ChatGPT For Data Scientists: The Unmissable Cheatsheet For 2024
Table of content:
- Code Analysis
- Data Analysis
- Data Exploration and Data Visualisation
- Data Mining
The demand for AI-based technologies is increasing among data scientists. The professionals in the data science field have to use a great number of tools to handle and process huge amounts of data. OpenAI's ChatGPT has emerged as a savior for them. In this article, we will explore how ChatGPT for data scientists can bring revolutionary changes in the field.
We will shed light on the major contributions of ChatGPT for data scientists. Let's begin!
Code Analysis
ChatGPT for data scientists has emerged as a powerful tool ever since it was launched. It allows data scientists to perform a wide range of activities related to code analysis. We will explore a few of them here, namely:
- Code debugger
- Code explainer
- Code optimizer
- Code simplifier
- Code translator
- Code corrector and quality tester
We'll explore some of these code analysis activities and ChatGPT for data scientists can be useful here.
Code Debugging
Code debugging is the process of identifying and resolving errors or bugs in the source code of the software. When software does not function as planned, computer programmers examine the code to ascertain the cause of the error that may have occurred.
ChatGPT can help data scientists to perform debugging of codes written in different programming languages, such as R, Python, SQL, etc. All they have to do is write a well-defined prompt to perform the action.
Let's see a sample prompt of how data scientists can use ChatGPT for code debugging:
"In the following code snippet <enter code snippet> of this code <enter code> I am getting the following error <enter error>. Act as a Python programmer and find the reason for the bug" |
By using this prompt, the Artificial Intelligence-based chatbot will conduct an analysis of the code and return the response with the name/type/nature of the error.
Code Explanation and Code Optimisation
As a data scientist, you can use ChatGPT for its abilities as a code explainer and code optimizer.
Again, all you have to do is write a well-defined prompt and ask ChatGPT to explain the code correctly. We have provided a sample prompt for you to help you with the task:
"act as a code explainer in R programming language and explain the function of <enter function> in terms of what it does. Please provide an illustrative example as well" |
Apart from this, ChatGPT for data scientists can also be utilized for optimizing a code for performance. See the example with the following prompt for this:
"act as a code optimizer in Python and optimize this code <enter code> by cleaning it and making it more readable, run faster, be error-free, and efficient" |
Here, you can see we have described all the categorical variables properly so that the chatbot is clear with what it has to do.
Other than these code analysis tasks, this Artificial Intelligence technology is also capable of undertaking code simplification, code translation, and testing the quality of the code. Below are the prompts you can you for these code analysis tasks:
Prompts for code simplification:
"act as a SQL programmer and simplify this query <enter query> in the code <enter code>. Please make sure the final product is efficient, has better time complexity, runs smoothly, and is easy to read" |
Prompts for code translation:
"act as a programmer and translate the following code from Python to R <enter code>" |
Prompts for code quality testing:
"act as an R Programmer and write unit tests for the function <enter function name> which is <enter requirement for the unit tests> in the code <enter code>" |
Data Analysis
In data science, data analysis is referred to as a process of obtaining raw data and converting it into useful information. To do this, raw data is collected, analyzed, and processed to find solutions, approve/disprove theories, and test hypotheses. ChatGPT is a valuable tool for data scientists as it enables them to perform data analysis with utmost ease and accuracy.
For instance, data scientists can use ChatGPT to generate data and create tables. A simple prompt can be used to do this task:
"act as a data generator and write a SQL query in <enter database version> and create a table <enter table name> with the columns <enter column names>" |
This ChatGPT prompt will provide users with the required data written in SQL.
You can further, analyze data in the tables you just created. See this prompt to know how ChatGPT performs data aggression in SQL:
"act as a data scientist and perform <enter functions like count/sum/take average> of <enter values> which are <enter variables>" |
Similarly, data scientists can perform data analysis tasks using ChatGPT. Have a look at some of them:
Prompt for generating and saving files:
"act as a data scientist in <enter programming language> and generate a <enter a file type (eg. markdown/CSV/JSON)> that contains <enter the required data>. Please save this file to <enter file name>" |
Prompt for data merging:
"act as a data scientist in <enter programming language> and please merge two tables where the first table is <enter table 1 name> that consists of the columns <enter column names> and the second table is <enter table 2 name> that consists of the columns <enter column names>" |
So, you can see how ChatGPT is for data scientists simply your work of data analysis.
