Daily Duties Simplified: Data Scientist's Routine Jobs Made Easy With ChatGPT
In the realm of data analysis, efficiency and accessibility are key. Two tools that are making significant strides in this area are ChatGPT and the Gemini CLI. This article explores five routine tasks these tools can handle in a data project, with a focus on data cleaning, exploration, and data visualization.
ChatGPT, a large language model, is revolutionising data workflows by automating tasks that were once labour-intensive. For instance, it can clean and validate datasets by detecting errors, inconsistencies, and outliers using natural language commands. This not only reduces manual effort but also improves data quality.
In the realm of data exploration, ChatGPT aids in identifying patterns, trends, and anomalies through summary generation and guided inquiry. Simple prompting frameworks, such as Jeff Su's DIG method, require little to no coding skills, enabling faster understanding of datasets.
Data visualization is another area where ChatGPT shines. It recommends the most effective chart types tailored to the data characteristics and generates interactive visualizations for better hands-on analysis. This enhances the communication of insights to stakeholders without requiring deep technical expertise in visualization tools.
The benefits of these capabilities were demonstrated in a case study by Gett, a London black taxi app similar to Uber. ChatGPT's large language model facilitated the acceleration of the data workflow from raw data to actionable insights with both automation and interpretability, ultimately supporting more informed decision-making.
The Gemini CLI, an open-source agent, also plays a pivotal role in handling routine data science tasks. It can automate EDA, data cleaning, data visualization, machine learning preparation, and machine learning model application. The Streamlit app built with the Gemini CLI requires user input for the target variable and displays each step in a different tab, making the process transparent and user-friendly.
Nate Rosidi, a data scientist and adjunct professor, has highlighted the potential of these tools. He noted that data scientists spend nearly 60% of their time on cleaning and organizing data. Tools like ChatGPT and the Gemini CLI can significantly reduce this time, boost productivity, and democratise data analysis and visualization.
The Streamlit app, demonstrated using a data project from Gett, analyses failed ride orders. It examines key matching metrics to understand why some customers did not successfully get a car. The app can prepare a dataset for machine learning by encoding categorical variables, scaling numerical features, and returning a clean DataFrame ready for modeling.
The Gemini CLI can be installed using a provided code and is available at no cost. It provides a straightforward command-line interface, making it accessible to data scientists of all levels. With these tools, data projects are becoming more efficient, insightful, and accessible than ever before.
- For efficient data cleaning and validation, SQL can be employed, while ChatGPT automates this process with natural language commands, detecting errors, inconsistencies, and outliers.
- In the pursuit of insights, R and ChatGPT can be harnessed for data exploration, identifying patterns, trends, and anomalies through summary generation and guided inquiry.
- To effectively visualize data, ChatGPT recommends chart types tailored to the data characteristics and generates interactive visualizations.
- The benefits of democratizing data analysis and visualization can be seen in tools like Gemini CLI, which automates EDA, data cleaning, data visualization, machine learning preparation, and application.
- In the educational realm, technology and resources available in the field of education-and-self-development can equip individuals with the skills to utilize ChatGPT and similar AI tools.
- The Gemini CLI's Streamlit app, built with the Gemini CLI, exhibits transparency by displaying each step of the process in different tabs, making it accessible for data scientists of all levels.
- The Streamlit app can prepare a dataset for machine learning by encoding categorical variables, scaling numerical features, and returning a clean DataFrame ready for modeling.
- Interviews with data professionals, such as Nate Rosidi, have emphasized the potential impact of these tools, with ChatGPT and the Gemini CLI poised to reduce the time spent on data organization from nearly 60% to a fraction, boosting productivity.
- In the blogosphere, discussions on data-and-cloud-computing and artificial-intelligence frequently touch upon these tools, promoting their adoption and continuous improvement in data projects for greater efficiency, insights, and accessibility.
