ChatGPT is the most powerful tool yet to enhance data science and engineering productivity.

Here are 6 ways data scientists can use ChatGPT:

1. Generate code snippets:

Do you remember how to parse a JSON file every time?

How about increase the amount of rows that Pandas displays by default?

ChatGPT is extremely useful for quickly creating boilerplate code, prototyping new ideas, and experimenting with different algorithms.

2. Automating data preprocessing and cleaning scripts!

These are the worst parts of a data scientist’s job that can be automated.

For example, if you need to clean a large dataset by removing missing values and outliers, you can write a prompt to ChatGPT to with the details of the task (e.g. which columns to clean, how to handle missing values, etc.)

3. Writing comments and documentation

ChatGPT can be used to generate comments and documentation for codebases. This can be useful for making code more readable and easier to understand, which can improve collaboration and maintainability.

4. Data exploration and visualization scripts

Do you remember how to import matplotlib every time and the exact parameters for a scatterplot? I always forget but ChatGPT doesn’t.

Just describe the schema of your dataset and the visualization that you want!

5. Generate SQL queries

ChatGPT can be used to generate SQL queries for querying relational databases.

Again by just describing the table – ChatGPT can usually get 90% of the way there on queries that involve multiple joins, sub-queries, window function, etc…

Disclaimer though – that last 10% can be tricky.

6. Summarize project reports:

ChatGPT can be used to generate reports summarizing the results of data science projects.

This can be useful for communicating findings and insights to stakeholders in detail for each piece of analysis.

I would not recommend using it for the high-level overview though!

Let me know what you guys think – Is AI + Human workflows the future?