Data Exploration and Data Visualisation
Data exploration and data visualization are important aspects of data science. ChatGPT is trained to conduct data science applications and assist data scientists. It helps them gain critical insights into analyzing patterns within datasets.
In the process of data exploration, data scientists are required to evaluate patterns in a dataset. It is done through various tasks, such as summarising, generating descriptive statistics, data profiling, data cleaning, exploratory data analysis (EDA), and feature engineering. Additionally, it can also perform tasks like hyperparameter tuning, model evaluation, feature selection, and model interpretability.
On the other hand, data visualization is concerned with generating a graphic representation of data. For this, it uses maps, graphs, and other visual tools. In this way, it can help in communicating complex information through a visual format. Data scientists use visualization techniques to draw parallels, establish relationships, and create correlations in data.
While ChatGPT for data scientists can be used for both data exploration and data visualization, it needs to be paired with other technologies.
For instance, ChatGPT can be used with Python or R to conduct data exploration tasks as a data science tool. ChatGPT can be used in the preliminary stages of data exploration to retrieve information, ask queries, and perform other data-related tasks. Then, programming languages like Python and R are utilized for a wide range of data exploration techniques.
In the same manner, different programming languages are used for conducting data visualization tasks. For example, data scientists can use Python libraries, such as Seaborn, Plotly, Matplotlib, etc.
In this way, it can be seen that ChatGPT is not an effective tool for data exploration and data visualization. However, it can be used as an assistive technology. Take the example of how you can use ChatGPT to help understand and perform static visualization using other technologies (see the images below). The chatbot provides a detailed explanation, with code snippets on how data visualization can be done in Python.
Data Mining
ChatGPT can be a useful tool when it comes to data mining. The process of data mining involves a number of tasks, such as discovering patterns, finding relationships between variables, and producing insights from large and time-series datasets. Due to this, data mining is used by professionals of different disciplines. Some of these include finance, statistics, healthcare, marketing, etc.
ChatGPT is capable of supporting certain data mining functions. The chatbot can assist data scientists to conduct data collection, analysis, formatting, etc. Moreover, ChatGPT is also trained to generate synthetic data, create data analytics reports, provide machine learning research, and run SQL queries.
We have listed and described some ChatGPT data mining operations below:
Data transformation and formatting: ChatGPT can assist in converting raw data into meaningful information. Along with this, the chatbot can also offer the facility of formatting data.
Text mining through Natural Language Processing (NLP): ChatGPT operates on language models which allow it to support Natural Language Processing (NLP). With the help of NLP, data scientists can do text mining and extract information from large piles of data. Later this information is used for doing Natural Language Processing tasks, such as sentiment analysis, language translation, classification tasks, and identifying named entities. The machine learning models and predictive models further allow ChatGPT to understand the language patterns and assist in text mining.
Data insights and suggestions: Data scientists can also use ChatGPT for finding patterns in data and gain critical insights. The chatbot can examine the data and do correlation analysis. It can also help in finding potential relationships in the data through both supervised and unsupervised learning models. Through this, they may have clearer insights about work.
So, these were some of the features of ChatGPT for data scientists. These are some very basic examples of what the chatbot can offer. However, as the developers of ChatGPT are working to make improvements in the technology, we can hope to see this AI-based technology working more efficiently in the near future.
Hope you found this article useful. To help you grab amazing opportunities, we have brought more articles on ChatGPT. We encourage you to read them so that you remain abreast with the latest developments in the technological world.
You may like to read these ChatGPT articles:
- Learn How ChatGPT For Machine Learning Works: A Beginner's Guide
- Chat GPT VS. Other Language Models: Who Do You Think Wins?
- Who Wins The Battle Between ChatGPT VS. Other NLP Tools?
- Analyze Technical Documents Using ChatGPT: A Practical Guide
- The Impact Of ChatGPT On Job Market: Risks And Opportunities
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