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DTSTART:20241103T020000
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DESCRIPTION:A hands-on 2-hour workshop introducing seaborn — Python's most
  approachable statistical graphics library — alongside an AI coding assis
 tant workflow. Participants learn just enough seaborn to direct and verif
 y an AI (Gemini in Colab\, ChatGPT\, Claude\, or any tool they already us
 e) as it drafts charts. All examples use the clean datasets that ship ins
 ide seaborn (penguins\, tips\, flights\, titanic)\, so there are no files
  to upload. In this 2-hour in-person session\, you'll learn how to: Use s
 eaborn for the three families of plots — relational (scatterplot\, linepl
 ot)\, distribution (histplot\, boxplot)\, and categorical (barplot\, coun
 tplot) Layer multiple variables into one figure with semantic mappings (h
 ue\, size\, style\, col) — seaborn's grammar-of-graphics approach to mapp
 ing data columns to visual properties Apply five core visualization princ
 iples (chart choice\, reducing clutter\, intentional color\, labeling\, a
 voiding misleading charts) Work with an AI coding agent (Gemini in Colab)
  using the describe → generate → run → read-the-error → iterate loop Writ
 e effective visualization prompts (dataset & columns → goal → constraints
  → chart type) Recognize and catch the common failure modes of AI-generat
 ed charts (hallucinated functions\, wrong chart types\, misleading scales
 \, altered data) Apply the full workflow to your own data or research que
 stion in the open-work section What to know about this session: This trai
 ning is hands-on\; come ready to code alongside the instructor. No prior 
 plotting experience is required — if you're comfortable with basic Python
  and familiar with pandas DataFrames\, you're ready. We use Google Colab 
 (which has Gemini built in — no separate AI account or API key needed). D
 etails about technical setup will be emailed to registrants a few days be
 fore the course begins. Please note that registration is required for thi
 s event.\n\nAdmission:\nFree\n\nDetails URL:\nhttps://medicine.yale.edu/e
 vent/using-ai-and-python-for-data-visualization/\n
DTEND;TZID=America/New_York:20260827T120000
DTSTAMP:20260823T054440Z
DTSTART;TZID=America/New_York:20260827T100000
SEQUENCE:0
STATUS:Confirmed
SUMMARY:Using AI & Python for Data Visualization
UID:00a7d3d1-19d5-4118-8b0a-20edc57b7b28
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