Simplifying Data Analysis and Visualization with Developer Tools and AI

Explore a comprehensive 30-minute conference talk on simplifying data analysis and visualization using developer tools and AI in the era of Large Language Models and generative AI. Learn how non-Python developers can rapidly acquire essential skills and best practices without years of experience. Follow along as the speaker demonstrates the process of identifying an open-source dataset, analyzing it for insights, and visualizing relevant outcomes using only a GitHub account and an OpenAI endpoint. Discover a range of developer tools that streamline the journey, including open datasets, Data Wrangler, Jupyter Notebooks, GitHub Codespaces, GitHub Copilot, and Microsoft LIDA. Gain practical knowledge on creating reproducible environments, leveraging AI-assisted learning, and building intuition for data visualization. By the end of the talk, acquire the skills to progress from discovering a dataset to obtaining visual insights using existing tools enhanced by AI assistance.

Simplifying Data Analysis & Visualization with Developer Tools & AI
Follow along
Introduction - Data Analysis Challenges & Goals
GitHub Codespaces - Reusable environments
Jupyter Notebooks - Make it reproducible
GitHub Copilot - AI-assisted learning
Visual Studio Code - Productivity extensions
Open Datasets - Data Wrangler
Resonsible AI toolkit - Model debugging for fairness
Project LIDA - AI-assisted intuition & visualization
Azure AI Studio - Paradigm shift to LLM Ops
Summary - Questions & Next Steps