Data Analysis with ChatGPT as Copilot

In this video I compare a data project done with and without ChatGPT as a Copilot

Enhancing Data Projects with LLM Copilots: A Comparative Analysis

Four years ago, I embarked on an intriguing project that involved the detailed analysis of my calendar data. The project was executed using traditional programming techniques and methods, and it was, undoubtedly, a rewarding yet time-consuming endeavor that took me three weeks to complete. Recently, I decided to recreate the same project, this time leveraging Python programming language and ChatGPT as a Copilot. Notably, the project completion time drastically decreased to less than a day, showcasing the powerful impact of AI assistance in data project execution.

The code, in both scenarios, performed similar functions:

- Retrieved data from Google Calendar using its API

- Analyzed the data and pulled related emails pertaining to the contacts I met

- Searched within those emails to find phone numbers, Linkedin and Twitter handles

- Consolidated the data into a CSV file

In this video I delve into the key differences between the two approaches and the potential implications for the enterprise world.

Efficiency and Speed

With ChatGPT as a Copilot, the task was completed with incredible speed. The time to complete the project was significantly reduced from three weeks to less than a day. This reduction is a result of the AI's ability to understand instructions, generate high-quality code, and provide suggestions, thereby reducing the time spent on coding, debugging, and testing.

Error Reduction

ChatGPT's capacity to generate code snippets and provide suggestions minimizes the potential for errors that might occur during the coding process. It is capable of accurately interpreting requirements and generating corresponding code, which reduces the likelihood of mistakes and contributes to a more efficient coding process.

Collaborative Learning

ChatGPT, acting as a copilot, presents an opportunity for developers to learn while they code. It offers alternatives and suggestions, prompting the developer to consider different approaches and solutions that they might not have considered otherwise.

Implications for the Enterprise

The adoption of AI, like ChatGPT, in an enterprise setting has immense potential. Not only can it drastically reduce the time to complete projects, but it can also elevate the quality of work and facilitate continuous learning and skill enhancement for developers. This can lead to a significant increase in productivity and efficiency.

Moreover, AI copilots can assist in automating routine tasks, freeing up developers to focus on more complex and strategic aspects of projects. This can lead to innovation and the development of more advanced and sophisticated solutions.

In conclusion, the impact of AI on data projects and its potential for the enterprise is substantial. It can revolutionize the way we approach coding and data analysis, offering more efficient, faster, and more innovative solutions. As demonstrated by the calendar data project, AI copilots like ChatGPT can be powerful tools that enhance and expedite project execution. The future of programming and data analysis is indeed promising with the integration of AI.

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Emad Hasan