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ChatGPT cheat sheet for data science.pdf
29 MB
Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .

The guide also addresses ChatGPT’s limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.

Tags:
#ChatGPT #DataScience #CheatSheet #2025Edition #DataCamp #Python #MachineLearning #DataAnalysis #Automation #NLP #SQL

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Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .

The guide also addresses ChatGPT’s limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.

Tags:
#ChatGPT #DataScience #CheatSheet #2025Edition #DataCamp #Python #MachineLearning #DataAnalysis #Automation #NLP #SQL

https://www.tgoop.com/CodeProgrammer ⭐️

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