Events

This three-hour workshop will cover charts in more detail, review pivot tables, and the widely-used VLOOKUP function. We recommend first taking the introductory workshop Excel Data Analysis: Introduction.

This three-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. The complete Python Fundamentals series has 6 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walkthrough interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. Parts 1-3 are intended for the complete beginner in Python. We will go over the basics of Python in Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas: Part 1: Introduction to Jupyter and Python, Variables Part 2: Data Types and Structures Part 3: Introduction to Pandas After completing parts 1-3, you will be able to do basic operations in Python. You will know how to navigate Jupyter Notebooks, how to work with common data types and structures, methods, and basic operations in Pandas. You will have the minimum requirements to continue to other D-Lab workshops such as Python Data Wrangling or Python Data Visualization.

This two-part workshop provides an introduction to machine learning algorithms using the tidymodels package. It covers what machine learning is, which problems it is most and least equipped to address, and explores the tidymodels framework to fit supervised machine learning models in R. Addressing machine learning problems requires a deep conceptual understanding of the material. While the workshop will cover coding in R, it will also dedicate a significant portion of the time to motivating machine learning techniques. By the end of the workshop, learners should feel prepared to explore machine learning approaches for their own data problems. This workshop does not cover unsupervised machine learning techniques. Prerequisites: Familiarity with R programming and data wrangling is assumed. If you are not familiar with the materials in Data Wrangling and Manipulation in R, we recommend attending that workshop first. In addition, this workshop focuses on how to implement machine-learning approaches. Learners will likely benefit from previous exposure to statistics.