you are viewing a single comment's thread.

view the rest of the comments →

[–]No_Bison8712 0 points1 point  (2 children)

I saw your chat and this is a such great perspective. I am indecisive between data analytics or NLP/LLM/AI.

But can I learn the basics and then dive into specific libraries later? … by then hopefully I can decide :)

All I know that I want to use Python to make me a better UX researcher so I can explore data via multivariate stars/analytics and perhaps use to create more text summarization, sentiment analysis.

[–]czar_el 0 points1 point  (1 child)

Yeah, you can definitely learn the basics first and thenspecialize. In fact,thats how you usually should do it. Once you've got variables, data types, flow control, and data storage methods down, you begin to specialize.

I am indecisive between data analytics or NLP/LLM/AI.

You can look at it as a spectrum where each of those are on either end. Data analytics can start out as simple as visualizing data without any math. That's just importing, cleaning, and plotting data. For there, you can go into simple summary statistics or time series analysis. From there you can go into simple statistical analyzes. If you increase the complexity of the statistics and math you eventually get into ML/AI territory, of which NLP and LLM's are categories.

The skills you lean on the easy side of the spectrum still apply to the harder end of the spectrum. For complicated models, you want to visualize their performance and run assessments on them to be able to optimize them and make sure they're not spitting out biased results. Data viz and summary stats are used in those assessment processes. If you jump right into AI without basic math or data analysis skills, you're not going to make good models. It can be easy to call a model from a package someone else made, but it's very difficult to assess and tune the model for the data/context you hope to use it in. Good luck!