How to Get Better at Solving Programming Problems

How to Get Better at Solving Programming Problems

Getting better at problem-solving requires more than just reps. It’s also how you go about it. Most of us have been given the same advice: to become a better problem solver.

Most of us have been given the same advice: to become a better problem solver, you need to solve more problems. But this advice is too simplistic. Getting good at anything requires more than just reps. It’s also how you go about it.

Besides, when programmers hear the advice “solve more problems,” they often think that “more” means faster. It’s a mistake I made when starting out.

The problem-solving treadmill can be detrimental to learning and improving. Early on I’d speed through one problem and head to the next. But the reality of that approach soon set in. By focusing on quantity, I compromised quality and missed key learnings along the way.

It’s not to say that repetition doesn’t matter; it does. However, repetition alone doesn’t get to the heart of the matter. The process does.

I have a vested interest in this topic: I want to get better at solving problems in order to improve as a programmer. So here I offer my plan of attack. It involves reps to be sure-and a whole lot more.

1. Solve a Variety of Problems on a Variety of Platforms

“I was obsessed with HackerRank when I began learning to code,” said an instructor of a Python course I was taking. Although it’s fine to have a favorite platform, don’t limit yourself to a single one. Here’s why: you need to be ready for anything.

One goal of mine is to toggle smoothly among different problem types and across different platforms. The problems on Interview Cake are different than those in Reuven Lerner’s book, Python Workout. Likewise I find the problem statements in Lerner’s Weekly Python Exercise different from those on HackerRank.

I have my preferences, to be sure. However, I need to be able to solve problems of all types. So I’m using a variety of platforms and resources to get practice.

Here are some examples:

  • LeetCode
  • Cracking the Coding Interview by Gayle Laakmann McDowell
  • Exercism
  • Interview Cake
  • Python Workout by Reuven Lerner
  • Weekly Python Exercise by Reuven Lerner

On Sundays, when I make my plan for the week ahead, I select a few problems from the above resources. I’m deliberate about it. For example, I’ll pick a problem that focuses on binary search from LeetCode. Then, one that focuses on data structures from one of Reuven Lerner’s sources.

This practice prevents me from getting too comfortable. I can’t rely on the same data structure or technique. I need to be able to pick the best tool for the job. I’ve got to be able to pivot.

It also challenges me. That’s because I select problems that push me to the edge of my limits, a feature of what psychologist Anders Ericsson calls “deliberate practice.”

Deliberate practice is all about skill development. It’s a fully-focused, conscious effort that takes you out of your “comfort zone,” centers on a specific goal, and “demands near-maximal effort,” Ericsson explains in his excellent book, Peak.

In other words, you’re not going through the motions doing something that comes easy or natural. “The hallmark of . . . deliberate practice,” Ericsson writes, “is that you try to do something you cannot do.”

code productivity learning problem-solving programming deep learning

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