Resources

The Dayton Data Challenge is open to UD undergraduate students with a wide range of experience. You do not need to learn a new programming language or software package to participate. Teams may use the data analytics tools that work best for them.

Preparation Workshops

UD Data Analytics Club will offer workshops to help students prepare for the Dayton Data Challenge. Topics may include:

  • Getting started with R
  • Getting started with Python
  • Data visualization
  • Communicating data insights
  • Responsible use of AI tools

Workshop dates and materials will be posted here as they become available.

Self-Guided Learning Resources

You do not need to learn all of these tools to participate in the Dayton Data Challenge. Choose the tools that best fit your experience, interests, and project. The resources below are provided for students who would like to learn a new tool or refresh their skills.

Excel

Excel can be a useful tool for exploring, summarizing, and visualizing data, especially if it is a tool you already know.

R

R is a free, open-source programming language widely used for statistical analysis, data visualization, and data science.

  • R for Data Science (2e) — A free online book covering data visualization, transformation, importing, and analysis.

Python

Python is a general-purpose programming language with a large collection of tools for data analysis and visualization.

Other Tools

Teams are welcome to use other tools such as Tableau, Power BI, JMP, or other statistical, visualization, programming, and AI tools. Additional resources may be added as they become available.

Support During the Challenge

During the challenge, teams will have access to daily virtual and in-person office hours. Support may be provided by faculty, industry professionals, and advanced students.

Office hours are intended to help teams discuss questions, troubleshoot problems, and receive feedback while developing their projects.