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RIP BI Dashboards. Tools like Tableau and PowerBI are about to become extinct. This is what's coming (and how to prepare):

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This guy literally just shared the code for his hedge fund's algorithmic trading strategy live:

QuantAgent: A Price-Driven Multi-Agent LLMs for High-Frequency Trading in Python Here's what you need to know:

Correlation is the skill that has singlehandedly benefitted me the most in my career. In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing). Let's go:

Python blows my mind for finance. Example: Asset Correlation Heatmaps. It's literally 1 line of code...

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Every Data Scientist needs to learn Generative AI Agents 99% of them are overlooking agentic AI. This is a massive opportunity for you. I'd like to help. Here's how:

Case in point: This SQL Business Intelligence Agent It converts text to SQL. More importantly, it answers analysis questions with business insights in real time.

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I've never been a fan of Tableau and PowerBI. Static dashboards don't answer dynamic business questions. That's why a new breed of analytics is coming: AI Analytics.

RIP BI Dashboards. Tools like Tableau and PowerBI are about to become extinct. This is what's coming (and how to prepare):

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Python is mind-boggling for finance. Case in point: There's a Finance database of 300,000 tickers. Available 100% for free:

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8. A new problem has surfaced that is changing data science: Companies NOW want Agentic AI. Agentic AI is the single biggest force of our decade. Yet 99% of data scientists are ignoring it. That's a huge advantage to you. I want to help.

7. Weaknesses: Only detects linear relationships. Non-linear relationships, even strong ones, might go undetected or be poorly represented by the Pearson coefficient. Affected by Outliers: Pearson's correlation is sensitive to outliers, which can significantly distort the

6. Strengths: Pearson correlation is widely understood, easy to calculate, and easy to interpret. I use it as a preliminary step in every data analysis.

5. Assumptions: Pearson correlation assumes that the variables are normally distributed and the relationship between the variables is linear.