When people hear the term competitive programming, they naturally think about programming contests and rankings. People who are encountering the term for the first time are just using the literal meaning, while those who are familiar with the topic think about the top competitors that they hear a lot about. But someone who is just starting to learn to solve the types of problems found in programming competitions may see things differently. In my experience, introductory problems often challenge mathematical thinking skills more than programming skills. Only a small subset of a programming language is required to solve these problems, and there’s plenty of time to look up syntax (since beginners are often not taking part in a timed competition). However, most problems (other than the very easiest ones) are structured to require mathematical thinking.

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Despite the increasing availability of alternative options such as online learning sites and coding boot camps, many students who are interested in programming still pick the traditional approach: getting a Computer Science degree. If you plan to do academic research, a degree is really the only option. But for someone who wants to get a job as a programmer, there are both advantages and disadvantages to a traditional degree.

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If you want to get better at programming, you need to get better at algorithms. In some ways, that statement is tautological. To quote Computer Science pioneer Niklaus Wirth, Algorithms + Data Structures = Programs. But besides the algorithms that you write yourself, it’s also worth studying well-known algorithms such as those taught in introductory Computer Science classes. Some software developers object to that idea. They say their language or framework already provides all of the standard algorithms, or that they can easily find them on the Web. Why do they need to learn how they’re implemented? It’s certainly true that professional software engineers shouldn’t re-implement standard algorithms for the purpose of using them in a product. But that’s not the point of learning them. The reason they’re part of CS education is that they contain useful ideas. Here’s one example: In modern programming languages, you don’t have to worry about finding the end of a string. The language hides that aspect of string implementation. But by studying string manipulation algorithms in C, you find out about the idea of a sentinel value. This is helpful in understanding how leaf nodes are represented in a tree. And now you have a couple of examples of a concept you can use in other situations where you need to indicate the end of a section of data.

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Last week I wrote about the benefits of working on programming fluency. But before a programmer can work on fluency, they need to learn to program in the first place. Until recently, prospective programmers had to go through a complicated setup process to get their compiler and environment up and running. There’s something to be learned from that process, but it can be a stumbling block for beginners. These days, it’s possible to skip that initial setup process completely. Over the last few years, dozens of sites have sprung up to teach programming in the browser. You can open a web site, and write your first line of code in a few seconds. Free options include Codecademy, Khan Academy Computer Programming, Code.org, and numerous others. There are also paid sites like CodeSchool and Treehouse. For a more do-it-yourself approach, or for those following a tutorial in a book, there are online compilers such as Coding Ground (formerly CompileOnline) and Ideone. You can find many more of these by searching online compiler. Online compilers are less likely to have built-in tutorials, but they do allow programmers to skip the compiler setup step, or write a quick program from someone else’s computer.

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As you know if you’ve been following along, I’m currently working through a book called Competitive Programming 3. Each chapter has a set of practice problems, some of which are identified as “starred problems,” and are especially recommended. Chapter 1 contains 39 starred problems, categorized as “ad-hoc problems.” This generally means that they don’t focus on the standard algorithms that a beginning computer science student might learn. They only require some knowledge of a programming language, and the ability to turn a problem statement into an algorithm. This doesn’t mean these problems are trivial. A few are, but in general they do require some creative thinking. Some problems, such as How Many Knights, require almost no coding, but take some time to work out on paper. Others, such as Jollo, require a greater command of a programming language, and the ability to write and debug short programs (75-100 lines or so). And although these ad-hoc problems don’t involve implementing standard algorithms like binary search trees, some of them do involve well-known puzzles like finding all anagrams of a string.

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