Programming Languages

R Data Wrangling and Manipulation: Parts 1-2

April 7, 2025, 2:00pm
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation 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 tasks.

R Data Visualization

March 31, 2025, 1:00pm
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.

Python Data Visualization: Parts 1-2

April 1, 2025, 10:00am
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.

Python Text Analysis: Parts 1-3

March 17, 2025, 2:00pm
This three-part workshop will prepare participants to move forward with research that uses text analysis, with a special focus on social science applications. We explore fundamental approaches to applying computational methods to text in Python. We cover some of the major packages used in natural language processing, including scikit-learn, NLTK, spaCy, and Gensim.

Qualtrics Fundamentals

March 5, 2025, 3:00pm
Qualtrics is a powerful online tool available to Berkeley community members that can be used for a range of data collection activities. Primarily, Qualtrics is designed to make web surveys easy to write, test, and implement, but the software can be used for data entry, training, quality control, evaluation, market research, pre/post-event feedback, and other uses with some creativity.

Python Machine Learning Fundamentals: Parts 1-2

February 24, 2025, 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.

Python GPT Fundamentals

February 20, 2025, 10:00am
This workshop offers a general introduction to the GPT (Generative Pretrained Transformers) model. No technical background is required. We will explore the transformer architecture upon which GPT models are built, how transformer models encode natural language into embeddings, and how GPT predicts text.

R Data Wrangling and Manipulation: Parts 1-2

February 10, 2025, 10:00am
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation 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 tasks.

MAXQDA Fundamentals

February 25, 2025, 3:00pm
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 Data Visualization: Parts 1-2

February 19, 2025, 10:00am
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.