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Prerequisite: Introduction to R for Absolute Beginners or some experience using R. You’ve cleaned and analyzed your data, now learn how to visualize it. Visualizing data is critical for both understanding the meaning and patterns hidden in your data, and communicating it to an audience. This workshop by JHU Data Services serves as an introduction to using R’s data visualization tools and techniques. In this hands-on session, we’ll cover design concepts of data visualization and popular R packages, before diving into creating data visualizations for a prepared dataset using base R and ggplot2. Some prior experience in R is required.    

Registration link: https://jhu.libcal.com/event/8219190  

If you have tabular data that you would like to visualize using a GIS but don’t know how to format it correctly, this class is for you.  Students will be led through hands-on exercises using both table joins as well as a spatial join.   While these exercises are taught using ArcGIS Online, students will receive directions on how to Join data using ArcGIS Pro.  Prior experience will help a student navigate the interface but is not required. 

Registration link: https://jhu.libcal.com/event/8219426  

Prerequisites: Participants should be familiar with basic programming concepts, including variable assignment, data types, function calls, and installing packages or libraries. Introductory experience in Python or R will be especially helpful for this workshop. This beginner-to-intermediate level workshop will introduce you to the pandas library, a popular Python library for data cleaning, data wrangling, and data analysis. Participants in this interactive class will use Jupyter Notebooks software and Python code to import, understand, and prepare a dataset for further analysis or visualization. By the end of this workshop, participants will be able to:  

1. Identify and use the two primary data structures of the pandas library: Series and DataFrame  
2. Implement functions from the pandas library to explore and analyze a dataset, including:  
   • Handling missing data  
   • Filtering and sorting data
   • Grouping data  
3. Calculating basic summary statistics  
4. Find documentation for the pandas library to troubleshoot errors and apply new functions to analyze a dataset   

Registration link: https://jhu.libcal.com/event/8219448

Effective data management can increase the pace of the research process, contribute to the soundness of research results, and meet funding agency requirements by making research data easy to share.  Join JHU Data Services for an overview of best practices including backup procedures, tips on effective file names, data security and access controls, and data documentation/metadata. This seminar is for faculty, postdoctoral researchers and graduate students from all disciplines. This course does not focus on creating or using any particular data collection or analysis tool (e.g. REDCap, SPSS), but discusses data management at a general level.   

Registration link:  https://jhu.libcal.com/event/8219458