Over the past few years, I’ve spent a lot of time working with Python writing scripts for data cleaning, transformation, and analysis.
Tools like Pandas, NumPy, and Scikit-learn made it easy to structure messy datasets, remove duplicates, handle missing values, and extract useful insights.
But as I started dealing with larger and more complex data, I found myself asking a question:
How far can we really go with traditional coding and where does AI actually make a difference?
That’s what inspired my latest article on FactsByte:
👉 AI vs. Traditional Coding in Data Cleaning and Insight Extraction
In this piece, I break down the topic in simple terms, comparing rule-based Python scripts with learning-based AI models.
Here’s a quick look at what it covers:
How traditional coding handles data cleaning and transformation
What AI adds through automation, adaptability, and continuous learning
The difference between structured and unstructured data handling
Why predictive insights are a strength of AI
And how combining both Python and AI gives the best of both worlds
It’s written in a way that even non-technical readers can follow but with enough depth for developers to relate to.
If you’ve ever wondered where coding meets AI in the world of data, this one’s worth a read.
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