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In today’s ever-evolving world of data science, it’s very important to stay updated with new and popular tools and technologies in order to survive and thrive in the IT industry. When we talk about popular technologies, ChatGPT is inevitable. Nowadays, with the help of machine learning and artificial intelligence, ChatGPT can accomplish many tasks on its own. When we talk about data science, ChatGPT is revolutionizing the way data scientists interact with machine learning systems.
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It can talk to you and even help you write an entire term paper. But did you know that ChatGPT can do more? It can make a cool logo for a brand and even compose music! ChatGPT has a lot of amazing features and in this article, we will further explore how to use ChatGPT for data science? or how can data scientists use ChatGPT?
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What are ChatGPT prompts?

When we talk about interacting with artificial intelligence systems, a question arises: how? The answer is just like humans, but a little more specific, it's that simple. Whatever you ask ChatGPT in the form of queries, questions, or simple text, they are called ChatGPT prompts. They take input based on the prompt, process it, and provide specific output using different machine learning techniques.

For example:
Prompt: What are the key steps in building a machine learning model?
In the above example, the question is considered as a prompt and depending on the length and complexity, this prompt has different types of answers which ChatGPT provides when specifically asked.

ChatGPT for Data Science

If you are an experienced practitioner or just starting your data science journey, there are many prompts that can help you explore the full potential of ChatGPT which will ultimately increase your productivity in the endless world of data science.

As the field of data science continues to evolve, so do the tools. ChatGPT offers an exciting new frontier for data scientists, enabling them to leverage the power of AI in their daily work. By using these top 15 ChatGPT tips in your data science workflow, you will be able to accelerate your analysis, improve model performance, and ultimately deliver impactful insights.

ChatGPT Tips for Data Scientists

Here, we have listed ChatGPT tips into multiple sections such as Data Analysis, SQL, Machine Learning, etc. So, let’s dive into the 15 ChatGPT tips that data scientists must try and unlock the full potential of this powerful tool in our data science journey.

ChatGPT Tips for Data Analysis
1. Data Generation

If you are a data scientist, then this tip is definitely your lifesaver. Because you have to create dummy data every other day in the process of work, and this tip makes it much easier, you can generate data of different sizes and lengths as needed.

Tip: Assuming you are a data scientist, create a dummy data with 100 rows and 5 columns: [id, name, contact, email, age]

2. Data Exploration

Data Exploration AKAE Exploratory Data Analysis [EDA] is an important part of data science. In order to investigate and analyze a dataset, you have to detect anomalies, discover patterns, and gain insights into the data, and this tip can easily accomplish this task, you just need to use the following tip:

Tip: Assuming you are a data scientist, I have 100 rows and 5 columns of data: [id, name, contact, email, age]. Write R code for data visualization and exploration.

3. Data Visualization

Sometimes, as a data scientist, you may need to represent data in a visually appealing way to show patterns, trends, and insights, and for this, you need to convert the dataset into a visual format. This prompt does the same thing without any effort, you just need to type-

Hint: Assume you are a data scientist and I have 100 rows of data with 5 columns: [id, name, contact, email, age]. Create a matplotlib bar plot of name vs age.

B. ChatGPT prompt for machine learning
4. Train a regression model

Basically, regression models are used to predict continuous values, like predicting house prices based on features like square feet, number of bedrooms, etc. In order to train a regression model, most of the time you need a dataset with input features and corresponding target values. All of this is provided by ChatGPT in seconds, to get it, you have to use the following prompt.

Hint: Assume you are a data scientist, please write me a Python code. I have a dataset with the columns [model, hp, speed]. Please build a machine learning model that predicts speed.

5. Hyperparameter tuning

Hyperparameters are configuration settings that are not learned from the data but are set before the training process. They basically control the behavior of the model and can significantly affect its performance. Whenever you need to find the best combination of hyperparameter values ​​for a machine learning model, you need to perform hyperparameter tuning, which you can do by typing in the following prompt.

Tip: Assuming you are a data scientist, I have a logistic regression model and write Python code to tune the hyperparameters.

