Why was the data scientist so kind to the AI bot?
Because it was really self-conscious.
ChatGPT might not have a conscience (yet!), but its super-smart deep-learning capabilities have got the data science world talking over the last couple of months. On the surface, this chatbot built on OpenAI seems to do nearly everything, from writing code to generating insights from data sets. Dig a little deeper, though, and it’s clear ChatGPT can’t replace data scientists just yet, nor will any AI tool for many, many years to come. That’s because data science requires a human skill set that no machine can duplicate.
That said, it’s critical to learn how to use this tool if you haven’t already. While ChatGPT almost certainly won’t take your job—no matter how many times you’ve read this claim on Twitter—it will definitely play a role in your data science career. So there’s no better time than now to start playing around with ChatGPT’s impressive capabilities and discover how it automates tasks like writing simple SQL queries and classifying text.
While this post focuses on ChatGPT, other AI tools will eventually complete data science tasks in a similar way. The main takeaway here is that AI can help data scientists, not hinder them.
How to Get Started With ChatGPT As a Data Scientist
ChatGPT, for now, is completely free and available for anyone to use. Head over to ChatGPT, create an account and verify your credentials via a text message. Then familiarize yourself with the ChatGPT interface. The first thing you’ll notice is how simple it looks. There’s a black menu on the left side that saves your chatbot conversations and lets you start a new one. For now, all you need to do is focus on the search bar at the bottom of the page. This place is where you’ll enter prompts for the chatbot to reply to.
Prompts are the basis of ChatGPT. You enter a phrase or full sentence like you might do in a search engine, to initiate a response. If you type the prompt, “How can ChatGPT help data scientists?” and hit “enter” or click the “send” icon, ChatGPT will give you a reply that’s something like this:
ChatGPT can assist data scientists by providing text-based insights, generating reports and answering questions through its advanced natural language processing capabilities.
Pretty simple, right? It’s time to move on to something more advanced.
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Use ChatGPT to Create SQL Queries for Data Science
Say your chief data officer asks you to write some simple SQL queries, but you have other things to do. ChatGPT can complete this task pretty easily. Take a look at this example. We opened a new chat in ChatGPT and asked the chatbot the following question: “Can you write an SQL query to find countries that begin with the letter A?”
Below is the response:
SELECT country_name
FROM countries_table
WHERE country_name LIKE ‘A%’;
This query selects the country_name column from the countries_table and filters the results to only show countries whose name starts with the letter “A”. The LIKE operator is used with the pattern ‘A%’, which means to match any string that starts with the letter “A”.
All that took around 10 seconds. ChatGPT may struggle with more complicated queries, but the above proves it’s a helpful tool for getting quick jobs done in your future career.
Research Data Science Projects
Data science involves researching the best processes and metrics to use for a project. While you’ll learn all this in a data science program or bootcamp, ChatGPT can provide you with additional information as the data science industry evolves.
Let’s say you get a job as a data scientist in a large call center, and your manager wants to track performance from the latest data. You could ask ChatGPT, “What are the best KPIs to use?”
In a call center, Key Performance Indicators (KPIs) are crucial to track performance and measure success. Some of the best KPIs to use include:
Average Handle Time (AHT) – measures the average time it takes to handle a call
First Call Resolution (FCR) – measures the percentage of calls resolved on the first attempt
Abandonment Rate – measures the percentage of calls that are terminated before being answered
Call Volume – measures the total number of calls received
Customer Satisfaction (CSAT) – measures customer satisfaction with the call experience.
While this is a pretty generic answer—and results vary each time you ask—it can help your research.
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Classify Text for Data Science
What about classifying text? Surely ChatGPT can’t do that? It turns out that it can. We asked the chatbot to guess the sentiment in the following three phrases: I love data science, I hate data science and I’m ambivalent about data science.
ChatGPT guessed all three correctly:
“I love data science.” – Positive sentiment
“I hate data science.” – Negative sentiment
“I’m ambivalent about data science.” – Neutral sentiment
Too easy? What about, “I love data science sometimes, but other times I hate it.”
ChatGPT guessed correctly again:
The sentiment in the phrase “I love data science sometimes, but other times I hate it” is mixed or conflicting. It expresses both positive and negative sentiments toward data science.
Sentiment analysis was never really a manual task anyway, and text classification tools existed long before ChatGPT. However, the time it took for the chatbot to classify text is remarkable.
Final Word About ChatGPT/AI in Data Science
While the focus here is on ChatGPT, you now have a better idea of how to use AI to assist your data science projects and make life a little bit easier. Remember, while ChatGPT is certainly useful, it can’t do data cleaning, data collection, complex data visualization, hardware-intensive tasks and many other data science jobs. Perhaps it’s appropriate to let ChatGPT have the final word:
It’s important to note that ChatGPT is a tool that can greatly assist data scientists, but it is not a replacement for the full suite of skills and knowledge that a human data scientist possesses.
And there you have it.
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