Abstract: Your boss just asked you to ""whip up"" a chart for a stakeholder review in an hour. You know you made that really slick visual a couple months ago, it would be a perfect fit for your current dataset. However, you can only find the final PNG, not the original notebook with your carefully crafted matplotlib magic. After a quarter hour's search, you give up and start pummeling Stack Overflow, hoping to find that thread that had all the answers last time...
Sound familiar? If you're a Pythonista whose data visualization process could use a makeover, then this talk is for you. We'll identify the elements of an effective data visualization flow and explore how the Altair and Vega-Lite stack can improve your own data visualization practice.
The visualization demonstration will feature data sourced from S&P Global's curated content sets, now available through the Snowflake Cloud Data Platform.
Bio: Rachel House is a Senior Data Scientist on S&P Global's Artificial Intelligence Engineering team. Prior to her tenure at S&P, Rachel served as a software developer in the ad tech industry and as a proposal writer in defense contracting. She has leveraged her dual background in technology and communication to build a portfolio of experience in the design and development of robust, elegant systems as well as the ability to pitch those creations to varied audiences.
