Research

This course focuses on introducing the American Community Survey (ACS) data by U.S. Census Bureau as one of the most important open data sources to support research. The ACS data helps researchers, local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. 

This course consists of two parts. 

Part 1 will provide an introduction of the American Community Survey.

One of the attractive features of Tableau for data visualization is that it can produce maps in addition to common charts and graphs. Although Tableau’s map feature is limited compared to a full-fledged GIS application, it is a useful option to plot geographic data or to show how data elements are spatially distributed. In this workshop, the following mapping topics will be covered.  

In this workshop, you will learn how to use Docker to build a basic, interactive app in Plotly and R.

You will learn how to

• Launch a Docker environment

• Run an app in that environment

Learn to quickly present your data in an interactive visualization, using this popular framework.

In this workshop, you will learn how to build a basic data visualization app using Python.

This short course will introduce students to text analysis with an emphasis on learning the technological basics of Python and Jupyter Notebook. After discussing why to learn text mining and text analysis methods, we will explore how to use Python to analyze word frequencies. We will use Constellate to access our dataset.

Drowning in data? Not sure how to organize and back it up? This hands-on, interactive workshop will share tips for effectively organizing, documenting and storing research data. Participants will walk away with ideas for completing a data management plan, naming and organizing files, and safely storing data. 

All the primary computing systems & services offered by the Center for Research Computing (CRC) are available to researchers who are working from off campus.

In fact, "remote" access workflows are often the same as best practices for using our resources.

Get a schematic overview, along with some practical examples of how to use the following in concert with one another to multiply your computing power:

Learn how to create lightweight, interactive data visualization apps for medium-sized datasets. This will be useful for introducing students and researchers to basic exploratory data analysis. We will begin with a basic introduction to data frames in Python and R using a basic dataset; then we will see how to render that data as an interactive dashboard in Python and R; finally, we will deploy see what deploying your app to a free public web server in Heroku looks like!

U.S. Census Bureau conducts surveys on demographic, economic and population data. In this class, we will introduce Census Data Products; learn how to access Decennial Census of Population and Housing and American Community Survey using Census API. We will also anlyze the data with Python Pandas library. 

Excel is a popular and Powerful tool for data storage and analysis. The openpyxl module allows Python to read and modify Excel files, automate your boring tasks and increase your work efficiency. In this workshop, you will learn:

• How to import Excel workbook
• Change sheet name
• Create / remove sheet 
• Change fonts
• Write values to certain cells
• Automate your boring tasks with code