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Deliberate practice techniques for software developers

Programmer Skills (and Salaries) According to Stack Exchange

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Skills

In July of this year, Stack Exchange Inc. released an online tool that lets you calculate how much money you would make if you worked there. The number you get out of the tool is based on four factors. There’s a salary floor based on the position you select (e.g., Developer or Product Designer), an adjustment based on your years of professional experience, and a bonus for living in one of a few high cost cities (New York, San Francisco, or London). Finally, the tool takes into account your skills.

Having written in the past about skills for programmers, I was interested to see what Stack Exchange decided was important for success in a programming job. Here’s what I found.

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Getting Past a Competitive Programming Plateau

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Plateau

In the Peak book, the authors describe the following learning challenge in a section called “Getting Past Plateaus”:

When you first start learning something new, it is normal to see rapid — or at least steady — improvement, and when that improvement stops, it is natural to believe you’ve hit some sort of implacable [immovable] limit. So you stop trying to move forward, and you settle down to life on that plateau. This is the major reason that people in every area stop improving.

The concept of the learning plateau is one way to describe how people approach learning, work, and self-improvement. With a new skill, there’s an initial period of excitement driven by how easy it is to make progress. Then a plateau arrives, and you have to decide whether to push through it or stick with your current skill level. And even if you push through it, you can look forward to another plateau where you’ll get to make the same decision again.

Skill Components

For most skills in life, there’s no need to push through plateau after plateau. It’s not worth the effort to become an expert in driving a car unless that’s your hobby or profession. But for the field that you specialize in, you probably do want to keep getting better. So what’s the secret to avoiding or conquering a learning plateau? The authors of Peak have something to say about that as well:

Any reasonably complex skill will involve a variety of components, some of which you will be better at than others. Thus, when you reach a point at which you are having difficulty getting better, it will be just one or two of the components of that skill, not all of them, that are holding you back.

According to this approach, the way to resist the plateau effect is to break down your target skill into its constituent parts, and be prepared to target those parts individually. In the book, the authors use typing speed as an example. Everyone who learns to type eventually reaches a speed plateau. Physical constraints mean you can’t keep increasing your typing speed forever. But you may plateau at a speed that is below your physical limits, or at least is slower than you want.

One idea for increasing your typing speed is just to push yourself to type faster whenever you get the chance. But according to the authors, there’s a more effective way. Rather than trying to type faster 100% of the time, try typing faster for just 15-20 minutes per day. During that time, document the mistakes you make. It’s likely that some letters or letter combinations will trip you up more than others. Once you identify them, you can more efficiently target those components, rather than trying to get better at the skill all at once.

Typing happens to be one component of the skill known as competitive programming. If your typing skills are slower than average, or if you’re competing at a high level in timed contests, working on your typing speed might be worthwhile. But for most competitive programming enthusiasts, working on other skills is more likely to produce results. What are those other skills?

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Three Ways to Solve UVa 108

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Window Grid

UVa 108 is rated as a Level 1 (easy) problem by uHunt, but its solution nevertheless contains some interesting techniques. Here’s a summary of the problem statement:

Given an $N \times N$ array $A$ of positive and negative integers, print the sum of the nonempty subarray of $A$ that has the maximum sum. The sum of a subarray is defined as the sum of its elements.

uHunt lists this problem in the section called Max 2D Range Sum, a subcategory of Dynamic Programming. But before we get into the dynamic programming solution, let’s examine the Complete Search approach.

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Competitive Programming Training Tips

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GolfBalls

Over the past couple of weeks, I have been writing about deliberate practice as described in Peak by Anders Ericsson and Robert Pool. The book describes three types of practice: Naïve practice, purposeful practice, and deliberate practice. The latter two types of practice are both effective, but there’s a key difference that makes deliberate practice the best choice. The difference is where your training plan comes from. Purposeful practice can be used with any reasonable plan. Deliberate practice requires that you practice using a plan that has been proven to work by an expert who has gone before you and achieved success in your target field.

How might we find an expert training plan for competitive programming? This type of programming is different from regular software development. So it won’t do to use a standard programming training plan, as provided by a university curriculum or a programming boot camp. Competitive programming is taught as a specialty in some universities, where coaches devise their own training plans to build winning teams each year. But that’s only useful if you’re a student at one of those universities. There are a couple of books specifically about competitive programming, including Competitive Programming 3, which I’m in the process of writing a chapter-by-chapter summary for.

But for today’s purposes, I’m going to cover training advice provided by competitive programming experts on Quora, specifically in answers to the question What is the best strategy to improve my skills in competitive programming in 2-3 months? Over time, that question has become a merge target for a number of similar questions, and quite a few experienced competitive programmers have weighed in with suggestions.

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Achieving Peak Performance in Competitive Programming

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Peak

Last week, I wrote about the concept of mental representations, an important topic in Peak by Anders Ericsson and Robert Pool. According to the authors, learners seeking expertise should have as their goal a virtuous cycle between mental representations and deliberate practice: Deliberate practice should produce more effective mental representations, and more effective mental representations should drive better practice.

This week I’ll be covering the other half of that virtuous cycle, deliberate practice.

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Mental Representations for Competitive Programming Practice

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Brains

Psychologist and deliberate practice pioneer K. Anders Ericsson has been studying and writing about deliberate practice for decades, and his landmark 1993 paper provides an accessible introduction to the topic. This year, he published his first book-length exploration of deliberate practice for a general audience. Peak: Secrets from the New Science of Expertise explains the idea of deliberate practice from the ground up, and provides examples of how people have used it in different fields. I started this blog with the goal of studying and explaining deliberate practice techniques for software developers. This week and next, I’ll be applying the ideas from the book to that end. As usual, I’ll be drawing my examples from competitive programming practice.

The topic for this week: Mental representations.

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Three-Dimensional Dynamic Programming for UVa 10755

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Cuboid

Two weeks ago, I introduced the concept of memoization for dynamic programming, using as an example UVa 787. That problem involves operations on a sequence of integers, a one-dimensional structure. UVa 10755: Garbage Heap increases the problem complexity by organizing its integer data into a three-dimensional shape, a rectangular parallelepiped. Nevertheless, we can use memoization in a similar way to solve this problem.

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Dynamic Programming Basics for UVa 787

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Memo

In programming contests, some algorithms and techniques get more emphasis than they do in school or in professional programming work. One such technique is dynamic programming. CP3 has this to say about dynamic programming:

This technique was not known before 1940s, nor frequently used in ICPCs or IOIs before mid 1990s, but it is considered a definite prerequisite today. As an illustration: There were ≥3 DP problems (out of 11) in the recent ICPC World Finals 2010.

As you might expect for such a popular competitive programming topic, uHunt has twenty-one starred problems, divided into seven sub-categories, for dynamic programming practice. As I make my way through that list, I’ll be writing about a selection of these problems. Today I’ll cover the first one, UVa 787: Maximum Sub-sequence Product.

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Write Your Own Manual

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SmallComputerHandbook

You can become a better software developer by improving your ability to organize information. A key part of that skill is being able to communicate what you know in writing. Whether you’re enlightening your peers on Stack Overflow or writing FAQs for your team, it’s good to have a reputation as a person who Knows The Answers. If you can write clear technical designs, how-to guides, and other documentation, you can have a lot of influence. In fact, even if you just write for yourself, you can get things done faster by avoiding trips to the Web to repeatedly look up the same information.

Here are some tips on when, what, and how to write as a programmer.

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