GPTs: Data Analyst – Exploring the Potential of GPT Store (With: Prompt Tips)

With the rapid development of artificial intelligence, GPT Store (referred to as “GPTs”) is becoming a rising star at the forefront of technology. As an integrated multi-functional platform, GPTs offer a variety of generative pre-trained transformers (GPT) modules that not only generate fluent text but also demonstrate astonishing potential in multiple fields including data analysis. GPTBiz is also set to launch a GPTs module named “Data Analyst” within its ChatGPT functionalities. This article will delve into the data analysis applications specific to the GPTs platform, illustrating how it can transform data processing through concrete examples.

Fundamentals of GPTs

When discussing GPTs, it typically refers to the customized versions of ChatGPT available in the GPT Store, i.e., ChatGPT models tailored for specific purposes. GPTs are based on OpenAI’s large language model, ChatGPT, and can be adjusted and optimized for specific needs, such as applications in specific industries or particular task processing. Compared to the general ChatGPT, GPTs are more focused on specific functionalities or fields, making them more effective and precise in handling related tasks.

GPTs can be applied in various scenarios, such as customer service, data analysis, educational tutoring, and technical support. Each GPT is trained and adjusted for its specific use. These models can be purchased by individuals or organizations to enhance work efficiency or improve service quality. By offering targeted optimization, they provide a professional service, although their knowledge and response capabilities might be limited to their training and customization scope. In this way, they offer a flexible and efficient method for users from different fields to leverage advanced natural language processing technology to address specific problems.

Applications in Data Analysis

Automatic Report Generation: The GPTs platform can automatically generate analysis reports based on data sets, reducing repetitive work for data analysts and enhancing efficiency. Market Trend Forecasting: Using the GPTs platform to analyze historical data and predict market trends, supporting business decision-making. Optimization of Data Processing Workflows: The GPTs platform can automatically identify and correct anomalies and errors in data, ensuring data quality.

Tool Use and Technical Guide

Using Python for Data Cleaning: Detailed explanation of how to use Python libraries such as Pandas and NumPy for data preprocessing and cleaning. Application of R in Data Visualization: Exploring effective data visualization with the R language’s ggplot2 package to better understand data sets. Strategic Analysis with SQL: Discussing how to use SQL for data querying and management to support complex data analysis needs.

Exploratory Data Analysis and Model Building

Exploratory Data Analysis: Analyzing data sets to discover patterns, anomalies, and correlations using descriptive statistics and data visualization techniques. Data Model Selection and Validation: Choosing appropriate models for specific data issues and using cross-validation methods to optimize model performance.

Practical Prompt Tips Sharing

Data Collection and Cleaning

  • Key Data Types: List the key data types needed for “[insert project]”.
  • Data Collection Methods: Describe how to collect data from “[insert data source]”.
  • Data Preprocessing and Cleaning: How to preprocess and clean “[insert data type]”.
  • Data Cleaning Method Evaluation: Discuss the most effective data cleaning method for “[insert issue]” and explain why.
  • Assessment and Improvement of Collection and Cleaning Processes: Explore how to assess and improve the effectiveness of the data collection and cleaning processes.

Exploratory Data Analysis

  • Preliminary Exploration: Conduct a preliminary exploratory analysis on “[insert data set]”.
  • Descriptive Statistics: How to use descriptive statistics to understand “[insert data set]”.
  • Data Visualization Strategy: Describe an effective data visualization strategy to better understand “[insert data set]”.
  • Handling Unexpected Trends: Strategies for addressing unexpected trends in data.
  • Pattern and Trend Discovery: Explain how to discover patterns and trends through exploratory data analysis.

Data Modeling and Interpretation

  • Model Selection: Choose an appropriate data model for “[insert data issue]”.
  • Model Training and Evaluation: Explain how to train and evaluate “[insert model]”.
  • Results Interpretation and Business Insights: Describe how to interpret the results of “[insert model]” and translate them into business insights.
  • Model Optimization: How to use cross-validation to optimize model performance, summarizing effective model diagnostics and improvement methods.

Tool Usage

  • Tool Usage Guide: Provide a guide on using “[insert tool]” to address “[insert issue]” (Tools include: Python, R, SQL, Excel, etc., as mentioned below).
  • Data Cleaning and Preprocessing: Describe how to use “[insert tool]” for data cleaning and preprocessing.
  • Data Visualization: Methods for data visualization using “[insert tool]”.
  • Case Study Analysis: Provide a case study of data analysis using “[insert tool]”.


As a powerful platform, GPTs are redefining the boundaries of data analysis. With more industries and organizations exploring and practicing, there is every reason to believe that this advanced technology will unlock more possibilities in the future. We at GPTBiz will also be launching more practical GPTs, and we invite you to join us at GPTBiz to experience the variety of GPTs available.