Programming Languages

R Fundamentals: Parts 1-2 (5pm-8pm)

March 28, 2022, 5:00pm
Evening workshop 5-8pm. This workshop is a two-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

Python Machine Learning Fundamentals: Parts 1-2

June 25, 2024, 9:00am
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. No theory instruction will be provided.

Python Visualization

October 26, 2022, 3:00pm
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 notebook.

R Fundamentals: Parts 1-4

October 25, 2021, 9:00am
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

Python Web Scraping

June 26, 2023, 2:00pm
In this workshop, we cover how to scrape data from the web using Python. Web scraping involves downloading a webpage's source code and sifting through the material to extract desired data.

Python Web Scraping

April 5, 2022, 1:00pm
In this workshop, we cover how to extract data from the web using Python. We focus on two approaches to extracting data from the web: leveraging application programming interfaces (APIs) and web scraping.

R Visualization

May 27, 2022, 9:00am
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.

CANCELED: MaxQDA: Introduction

November 15, 2022, 10:00am
This two-hour introductory workshop will teach you MaxQDA from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the MaxQDA software, upload multiple forms of data then how to use manual and autocode features. We will review some of the additional analytic features including visual, memo and the Questions, Themes and Theories (QTT) tools. We will briefly touch on the MaxQDA Team cloud-based version. Instructors will share recommended resources.

Python Introduction to Artificial Neural Networks

November 17, 2021, 9:00am
This workshop presents a brief history of Artificial Neural Networks (ANNs) and an explanation of the intuition behind them; a step-by-step reconstruction of a very basic ANN, and then how to use the scikit-learn library to implement an ANN for solving a classification problem.

Python Machine Learning Fundamentals: Parts 1-2

October 2, 2023, 2:00pm
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. No theory instruction will be provided.