If you’re working as a data analyst today, Python is no longer just a “nice to have.” It’s part of the daily toolkit.
But here’s the reality:
Continue readingMost analysts don’t need dozens of libraries — they need a small, reliable stack, used well.
Over the past two years, artificial intelligence has rapidly entered the world of data and analytics. Large language models can now write SQL, explain trends, generate dashboards, and summarize reports in seconds. Because of this, a common question is starting to appear in data teams and executive meetings:
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AI is getting uncomfortably good at the parts of analytics that used to look impressive.
It can:
So it’s natural to wonder: Will AI replace analysts?
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Over the last few years, “AI transformation” has become a board-level conversation.
Executives ask:
Underneath the noise, a more important question exists:
Continue readingWhat actually changes when an organization moves from BI to AI?
Tree models are among the most widely used machine learning methods in modern business systems.
From fraud detection and churn prediction to logistics risk scoring and pricing optimization, tree-based models power decisions across industries.
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Most analysts start experiment analysis in SQL.
You write a query.
You compare treatment vs control.
You calculate lift.
You declare a winner.
For many well-designed A/B tests, that’s perfectly valid.
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If you work in applied data science long enough, you’ll eventually hear some version of this question:
“Should we switch from logistic regression to a tree-based model?”
Sometimes the answer is yes.
Very often, the answer is not really.
In a world filled with gradient boosting, deep learning, and AutoML tools, logistic regression can feel almost embarrassing to mention.
It’s old.
It’s simple.
It’s taught in every intro course.
Over the past decade, data scientist has become one of the most attractive titles in tech.
It promises impact, influence, and technical depth.
It suggests working on hard problems, building models, and shaping decisions with data.
When people talk about feature engineering, SQL is often treated as a second-class citizen.
You’ll hear things like: