Master data science workflows with ChatGPT

Data science is an ever-evolving field and the constant influx of data makes it a compelling case for solving complex problems with innovative solutions. One such solution that has attracted attention in recent times is ChatGPT. This powerful language model, developed by OpenAI, has demonstrated remarkable natural language generation and understanding capabilities.

While ChatGPT is primarily used for conversational and text generation tasks, data scientists can leverage its potential in their workflows to streamline and improve their work, making their processes more efficient and productive.

This article highlights skills data scientists can learn to make the most of ChatGPT's capabilities.

ChatGPT can be a versatile assistant capable of generating code, explanations and ideas. ChatGPT's effective prompts can be useful in data science workflows and code debugging. Additionally, iterative and experimental prompting techniques can generate more accurate and insightful responses from ChatGPT.

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Master stimulation techniques

Some of the common ways to activate ChatGPT effectively are listed below.

• Iterative prompts: It involves creating instructions that are based on previous answers, promoting a conversational flow.
• Experimental indications: Similar to the iterative and experimental development of machine learning models, data scientists can also experiment with prompts with different levels of guidelines. This is an essential skill for budding data scientists, mainly because ChatGPT tends to assume any missing information instead of asking for it. A typical example would be an instruction that asks ChatGPT to read a file and perform some processing on the data, which may cause it to assume that the input file is a CSV. This may or may not be true, depending on your use case. Therefore, experimenting with incremental guidelines is often a best practice.
• Low-possibility and zero-possibility learning: When the model does not see any examples but is instructed to respond, this type of direct prompting is called zero-trial learning, while few-trial learning involves providing some examples for the model to learn before receiving the stimulus.

Effective prompting techniques are essential to extract meaningful information from ChatGPT. We can explore various methods to develop clear and precise quick instructions to obtain the desired results.

• Understanding the use of delimiters is essential to structuring statements and queries effectively.
• Learn how to specify input arguments, required steps, and the function return data structure of a data science workflow in prompts.

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Optimizing Code Review Workflows

Efficient code reviews are crucial to the success of data science projects. As data scientists, we can ask ChatGPT to improve code review workflows, meet coding standards, and debug code effectively.

Chain of Thought (CoT) prompts can be designed to improve code quality. For quick reference, CoT is a technique that invokes the reasoning process of LLMs by providing them with some short examples, which explicitly describe the reasoning process. The model then follows a similar reasoning process to answer the question, thereby improving the model's performance on tasks that require complex reasoning.

Code explanation and simplification

Data science code can sometimes become complex and challenging for a not-so-tech-savvy audience to understand. ChatGPT can explain or simplify complex code, making it more readable and understandable. CoT prompts are useful for explaining and simplifying the code.

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Code optimization

Optimizing code for efficiency is a critical aspect of data science workflows. ChatGPT can be used to write efficient code and explore the possibilities of alternative solutions.

Effective CoT prompts are used to propose an efficient alternative code along with an explanation. Data scientists can also learn to develop suggestions that encourage writing efficient code, using keywords such as “algorithmic efficiency” or suggesting alternative data structures.

Code testing and validation

Data scientists also use ChatGPT to design practical tests and assertions, generate code tests, and validate code correctness.

Zero-fire prompts are quite effective when writing assertion statements for commonly used functions in Python. Developing messages to generate unit tests to validate a block of code is also a good use of ChatGPT.

SQL data analysis

SQL is a fundamental tool in data analysis and ChatGPT can help generate SQL queries for various tasks. Data scientists can explore the possibility of writing zero-shot CoT messages to generate SQL statements to query specific data conditions.

Additionally, they can also design requests for SQL commands that perform data aggregation.

Translation and data manipulation

Translating and manipulating data between different formats and languages ​​is common in data science. Data scientists can use ChatGPT by learning how to design few-shot comparative and conditional messages to translate complex SQL queries into corresponding Python code.

They can also apply zero-shot and few-shot indication techniques to calculate aggregate values ​​for different fields and manipulate data effectively.

Data transformation and remodeling

ChatGPT can also be asked to assist in data transformation and reshaping tasks, which are quite common for data analysis. We can apply context-based zero-shot indication techniques to consolidate data from different sources. Additionally, few-shot prompts are also designed to create confusion matrices or pivot tables to reshape the data as needed.

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Data preprocessing

We can employ ChatGPT to identify missing fields and determine outliers. Effective prompts can also be designed to impute missing data using mean and median values.

Data visualization

As data professionals, we can write context-based prompts to generate code to create various diagrams, charts, and graphs. You can also format the plot and annotate it with relevant labels, legends, and titles to improve data representation by enabling ChatGPT.

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Feature Engineering

Feature engineering is one of the most sought-after skills in a data scientist's toolbox. ChatGPT can help generate meaningful features for machine learning models, such as creating time-based engineering features. Common time-based functions in date and time columns include day of week, month, and year.

Additionally, general feature engineering benefits from ChatGPT, such as clustering, normalization, and categorization.

Reports for non-technical audiences

ChatGPT can identify key differences between technical and non-technical communication styles and recognize the importance of tailoring communication to specific audiences. Iterative context-based prompts can help explain data science insights using terminologies and KPIs suitable for non-technical stakeholders.

With this, we conclude this post by discussing the various activation techniques to use ChatGPT effectively in data science workflows. This comprehensive roadmap covers how ChatGPT can be a valuable tool for improving productivity and efficiency in coding, data analysis, machine learning, or storytelling.

Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author and international speaker. Her mission is to democratize machine learning and break down the jargon so everyone can be a part of this transformation.