Training & Workshops

Python is a popular programming language that is used for data management and analysis, web development, software development, machine learning and artificial intelligence.  Although we will not be going quite that deep in this introduction, this JHU Data Services workshop will provide users with the fundamentals necessary to get started using Python. This workshop is heavily hands-on and will have users feeling comfortable coding and confident enough to leap from beginner to intermediate and beyond in no time. Students should expect to:  instruct the computer to accomplish tasks, use variables to contain content, learn new Python vocabulary, write and run calculations, and conduct overall data analysis/manipulation using Python’s base functionality. 

Registration link: https://jhu.libcal.com/event/7499269 

Prerequisite: Data Visualization in R or moderate experience using R.  Are you interested in exploring and understanding your data? Would you like to communicate your R analysis in an easy to use, interactive way? Shiny is an R package that makes it simple to build and share web applications for the purpose of visualizing and communicating your data interactively. Join JHU Data Services for a workshop introducing the Shiny package in R for developing and sharing interactive data visualizations. In this two-part hands-on session, we’ll cover design concepts of interactive data visualizations and build a Shiny web app from scratch using a prepared dataset. Some prior experience in R is required.   

Registration link: https://jhu.libcal.com/event/7478089  

Do you work with data in spreadsheets? Are you tired of editing spreadsheets cell-by-cell to clean your data? This JHU Data Services workshop will teach you how to clean and standardize your data efficiently and reproducibly using OpenRefine. OpenRefine is a free, open source tool with a graphical user interface (GUI) to clean and organize data – no coding required! This workshop will start with an overview of OpenRefine and what it means to have “clean” data. Instructors will provide a dataset and lead a hands-on tutorial to show common data cleaning steps in OpenRefine. The workshop will focus on cleaning text-based spreadsheets to prepare data for further analysis.

Registration link: https://jhu.libcal.com/event/7499376  

Attendees will learn how to search, find, and share geographic content using Johns Hopkins ArcGIS Online Organization account. This JHU Data Services class will provide the fundamental skills necessary to create, design, and share web maps, as well as use some of the various geoprocessing tools currently offered via the online application. 

Registration link: https://jhu.libcal.com/event/7499381  

Prerequisites: Some programming experience recommended (beginner level, preferably but not limited to R or Python) for the Jupyter Notebooks, RMarkdown and Git/GitHub sessions.  

What is reproducible research and how can I make my research and code reproducible? In this one-day workshop with JHU Data Services, learn how open tools like R Markdown, Jupyter Notebooks, Git and GitHub can help make your research reproducible. Join JHU Data Services for this full-day workshop about concepts and tools for doing reproducible research. This workshop will be split into four separate interactive sessions: “Introduction to Reproducible Research,” “Getting Started with Jupyter Notebooks,” “Getting Started with RMarkdown” and “Version Control: Using Git and GitHub.” Sessions are run independently of each other. You may attend all sessions or only those that fit your interests. 

Workshop schedule:  

9:30-10:30am: Introduction to Reproducible Research (Required)  

A brief introduction to reproducible research. What is reproducible research? Why is it important? This session provides several good and not so good examples of reproducible research. We also provide several tips and useful resources for you to start making your research more reproducible.   

10:45-12:15pm: Getting Started with Jupyter Notebooks (Optional)  

You are already writing comments for your Python code so you may be wondering why you need to document code with a Notebook tool, such as Jupyter Notebooks. Notebook tools help you document and share your work with fellow Python coders, non-coders or non-Python coders. This workshop will teach you the basics of using Jupyter Notebooks and will focus on the features of Jupyter Notebooks that help you create reproducible research and code for technical and non-technical audiences. Many Python users choose to document and share code with Jupyter Notebooks, but this tool can also be used with other programming languages, including Julia and R. You can find examples of Jupyter Notebooks here.  

1:30-3:00pm: Getting Started with RMarkdown (Optional)  

You are already writing comments for your R code so you may be wondering why you need to document code with a Notebook tool, such as RMarkdown. You can find several RMarkdown examples here. Notebook tools are particularly useful when you share your code and analysis with non-coders or non-R coders. RMarkdown can help you create interactive web document with text, code and results of executing that chunk of code all in one place. Many R users choose to document and share code with RMarkdown, but it can be used to document scripts writing in other programming languages, such as Python and SQL.  

3:30-5:00pm: Version Control: Using Git and GitHub (Optional)  

Do you use different file names to track versions of your files? Are you wondering why everyone else is using GitHub? Do you comment out a chunk of code to test out code or debug? Do you want a system that can do version control automatically for you? If you answer yes for any of the questions above, then you should take this session to learn version control with Git and use GitHub as a git-hosting platform. We use a graphic user interface (GUI) tool, GitHub Desktop, to get your started with using Git and GitHub. No knowledge of command lines is needed for this session.  

Register once here for access to all four sessions: https://jhu.libcal.com/event/7499496