Potential Workshop Options

• Anonymous or Not: Data Ethics and Practices (discuss current data practices that comply with or break ethical code and how we even define ethics)
• Data in the Cloud: Azure, AWS, GCP, and Me (introduces the concepts of "the cloud" with room to shift more technical with demonstrations)
• Data Storytelling (looking at how data is presented in all mediums)
• Guide to Technical Interviews (exploring the different types of interviews, how to prepare, and maybe do some practice/ demos)
• Let's Talk Visuals: Review of All the Tools (exploring programmatic and non visualization tools like ggplot2, matplot.lib, seaboard or Power BI, excel, tableau)
• Project Portfolios: Showcasing your Skills (we provide a template website where you can upload your projects for display; talk best practices and what to display like notebooks and visuals)
• Statistics in Data Science: Why is it important? (exploration of foundational stats concepts such as distributions, assumptions, and probability)
• The Top Hits: Python, SQL, and R (demo-ing the three most popular data science languages, we can discuss the companies that most popularly use these languages)
• Translating Data (soft skills in talking data to non-technical and technical people alike; practice possible)
• What's my Job Title? (exploring all the different types of analysts/engineers/data scientists roles and how each vary)
• Recommended UArizona Classes (spotlight our favorite/ most useful data science classes, citing the tools, programs, and concepts used)