Simple Guide That Explains Why You Should Play Chess

The recent Netflix smash “The Queen’s Gambit” reignited my interest in chess, as I’m sure it did for many other viewers. I recall fondly my grandfather’s numerous after-school sessions of tutoring and defeating me until I could eke out a win by making improbable (and probably awful) moves. But as I grew older, I moved on, and my only competitive chess experience is from a single elementary school event.

I got fascinated again after login into Lichess for the first time a month ago. Chess has been tremendously interesting for the past 1500 years because of the balance of long-term strategy, short-term gain, problem solving, and pattern identification!

Furthermore, having recently completed a data science program, I’ve noticed many parallels between chess and data science. And I found myself admiring these similar features as I played in my leisure time to unwind after long days of coding, presentations, and job hunting.

Solving Issues

The goal of data science is to solve issues. It’s why businesses are eager to assemble pricey tech teams and develop proprietary software or models. Detecting fraud, improving a process or an ad campaign, and overcoming bias in facial recognition algorithms are all examples of these issues.

Because there is no clear, obvious path, these challenges are complex. There is the apparent challenge of “winning the game” study chess openings, but there are many lesser difficulties to solve in order to achieve that greater aim. What should I do to improve my knight? Is it possible for me to get away with pushing this pawn? Should I maintain developing or castle this turn?

Chess needs you to weigh your options, come up with some pros and disadvantages, and stick to your plan.

Should we swap some minor pieces with Ne5 if our goal is to control the center e5 square? On c5, why don’t you take the pawn? Or use our light-squared bishop to pin the knight on c6?

Maintaining Flexibility and Patience

Chess is not a cooperative game, just as having a plan and sticking to it is vital. Your opponent has devised their own strategy for victory! They’ll almost certainly throw substantial roadblocks in your way, forcing you to overcome or adjust your ideas. If you refuse to modify, your opponent will be able to readily figure out your strategy, adjust accordingly, and penalize you for being so rigid.

Patience and adaptability, in my opinion, are two undervalued soft qualities that every data scientist should possess. We’ll never have all of the data or knowledge we need to create the ideal process or model, and additional requirements or client-requested features could throw a wrench in the works! It’s critical to remember that our final aim hasn’t changed; only the path we’ll have to travel has. You must be patient, take in fresh information, restructure, and envision a new strategy.

So perhaps we’re employing the well-known strategy of forking the king and rook with Nc7 while our bishop protects us. However, instead of Nc6, our opponent moves Na6, interrupting our strategy by safeguarding his pawn on c7. So, what’s next? So we change, make a new plan, and keep moving forward.

Creativity’s Importance

By definition, data science is an analytic undertaking. In order to solve our goal challenge, we are analyzing massive amounts of data and creating fresh insights. That isn’t to say that ingenuity isn’t essential! The quickest path between two points is not always a straight line (at least not in reality)! As I previously stated, we may need to alter our strategy, but how we do so has ramifications.

We will never discover something new if we merely follow established avenues of thought! Furthermore, creativity tends to breed more creativity — or, to put it another way, “yes, and…” — since if you break one “rule,” you might as well break them all. While this can be frightening because you don’t have the same background, you will undoubtedly learn something, unlike if you “stayed the course.”

Take, for example, the Crab Opening. It foregoes all classic opening concepts (take the center, develop minor pieces, castle, attack) in favor of a unique, unexpected strategy that will surprise your opponent!

Whether you win or lose, you will gain knowledge.

Failure is an unavoidable reality. This may appear to be a pessimistic remark, but it isn’t! To fail is to gain knowledge. And adopting this perspective will go a long way toward ensuring long-term success and resilience in the face of setbacks or failure. Training neural networks exemplifies this in data science. They create forecasts, which are frequently inaccurate at first, and then modify the weights and try again. We should all strive to be just like neural networks (and we are, because they are modeled after our brains, after all).

You’ll also fail frequently at chess. You’ll make a mistake by misplaying, misinterpreting, or misclicking. If you play on a website that uses balanced matchmaking, you’ll always be matched with opponents who are at your skill level. As a result, you’ll probably lose 40–50% of your games! And you’re not just going to give up, are you? You study, you learn, and you try again. It’s just as easy to get disappointed by a string of losses as it is to fail to improve a model’s accuracy from 90% to 92 percent accuracy. What matters is that you continue to learn.

Conclusion

I hope that this essay has persuaded you to play chess again, even if you haven’t done so in years. I assure you that you will not be disappointed. And perhaps you’ll use what you’ve learned both on and off the board. I believe you will find them helpful in dealing with setbacks, continuing to learn, and achieving long-term goals.

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