Why dumping spreadsheets into ChatGPT isn't actually data science
Original: Large Language Models Can't Handle ALL Your Data Problems.
Summary
While large language models have impressive capabilities, they are not a panacea for all data challenges. This blog post underscores the importance of understanding the limitations of these models and leveraging specialized techniques and tools to tackle complex data problems effectively. Data professionals, including data engineers, analysts, and business intelligence analysts, can benefit from this insight to optimize their data management and analysis workflows.
Who This Is For
Data Scientists
Business Analysts
Analytics Engineers
Key Takeaways
- Learn why text prediction models (LLMs) fundamentally differ from data analysis models like gradient boosted trees
- Understand when to use specialized statistical models instead of forcing everything through chat interfaces
- Recognize that different AI approaches (transformers, SVMs, neural networks) solve completely different problems
- Stop conflating all AI tools as interchangeable and pick the right model class for your specific data task
Tools & Technologies
GPT/Gemini/Claude Google Sheets Apps Script GCP NLP API Pandas Gradient boosted trees Support Vector Machines Transformers
Topics Covered
data-warehouses column-store snowflake bigquery redshift
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