ChatGPT is an advanced AI tool that Data Analysts worldwide use for their everyday Data Analysis responsibilities. This article will explain how Data Analysts can simplify and speed up the Analyzation processes via ChatGPT.
Can ChatGPT perform data analysis?
Being a powerful AI tool, ChatGPT can assist only in some tasks of Data Analysts like – Analyzing different domains ( such as finance, marketing, etc.), Textual Data, counting words, generating insights, suggestions, and reports on customer data, behavior & sentiment, composing data dictionaries, etc.
Therefore, ChatGPT can analyze only simple text-based Data prompts. Hence, it’s not a complete Data Analysis tool. ChatGPT can only assist in limited tasks for Data Analysts. To process complex Data, Data Analysts still require advanced programming softwares such as SQL, Python, R, etc.
How to use chatgpt for data analysis?
Here are some ways to utilize ChatGPT for Data Analysis.
Text summarization
In Data Analysis, Text Summarization shortens large text pieces to develop a precise summarized text with main points. You can summarize News articles, customer feedback, legal documents, scientific articles, social media posts, etc., to generate a quick overview summary. Follow the below steps for Text Summarization in Data Analysis.
- Select the input text data for Summarization: It can be any text data of your work.
- Insert the text data as an input prompt in the ChatGPT and wait for it to process it.
- Analyze the generated text summaries and ensure they contain the essential insights and info in the initially entered Text.
- Repeat the above three steps for different data and utilize the generated summaries to recognize trends, patterns, and valuable insights.
You can analyze large data sets in a short time with ChatGPT. Kindly understand that the generated summaries’ qualities depend significantly on the pre-trained NLP model & entered data text. Refrain from relying on ChatGPT entirely and evaluate the summaries to ensure that it contains all crucial information of the original textual data.
Sentiment analysis
Sentiment Analysis in Data Analytics is the procedure to classify a text on three emotions: positive, negative, and neutral.
For example, by analyzing its reviews/customer feedback, you can check if your company’s product satisfies or meets your customers’ needs. You can make data-driven strategic decisions after sentiment analysis.
Here are the steps to use ChatGPT for sentiment analysis in Data Analysis.
- Select the Data: Gather the dataset whose sentiment you want to analyze. Ensure that the selected dataset contains the labels which indicate its sentiments.
- Preprocess the input text data: Before the next steps, you must reprocess the input text data for cleaning by performing text cleaning processes.
- ChatGPT model fine-tuning: You must possess knowledge of NLP and ML concepts for this step. Fine-tune means you must train the ChatGPT bot against your input text data and then modify the hyperparameters for performance optimization.
- Predict the sentiment: After fine-tuning, use ChatGPT to analyze the sentiment of new data by entering it in the conversation prompt. Note down the predicted emotional labels of your input text data.
- Evaluate ChatGPT model – To analyze ChatGPT model performance, you can utilize metrics such as recall, accuracy, precision, and F1 score.
Topic modeling
Topic modeling is statistical modeling that requires advanced ML concepts to recognize clusters of similar words in a text.
For example – ChatGPT can assist you in extracting topics of the latest events and other interesting areas from news articles. You can perform Topic modeling of large datasets to extract relevant topics and themes from the processed data.
Here are the steps to use ChatGPT for Topic Modeling in Data Analysis.
- Select the Data: Gather the dataset you want to analyze. Ensure that the selected dataset contains the crucial data and reprocess to exclude irrelevant information.
- ChatGPT model fine-tuning: You must possess knowledge of NLP and ML concepts for this step. Fine-tune means you must train the ChatGPT bot against your input text data and then modify the hyperparameters for performance optimization.
- Extract essential topics via ChatGPT – After fine-tuning, your Text is ready for modeling. You can use an effective method, i.e., Non-negative Matrix Factorization ( or Latent Dirichlet Allocation ), to recognize the standard topics in the input text.
- Evaluate extracted topics – Check the ChatGPT extracted topics using any metrics from perplexity or coherence score to analyze the quality too.
Text classification
Text classification is the procedure of categorizing datasets into organized text groups. Performing Sentiment Analysis comes under Text classification.
Here are the steps to utilize ChatGPT for Text Classification in Data Analysis.
- Select the Data: Gather the dataset you want to categorize/classify into pre-decided categories. This dataset must contain the text data and respective labels pointing toward the text category.
- Preprocess the input text data: Before the next steps, you must reprocess the input text data for cleaning by performing text cleaning processes.
- ChatGPT model fine-tuning: You must possess knowledge of NLP and ML concepts for this step. Fine-tune means you must train the ChatGPT bot against your input text data and then modify the hyperparameters for performance optimization.
- Predict category of new textual data – After fine-tuning ChatGPT, it’s ready to classify the new input text data. Enter the input text prompt, and generate the new class labels.
- Evaluate ChatGPT model – To analyze ChatGPT model performance, you can utilize metrics such as recall, accuracy, precision, and F1 score.
Can ChatGPT analyze data in Excel?
ChatGPT doesn’t have the exact features to analyze and represent data visually like Excel because it’s an AI Bot.
But you can still utilize ChatGPT and Excel together for Data Visualization and Data Analysis tasks. Here’s how you can do that-
- First, Analyze the Text via ChatGPT. Enter the analyzed Text into MS Excel for more detailed analysis. Excel includes various in-built Data visualization tools like charts, graphs, pivot tables, regression analysis, etc. Use any Excel tool to visualize the input data and enter the results into ChatGPT to generate valuable insights for your business.
Can ChatGPT replace data analysts?
By looking at the current ChatGPT version, experts worldwide have predicted that ChatGPT cannot wholly replace Data Analysts because of the following reasons-
- ChatGPT cannot provide answers for numerical data. For example – if you upload your company’s Excel data of sales figures in ChatGPT, this AI language model tool will struggle to generate insights. That means, Data Analysts still require spreadsheets tools like Excel for everyday Data Analysis tasks.
- ChatGPT can assist Data Analysts in performing datasets analysis but still make mistakes. Hence, Data Analysts would have to use their own analytical skills to ensure that generated Text is correct and accurate as per entered data.
- Data Analysts possess various technical and soft skills requiring a sharp problem-solving mind with expertise in data understanding. Even while using ChatGPT, we must pay attention to the importance of requiring human insights and understanding while dealing with complex data.
What can ChatGPT do with data?
You can perform diverse Data Analysis tasks with data by using ChatGPT, which are not limited to the ones we have mentioned below. Understand that accuracy of output ChatGPT results depends on three factors – input data quality, fine-tuning, and training of ChatGPT AI language model.
- Data Reprocessing and cleaning by recognizing and correcting Data entry mistakes, giving a structured form to unstructured textual data, and automating the Data classification, labeling, and sentiment analysis processes.
- ChatGPT can assist as a valuable tool in Descriptive Analytics by generating complex reports highlighting crucial metrics and summarizing patterns in data.
- ChatGPT can act as your companion in developing predictive models and can select suitable ML algorithms as per input data.
- Language translation by converting one language Text data into another.
How to use ChatGPT for data visualization?
ChatGPT cannot generate visuals like graphs because it can only provide answers in text forms. You can use it as a Visualization tool by integrating it with another Data visualization software or tool. To get more accuracy of generated descriptions, fine-tune the ChatGPT model with training against your domain or dataset.
Here are the steps to use ChatGPT for Data Visualization.
- Generate a visual graphic of your data using any data visualization tool.
- Use ChatGPT to extract insights or descriptions from your visuals.
- Note down the extracted insights from ChatGPT and explain them to your business stakeholders.