6. Explain the model

Whenever you need to explain a machine learning model using a graph with shapes and want to write code for it in any language (python), you can directly head to ChatGPT and ask

Tip: Assuming you are a data scientist, I have trained a LightGBM model. Write a Python code to explain the output using a series of graphs with Shape.

C. ChatGPT prompts SQL
7. SQL format

Data science involves organizing and structuring SQL queries and statements in a standardized and readable way, and for this, it requires proper formatting to enhance code clarity, improve maintainability, and facilitate collaboration between developers. You can do the same using ChatGPT in seconds -

Tip: Assuming you are a data scientist, format the following SQL code and convert all reserved keywords to uppercase.

8. Conversion between DBMS

Most of the time, as a data analyst or data scientist, you might come across a situation where you have to fire queries in multiple DBMS, and that’s when many of us get confused about which query to use in which database. To simplify this, you can use this hint to convert between DBMS -

Hint: Assuming you are a data scientist, what is the equivalent of PostgreSQL’s DATE_TRUNC for MySQL?

9. Calculating Runway

Runway calculation in data science involves estimating the time, budget, or resources required to complete a project and comparing it with the available resources or constraints. To represent the estimated time or remaining capacity required to complete a project, we calculate the runway, and you can use ChatGPT to calculate the runway by asking the following hint:

Hint: Assuming you are a data scientist, write SQL to calculate your runway.

D. ChatGPT Hint Coding

10. Unit Testing

Sometimes you need to test a particular piece of code to verify or confirm the correctness of the code, at that time you need to perform unit testing, which can be easily done with the help of the following ChatGPT hint -

Hint: Assuming you are a data scientist, write a unit test for the training function. The test cases are: x should not be null, y should be numeric.

11. Code Optimization

To make the code run faster, use less system resources, or take less memory, while maintaining or improving its output, we need to optimize the code. There are several techniques for optimizing normal code, such as algorithm optimization, time and space complexity optimization, data structure optimization, etc. All of these can be achieved by using ChatGPT prompts.

Prompt: Assuming you are a data scientist, can you improve the time complexity of the code?

Python

def sum_of_squares(n):
result = 0
for i in range(n):
result += i ** 2
return result

def main():
num = int(input("Enter a number: "))
print("Sum of squares:", sum_of_squares(num))

if __name__ == "__main__":
main()

12. Code Explanation

As humans, sometimes it becomes difficult to understand the code written by others or the code you wrote yourself after a long time. At this point, this ChatGPT prompt can be used effectively.

Hint: Assuming you are a data scientist, can you explain what the code is doing?

Python

def sum_of_squares(n):
result = 0
for i in range(n):
result += i ** 2
return result

def main():
num = int(input("Enter a number: "))
print("Sum of squares:", sum_of_squares(num))

if __name__ == "__main__":
main()

E. ChatGPT Spreadsheet Tip

13. Create Spreadsheet Formulas

Creating formulas for a specific set of cells can be a time-consuming task, but ChatGPT can do it in seconds, you just need to specify your requirements and row and column restrictions to the AI.

Let's see how to do this-

Hint: Assuming you are a data scientist, create a spreadsheet formula to calculate the sum of cells C1 to C20?

14. Generate dummy data

Suppose you are a data scientist and want to create dummy data, then this tip is definitely your lifesaver. Since you have to create dummy data every other day in your work journey, this tip makes it much easier. Just by providing a simple tip, you can generate data of different sizes and lengths as needed.

Tip: Assuming you are a data scientist, please generate dummy data for me to use as placeholders in my spreadsheet.

15. Improve efficiency

In order to get more precise results in the shortest time, you need to improve the efficiency of your spreadsheet, which can be achieved by minimizing calculations, avoiding the use of volatile functions, using cell references, etc. But all of these seem very tedious and time-consuming, don't worry, you can hand this task over to your assistant ChatGPT by providing the following tip -

Tip: Assuming you are a data scientist, tell me how to improve the efficiency of my spreadsheet?

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