ChatGPT for Data Analysts (Part 1)

OpenAI launched ChatGPT almost a year ago on November 30, 2022. Being a fan of artificial intelligence, I immediately started experimenting with this conversational agent, which is based on the latest GPT (Generative Pretrained Transformer) model. Just like millions of other users, I was quickly impressed by ChatGPT’s ability to understand natural language and respond accurately to given instructions. Yes, I also observed its infamous “hallucinations” (the model’s tendency to confidently generate incorrect responses). But overall, ChatGPT seemed like a handy tool for improving productivity, particularly for tasks like drafting or formatting emails and creating marketing articles. Due to my job, I also spent quite some time identifying use-cases that could make my life as a Data Analyst easier. In this article — and possibly in a series of articles (I’m still figuring out the scope of this project) — I’ll discuss how ChatGPT can be useful for data professionals. But first, let’s understand some key concepts about how ChatGPT works. <h1>GPT for Beginners (I just wrote the simplest explanation on the internet, Trust Me )</h1> GPT stands for Generative Pretrained Transformer, the underlying deep learning model that allows ChatGPT to generate responses to user-specific prompts. At its core, GPT is like an advanced autocomplete system trained on a massive dataset from the internet. If the model frequently sees “the cat sat on the” followed by “mat,” then it’s likely to predict “mat” whenever someone types “the cat sat on the.” <a href="https://medium.com/mlearning-ai/chatgpt-for-data-analysts-part-1-b17dc1416a45">Read More</a>