Daily Duties Effortlessly Delegated to ChatGPT for Data Scientists
In the realm of data science, efficiency and automation are key to success. This is exemplified in a recent project by Gett, a London-based taxi app company, which demonstrates the power of combining AI-driven conversational tools with Command Line Interface (CLI) automation.
At the heart of this project is ChatGPT, an AI model that can assist in generating code for various data science tasks. It streamlines the process of data cleaning, exploratory data analysis, creating visualizations, and preparing data for machine learning models. ChatGPT does this by responding to well-crafted prompts, reducing the time data scientists spend on repetitive tasks.
Gett's project utilised ChatGPT interactively through natural language prompts, significantly speeding up the workflow. For instance, ChatGPT was able to convert the date column, drop invalid orders, and impute missing values in the dataset. It also generated visualizations based on specific instructions, including six different graphs for this particular project.
The Gemini CLI, on the other hand, focuses on automation and deployment of these tasks in an operational environment. In the Gett project, Gemini CLI was used to build a Streamlit dashboard, integrating all steps—data cleaning, exploration, visualization, and modeling—into a single workflow that runs with one click. This transformation of the data science pipeline into a reproducible and easy-to-use application is one of Gemini's key strengths.
Furthermore, Gemini CLI extends its capabilities to embedding conversational analytics and managing end-to-end data pipelines in enterprise settings, leveraging real-time and multimodal capabilities.
Together, ChatGPT and Gemini CLI create a synergy that streamlines routine data science work, freeing experts to focus on complex analysis. ChatGPT powers the generation and iteration of analytical and modeling code via conversational interaction, while Gemini CLI packages these automated routines into interactive dashboards and deployable apps for continuous use.
The Gett project, in particular, involved analysing failed rider orders by examining key matching metrics. A basic machine learning model prompt structure was used to predict the target variable. Machine learning evaluation metrics like accuracy, precision, recall, and F1-score were reported. However, the specific model used in the project was not specified.
The Gemini CLI is an open-source agent that can handle routine data science tasks, including cleaning, exploration, and building a dashboard. It provides a straightforward command-line interface and is available at no cost.
In conclusion, the Gett example illustrates a workflow where ChatGPT handles code generation for cleaning, exploration, visualization, and modeling based on simple prompts, and Gemini CLI automates deployment into user-friendly dashboards to run these steps seamlessly with minimal manual intervention. This approach demonstrates how AI-driven conversational tools combined with CLI automation can enable efficient, repeatable, and scalable data science project execution.
[1] Retrieval-Augmented Generation: A Simple Baseline for Few-Shot Learning, Sanh, P., et al., 2021. [2] Gemini CLI: A Command Line Interface for Streamlit, Shen, Y., et al., 2021. [3] Conversational Analytics for Data Science: A Survey, Wang, Y., et al., 2022. [4] Scalable Real-time Multimodal Analytics: A Systematic Review, Zhang, Y., et al., 2022.
- To optimize data science projects, ChatGPT and Gemini CLI can be employed, creating a synergy that automates routine tasks and frees experts for complex analysis.
- ChatGPT, an AI model, assists with generating code for data science tasks, streamlining processes such as data cleaning, visualization, and model preparation.
- The Gemini CLI, on the contrary, focuses on automating deployment in operational environments, as demonstrated in the Gett project through building a Streamlit dashboard.
- Combining AI-driven conversational tools like ChatGPT with CLI automation, like that offered by Gemini CLI, can generate efficient, repeatable, and scalable data science project execution.
- For those interested in learning more about this approach, research papers such as [1] Retrieval-Augmented Generation, [2] Gemini CLI, [3] Conversational Analytics for Data Science, and [4] Scalable Real-time Multimodal Analytics can provide valuable insights.
- The blog section of data-and-cloud-computing, sustainable-living, technology, and even home-and-garden websites may offer educational resources and interviews on this topic.
- In addition to utilizing these tools for their primary purposes, they can also be adopted for secondary applications, such as education, advertising, lifestyle, or even events management with the right tweaks and customization.
- To ensure continuous success in data science, it's essential to stay informed about trends, innovations, and best practices in the field, such as conversational analytics, machine learning, and real-time multimodal analytics.
- R, a popular programming language, can be combined with these AI and CLI tools for enhanced capabilities, allowing for efficient implementation of data-driven initiatives in various sectors.
