3 Ways ChatGPT Can Help You With Data Analytics
ChatGPT is an invaluable tool for data analysts and data scientists. ChatGPT can assist in conducting data transformation, generating insightful visualizations, and data analytics idea generation to make your life easier.
David @ WiseData · July 8, 2023
Data analytics is the process of transforming, modeling, and interpreting data to extract insights, support decision-making, and create value. Data analytics can be applied to various domains, such as business, marketing, finance, health, education, and more. However, data analytics can also be challenging, especially for non-experts who may lack the technical skills or domain knowledge to perform complex analysis tasks.
That’s where ChatGPT comes in. ChatGPT is a powerful natural language processing (NLP) model developed by OpenAI that can generate coherent and fluent text based on a given input. As a data analyst or a data scientist, you can greatly benefit from the abilities of ChatGPT in various tasks, such as:
- Transforming unclean data into clean data
- Generating insightful visualizations
- Generating ideas for data analytics
By incorporating ChatGPT into your workflow, you can save valuable time, streamline complex processes, and enhance your efficiency as a data analyst or a data scientist.
Here are 3 ways to use ChatGPT to help you with data analytics.
Data visualization is the process of presenting data in a graphical or pictorial form to make it easier to understand and communicate. Data visualization can help you discover patterns, trends, outliers, and relationships in your data, as well as convey your findings and insights to others.
ChatGPT can help you create code snippets for data visualizations using natural language prompts. For example, you can input a question or a statement related to your data, and ChatGPT can generate code snippets for suitable chart or graph based on the data. You can also specify the type of visualization you want, such as a bar chart, a pie chart, a scatter plot, etc.
Here is an example of how you can use ChatGPT to create data visualization:
Prompt: "Give me Python code to compare the average interest rate by credit policy in the loan dataset using a bar chart"
Response: "Sure, I can help you with that. I assume you have the loan dataset loaded as a pandas dataframe called df. If not, you can download it from here1. To compare the average interest rate by credit policy, you can use the following Python code:
import pandas as pd
import matplotlib.pyplot as plt
loan_df = pd.read_csv("policy.csv")
# Group the dataframe by credit policy and calculate the mean interest rate
grouped = df.groupby("credit_policy")["int_rate"].mean()
# Plot a bar chart with the grouped data
grouped.plot(kind="bar", title="Average interest rate by credit policy")
As you can see, the average interest rate is lower for customers who meet the credit policy than for those who do not. This makes sense, as the credit policy is a way of screening out risky borrowers who are more likely to default on their loans."
Data transformation is the process of modifying, cleaning, or enriching data to make it more suitable for analysis or modeling. Data transformation can involve tasks such as removing missing values, handling outliers, encoding categorical variables, scaling numerical variables, merging or splitting columns, etc.
ChatGPT can help you perform data transformation tasks using natural language prompts. For example, you can input a question or a statement related to your data transformation goal, and ChatGPT can generate Python code that can execute the task using common libraries such as pandas or scikit-learn.
Here is an example of how you can use ChatGPT to generate code for data transformation:
Prompt: “Encode the purpose column in the loan dataset as dummy variables”
Response: "Here is some Python code that can encode the purpose column in the loan dataset as dummy variables:
import pandas as pd
loan_df = pd.read_csv("loan.csv")
purpose_dummies = pd.get_dummies(loan_df["purpose"], prefix="purpose")
loan_df = pd.concat([loan_df, purpose_dummies], axis=1)
loan_df.drop("purpose", axis=1, inplace=True)
This code will create new columns for each unique value in the purpose column and assign 1 or 0 depending on whether that value is present or not in each row. Then it will concatenate these new columns with the original dataframe and drop the original purpose column."
Data Analytics Idea Generation
Data analytics idea generation is the process of coming up with new and interesting questions or hypotheses to explore or test using data. Data analytics idea generation can help you discover new insights, uncover hidden opportunities, or validate existing assumptions.
ChatGPT can help you generate data analytics ideas using natural language prompts. For example, you can input a question or a statement related to your data or domain, and ChatGPT can generate a list of possible questions or hypotheses that you can investigate using data.
Here are some examples of how you can use ChatGPT to generate data analytics ideas:
Prompt: “What are some interesting questions I can ask about the loan dataset?”
Response: "Here are some possible questions you can ask about the loan dataset:
Which loan purpose has the highest default rate and why?
How does the credit policy affect the interest rate and the loan repayment status?
What are the characteristics of borrowers who are most likely to pay back their loans on time?
How does the loan amount and duration affect the probability of not paying back the loan?
What are the trends and seasonality in the loan demand and supply?"
Prompt: “What are some hypotheses I can test about the relationship between FICO score and interest rate in the loan dataset?”
Response: "Here are some possible hypotheses you can test about the relationship between FICO score and interest rate in the loan dataset:
H0: There is no correlation between FICO score and interest rate
H1: There is a negative correlation between FICO score and interest rate
H2: There is a positive correlation between FICO score and interest rate
H3: The correlation between FICO score and interest rate varies by loan purpose
H4: The correlation between FICO score and interest rate is moderated by credit policy"
What is WiseData?
WiseData is your AI Assistant to help you with data transformation and visualization in Python even further!
- It saves you time and effort by automating the chart creation process, and gain insights faster.
- It allows you to express your data transformation and visualization needs in natural language.
- It allows you to write more concise and readable code. People who are reading code can interpret the visualization easily.
WiseData uses ChatGPT behind the scenes and adds additional optimizations to generate better results for data transformation and visualization.
As you continue to navigate the future of data analytics, embracing tools like ChatGPT and taking advantage of their capabilities will be essential for data analysts and scientists looking to stay ahead of the curve.
We’ve given you a clear perspective of how you, as a data analyst or a data scientist, can use ChatGPT to make your work more efficient while saving time!