1 Introduction to the use of GPT models in Data Analytics

This chapter will provide a brief overview of GPT models, their underlying technology, and how they have advanced the field of Natural Language Processing. The point is not to give the readers encyclopedic knowledge of the technology but a deep enough understanding to demystify it and allow a more critical interpretation of its abilities.

1.1 Key features (or limitations) of Genrative AI models

Before diving deep into the transformative potential of Generative AIs in the realm of Data Analytics, it's imperative to lay a solid foundation. LLMs, with their intricate architecture and vast capabilities, are not mere tools but powerful allies in deciphering complex data narratives. Getting a grasp of their key features (or limitations) will not only enhance your analytical prowess but will ensure tha you have capability to harness their potential both efficiently and responsibly.

While the temptation is strong to jump right into the advanced applications, let's begin by illuminating the fundamental characteristics of Genrative AIs. These features often result in limitations that a user should keep in mind in order to appropriately utilize the model.

1.2 The Role of LLMs in Data Analytics

1.2.1 The complementarity of language models and other data analytics tools

1.2.2 The limitation of GPT models’ ability to automate and streamline data analytics processes

1.3 Getting Started with GPT for Data Analytics

1.3.1 Accessing and using the API and SDK

1.3.2 Examples of programmatic access to ChatGPT

1.3.3 Third-party integrations of ChatGPT

1.3.4 Best practices and tips for successful implementation

1.4 Summary