cda-gai
computational data analysis, algorithmic prompting
Abstract
This course is aimed at users with no previous experience of programming, but who are nevertheless interested in leveraging Generative AI tools for data analysis. In the first two workshop sessions, you will learn how to navigate the Google Colab interface, explore AI-aided low-code programming. You will gain hands-on experience with Python’s potential for automating tasks and analyzing data with minimal coding effort.
In the remaining sessions, the course dives into AI-assisted data analysis using Python. We will explore new techniques for “chatting with their data” using natural language, including creating and manipulating tables and visualizing data through graphs. By the end of the course, you will have practical knowledge of using low-code Python environments and AI tools to perform data analysis tasks efficiently and effectively.
0. starting basic principles
- divide and conquer
- modularity, independence
- start small, scale up
- minimal viable product (mvp)
- fail early
- reduce complexity, refactor
- each function does one thing
1. python coding environment
- notebooks environment, visualizations
2. implement control, data structures
- list iteration = automation
- for loops, if else basics
- prompt engineering
data analysis
- generate synthetic datasets
- analyze, chat with your data
- descriptive and inferential analyses
- modify matplotlib visualization gallery
putting it all together
- cca using language models, vision models
- vibe coding, low-code programming
- product requirements document (prd)
- learn bayesian programming