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Sep 15, 2023
4 min read

4 Data Science Lessons to Learn from Professional Poker Players

There are more similarities than you might even think…

Poker, in various ways, reflects real-life situations, and the decisions made bear striking resemblances.

In fact, there are more similarities than you might think…

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Since April, I’ve been utterly obsessed with poker, and it has become my primary weekend go-to hobby. I play poker on every possible occasion.

From playing heads-up with my friends to crushing online poker games at Pokerstars.

I found myself curious about why poker has held my attention to such a degree, despite the existence of 1000s of other card games.

Here’s what I’ve come to understand:


In poker, understanding the probabilities of different card combinations and outcomes is essential. The poker game is full of calculations & optimizations (often under extreme uncertainty). This mirrors precisely the daily tasks of most data scientists. Starting from EDAs to building & evaluating ML models.

In addition, poker requires concentration. Exactly as a data scientist needs concentration for data cleaning processes, poker players need concentration for calculating odds for every single hand they play.

Cards are a mathematical problem.

That’s my fuel. That’s where my passions are.

Decision-Making Under Uncertainty

Poker players must make decisions under conditions of uncertainty. Similar to data scientists & engineers who often work with incomplete or noisy data.

Both poker players & data scientists share one of the most valuable skills in the modern world — making informed decisions while considering uncertainty.

Poker is a game that constantly pushes you to make complex decisions, and irrespective of whether a decision makes you win or lose, you are always improving your decision-making skills.

Points 1 & 2 represent the primary reasons for my unstoppable poker enthusiasm.

Pattern Recognition

Successful poker players develop the ability to recognize patterns in their opponents’ behavior, such as betting tendencies and playing styles.

Once the patterns are recognized — they exploit them to their advantage.

In the same way, data scientists rely on pattern recognition when identifying trends, anomalies, and insights in data to find the truth & answer the most complex questions out there.

Embrace Patience

Poker players typically play only about 20% of their hands. A significant part of the game involves folding (discarding your cards) and patiently waiting for the right moment.

In data science, you also should embrace patience.

Sometimes you can’t build a good model.

And you can’t analyze all the datasets.

You can’t win every time.

Embrace patience & the success will come!


Poker constantly dishes out bad beats and a good player is never scared of them. Poker, being a game of odds, witnesses them fluctuate unpredictably at any given point in time.

However, one’s resolve and mental conditioning help weather the bad beats and push them to keep going. Similarly, in life, a player encounters numerous setbacks and often fails to give it another shot due to the fear of losing again.

In Data Science, exactly as it is in poker, bad beats are a part of the game.

There are W and there are L.

It doesn’t matter whether you’ve been able to win a specific hand or improve the accuracy of the algorithm.

How one gets past the lows and continues to move ahead is what matters.

Thanks for reading! I hope you enjoyed it!

Stay tuned, stay safe & healthy!