Data Analysis Made Easy with ChatGPT

Are you weary of trawling through heaps of analysis data in search of meaningful insights? With ChatGPT, the rules will soon alter. ChatGPT may reveal hidden patterns and trends in your data that you never imagined were there because of its sophisticated natural language processing skills. In this blog article, we'll look at how exploratory data analysis with ChatGPT can revolutionize your data and change the way you conduct business.

Data Analysis with ChatGPT

For data analysts, ChatGPT can be a useful tool for processing, exploring, communicating, and collaborating on their data-driven ideas. Large volumes of data can be analyzed and processed by ChatGPT fast and effectively. Written inquiries can be interpreted and understood by ChatGPT through its language processing skills, which also allow it to extract pertinent insights from the data. Here are a few benefits that ChatGPT can provide: 

Data analysts can use ChatGPT to study their data, spot trends, and even produce useful data visualizations. The data is clearly outlined in these graphics, which makes it simpler for analysts to spot trends and insights. Data analysts can utilize ChatGPT to explain their findings to non-technical stakeholders. The chatbot can assist data analysts in providing simple explanations of complicated data ideas and insights by using natural language. Data analysts might benefit from ChatGPT's help in coming up with fresh, insightful queries to pose to their data. Analysts can investigate novel lines of inquiry and unearth previously unconsidered hidden insights by using natural language queries. 

Let's look at how chatGPT can make data analysis easy and straightforward. As a data modeler, I want to investigate the data's dictionary and metadata first.
 

Image 1: : Data Dictionary Using ChatGPT, Part 1

Image 2 : Data Dictionary Using ChatGPT, Part 2

ChatGPT gives us thorough details about the data dictionary for each column, including a complete description of each column. The final user will benefit from this guidance on when and how to use the data.

Asking chatGPT about the dataset's number of rows and columns will help you better grasp the overall statistics.
 

Image 2 : Dataset Statistics 

As seen in the image above, chatGPT gives us a precise estimate of the dataset's number of rows and columns. After getting a broad overview of the dataset, let's examine the data's quality:

 Image 3 : Exploratory Data Analysis - Null Value Statistics

Here, we've given the chatGPT an input containing the dataset and requested it to determine the percentage of null values therein in order to determine whether the data can be used for analytics. The dataset does not contain any null values, hence chanGPT responds that the given dataset contains no missing values.

Now, we can observe that the data set's header information is absent. Before we can use the data, the columns must contain meaningful data.
 

Image 4 : Dataset Column Naming Convention

Let's ask chatGPT how it can deliver valuable header data. As you can see, the output of chatGPT is a column header with a description and business-specific naming standards. The technical team's and business users' lives are made easier in terms of using this data.

We now know that the data quality is good. As this will affect the results of the data analysis, let's look for any outliers in the dataset.
 

Image 5 : Detect Outliers in the Dataset

In this case, chatGPT is carrying out an in-depth analysis at the column level to see whether any outliers are there. It's okay if it doesn't exist. If it does, it also offers advice on what kind of outlier is present and how it can affect the entire data analysis procedure.

Let's now look at how to use chatGPT to eliminate those outliers.

Image 7 : Remove Outliers from the dataset using python, Part 1

Image 8 : Remove Outliers from the dataset using python, Part 2

Therefore, for a given sample dataset, ChatGPT offers a thorough Python code that can be used to automatically eliminate the observed outliers. The team may have business analysts who are unfamiliar with Python. Let's see how chatGPT can assist business analysts with their data analysis work.

Image 7 : SQL Query to calculate monthly revenue, Part 1

Image 8 : SQL Query to calculate monthly revenue

In this case, chatGPT offers a default query that the business analyst may utilize to figure out the monthly income for a particular dataset. Let's then ask chatGPT to take on the role of a data analyst and offer further insights for a certain dataset.

 

Image 8 : Step by Step Data Analysis using chatGPT, Part 1  

Image 9 : Step-by-Step Data Analysis using ChatGPT, Part 2

As we can see from the chatGPT's results, it offers us step-by-step advice on various studies and results that may be applied on top of this particular dataset. The execution of each of these tasks using chatGPT is possible for each phase of the overall data analysis process.

Let's ask chatGPT to undertake this data analysis work so that it may use Python to analyze prices for the given dataset:

Image 9 : Price Analysis using python, Part 1

Image 10 : Price Analysis using python, Part 2

Image 11 : Price Analysis using Python, Part 2

For the purpose of doing price analysis on a given dataset, ChatGPT has developed a Python code and sample output. We can draw a judgment about how the prices are changing over time based on the data points at hand from this output.

Conclusion

In this article, we go into great detail on how to use chatGPT for a variety of exploratory data analysis tasks. Additionally, we looked closely at different approaches to carrying out data analysis tasks using Python and SQL. ChatGPT is, in a word, a very useful tool for performing exploratory data analysis tasks while working with massive volumes of data.

Author Bio

Sagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP. 

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