narrative frameworks for conversations in social media. Initially developed to make sense of the diffuse discussions about vaccine hesitancy and exemption seeking on parenting blogs, we refined our methods over the past five years to allow us to work with large, noisy social media data sets...In this brief talk, I will explain aspects of the pipeline and present some of the results of our work."

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