Data science Q&A

I've been a software developer and data scientist since 1994. I've worked at Google, Amazon, Pinterest, and a host of startups. I would be happy to answer questions, either one-on-one (bring some code to run and debug, or a particular problem) or to the whole group (bring questions), during this session. We'll write down questions for 5 minutes, then start going.

I'll favor questions that more people are interested in. So, this is likely a group discussion, heavily moderated by me. If that happens, then I can answer one-on-one questions outside the session.

NEW ADDITION: Link to submit questions: https://dory.app/events/s0ThwSO90oMfPdQfuNWQ/71fde18f_minnebar17-data-science-q-a/. Go ahead and submit and vote whenever you like. This is an experiment.

NEW ADDITION #2: minnestar slack URL for "data-science-qa": https://app.slack.com/client/T02RC2T5P/C053D3LSNB1

Some possible topics I could help with:

• Code: R and tidyverse (e.g., dplyr), python (but not pandas in-depth), Hive (i.e. SQL for Hadoop clusters), SQL
• How to become a data scientist (I wrote parts 0-3 on medium, start at https://medium.com/@dfrankow/how-to-become-a-data-scientist-part-0-introduction-3fc18c2073ee)
• A/B experiments (design, analysis, process)
• Deep learning for image recognition (gave a talk about a Kaggle contest last Minnebar)
• How data scientists work in groups or companies
• Software development for data scientists
• Statistics at an intermediate level (e.g., basic binomial frequentist confidence intervals)
• Basic cloud concepts (I have worked on cloud platforms like AWS, Heroku, Aptible, but I wouldn't speak in-depth about cloud migration strategies)
• High-level technical understanding of GPT, based on my notes from https://www.atmosera.com/ai/understanding-chatgpt/

Or maybe I'll crash and burn. Or maybe the room will be empty for 50 minutes. Who knows!

• Development
• Other
• Startups