Learn how to use Python and/or R programming languages for data analysis (via Zoom) (2024)

This workshop is for the absolute beginner wanting to slowly walk through the process of getting started with R, a programming language commonly used for data analysis. The session will introduce you to the RStudio interface for coding in R. We will work through setting up a project directory, cover key concepts and terminology, and load and inspect a dataset.

This workshop is geared toward programming novices, so no previous experience is required.

This workshop is an exact repeat of the September 20th “R Programming: R Basics” workshop (see above).

Data is rarely perfect out of the box. This workshop will cover how to manipulate datasets using an R package called dplyr. After this session, you will be able to select rows and columns, add new columns, remove missing data and create summary tables of your data.

A basic working knowledge of R and RStudio (e.g., functions, operators, data types) would be helpful for you to get the most out of this session.

So you’re familiar with R, but want to do more with your plots than the base graphics package. This workshop will show you how to use the ggplot2 package in R. After this session, you will be able to create a variety of plot types, alter their aesthetics, and create custom themes.

A working knowledge of R, RStudio, and dplyr would be helpful for you to get the most out of this session.

One way to automate your reports is to create files with human readable text and machine readable code. This workshop will cover creating reproducible reports of this type in RStudio using Quarto. After this session, you will be able to create Quarto documents, add formatted text and executable code blocks, and render the document into a final report.

A working knowledge of R and RStudio would be helpful for you to get the most out of this session.

Documenting your analysis in a way that is understandable to a colleague (or yourself 3 months later) can be challenging. README files are text documents that record your computational environment, methodologies, and more. After this session, you will be able to use Quarto in RStudio to create a README file template.

A working knowledge of R and RStudio, and some experience with a markdown language would be helpful for you to get the most out of this session.

This workshop is for the absolute beginner wanting to slowly walk through the process of getting started with Python, a programming language commonly used for data analysis. We’ll work through installation and setup of some helpful software and introduce basic concepts and terminology used in Python. Finally, we’ll work together to create your first simple but useful program!

This workshop is geared toward programming novices, so no previous experience is required.

This workshop is an exact repeat of the September 17th “Python: Introduction” workshop (see above).

This workshop will take a deeper dive into Python, covering essential topics such as automating tasks using loops, lists, and functions.

Prerequisite: Understanding of basic Python concepts (e.g., variables, data types) is helpful.

Real-world data can be messy.This workshop will cover a range of topics related to organizing and manipulating spreadsheet data for more effective analysis. We’ll use pandas, a popular and free data analysis library written for Python.

Prerequisite: Understanding of basic Python concepts (e.g., functions, operators, data types) is helpful.

In this workshop, we will explore different methods and tools for visualizing data. We’ll use seaborn, a popular and free data visualization library written for Python.

Prerequisite: Understanding of basic Python concepts (e.g., functions, operators, data types) is helpful.