This workshop will introduce Pillow, a free and beginner-friendly Python library for processing image files. Python Programming: Processing image collections with Pillow We’ll use seaborn, a popular and free data visualization library written for Python. In this workshop, we will explore different methods and tools for visualizing data using Python. Python Programming: Data Visualization with seaborn Prerequisite: Understanding of basic Python concepts (i.e. We’ll use pandas, a popular and free data analysis library written for Python. 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. Python programming: Spreadsheets and data wrangling with pandas This workshop will take a deeper dive into Python, covering essential topics such as automating tasks using loops, lists, and functions. Python Programming: Loops, lists, and functions This workshop is an exact repeat of the January 26th “Python: Introduction” workshop (see above). Python Programming: Introduction (repeat) Finally, we’ll work together to create your first simple but useful program! We’ll work through installation and setup of some helpful software and introduce basic concepts and terminology used in Python. 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. To find out more about this series, see: See links below to register for individual workshops. Sessions are filling up fast! *Registration required. Registration is by workshop, not for the entire series. After this session, you will be able to create R markdown documents, add formatted text and executable code blocks, and render the R markdown document into a final report. This workshop will cover creating reproducible reports of this type using knitr. One way to make reports more readable, even by people who don’t code, is to alternate human readable text with machine readable code. A working knowledge of R, RStudio, and dplyr would be helpful for you to get the most out of this session.ĭocumenting your analysis in a way that is understandable to a colleague (or yourself 3 months later) can be challenging. After this session, you will be able to create a variety of plot types, alter their aesthetics, and create custom themes. This workshop will show you how to use the ggplot2 package in R. So you’re familiar with R, but want to do more with your plots than the base graphics package. A basic working knowledge of R and RStudio (functions, operators, data types) would be helpful for you to get the most out of this session. 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. This workshop will cover how to manipulate datasets using an R package called dplyr. This workshop is an exact repeat of the February 3rd “R Programming: R Basics” workshop (see above).ĭata is rarely perfect out of the box. This workshop is geared toward programming novices, so no previous experience is required. By the end of this session, you will be able to create variables, use pre-defined functions, understand data types, and load and inspect a dataset using RStudio. This workshop will cover the basics of R programming. Learning how to code can be intimidating, but will save you time and effort in the long run.
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