Show Notes: In this episode, Will Bachman talks to Adam Braff, a former McKinsey partner who specializes in data analytics. Adam has been using chat GPT to explore how this powerful tool can be harnessed for data analysis. He explores the implications and potential impact of this innovative approach. The Quest for Analyzing Quantitative Data The ability to analyze quantitative data using generative AI has long been a holy grail for many data scientists. While Chat GPT and other language models have proven their prowess in generating text and even creating visual content. Adam talks about how to  tackle the challenge of applying these tools to analyze large datasets problems and uncover potential solutions. Adam outlines four key aspects of the problem at hand. First, there is a need to upload data into the Chat GPT tool, as the existing training data may not encompass the specific dataset of interest. Second, an intuitive interface is required to facilitate a conversation with the tool, allowing for iterative exploration and analysis. Third, the ability to visualize the data in various formats, such as tables and graphs, is crucial for understanding and validating the results. Lastly, incorporating up-to-date contextual information about the world around us is essential to gain insights into correlations and patterns within the data. Uploading Data: Bridging the Gap To address the challenge of uploading data into Chat GPT, several options have emerged. One approach involves integration with popular spreadsheet tools like Google Sheets and Microsoft Excel. Users can interact with the data by writing formulas and commands directly within the spreadsheet software.  Another option is to paste data directly into Chat GPT, as long as it fits within the context window. This approach allows for a quick overview of the data and initial exploration of its contents. The ability to have a conversation with chat GPT is a significant breakthrough in data analytics. Adam highlights the emergence of third-party plugins that enable users to interact with the tool directly. These plugins, such as "chat with your data" and "chat with G sheet," bring us closer to the goal of conversational data analysis within the chat GPT environment. Additionally, separate startups have leveraged APIs to connect with open AI models like GPT 3.5 and GPT 4. These startups, such as seek.ai and data DM, provide an alternative approach to interact with the data, although they operate outside the chat GPT window. Code Interpreter: The 800-Pound Gorilla Among the various solutions, Chat GPT code interpreter stands out as a powerful tool for data analysis. As an official open AI product, it offers a native and robust interface within Chat GPT. By activating code interpreter, users gain access to a chatbot-like interface where they can upload data, ask questions, and receive answers in real-time. The code interpreter translates user queries into Python code, allowing for complex data manipulations and analyses. For example, if a user wants to analyze the correlation between variables or observe trends over time, code interpreter can aggregate and analyze the data accordingly. While the current interface may require users to refer back to the original spreadsheet for column names and other details, it provides a promising solution for non-technical analysts to engage with data. Unleashing the Potential: A Case Study To illustrate the capabilities of code interpreter, Adam conducted an analysis using three datasets: daily credit card spending on fast food brands, weekly food spending in various categories, and macroeconomic data from the Federal Reserve. The goal was to explore correlations between fast food spending, overall food spending, and economic conditions. By uploading these datasets into code interpreter, Adam engaged in a conversation with the tool, asking questions and receiving insights on trends overtime. The analysis aimed to uncover potential drivers of sp