Dependent Types In Practical Programming: Fill & Download for Free

I think starting by thoroughly learning a programming language is a good idea. It is often said that CS is more than programming or a particular language, but much of CS is done through programming, and is a way for you to pick up good fundamentals. Languages such as C/C++, Python, and Java are popular starters. Some are probably bad choice for starters (ex. Javascript, imo). I started with Java (as my first CS class happened to be taught in this), and Java has its merits over C/C++ and Python, especially as a first language. I prefer Java's static typing, though I also enjoy Python's brevity and readability. C++ is a bit messy for starters, and C is a bit too low-level for your first language, but it has its benefits. It doesn't matter too much what you happen to pick.Once you have picked a language, grab an introductory book (or find resources online); work through the exercises (preferably by actually compiling and running your programs on a real computer) and learn basic programming constructs (if-else, loops, variables, Arrays, Input/Output). This might take a while if you have never programmed before, but you will get used to it.Once you got the basics down and can write simple programs, try building a larger OOP-style project involving multiple classes. This could be from an assignment online, from the back of the textbook, or something simple you wanted to build (some ideas: maybe a simple game with a GUI).You don't have to master the language, but just learn enough basics so that you can code up simple things when you want to (and also begin to think like a computer scientist). In fact, to master a language in all its depth takes quite a bit of time and experience.From this point on, what I describe follows more or less a standard college CS curriculum, which means that it has a substantial theoretical component to it. If you want to just practice programming or are interested in more direct applications such as web design, this may not apply to you, but some of these skills are fundamental to all of CS (and problem solving) , and even if you're writing a web application or a new smartphone app, knowing basic algorithms and data structures (and in general understanding the thought processes of a computer scientist) will help you a lot not only directly with what you're producing/coding, but allow you to have a greater appreciation for the kind of problem solving, design considerations, and analysis that computer scientists practice everyday.Typically, after the introductory CS course, one picks up a little more foundation for mathematical tools in CS: discrete math and basic data structures and algorithms. For this, I also recommend grabbing a textbook or looking for resources online (many colleges have their course notes and problem sets available online; MOOC's are becoming more popular too). Some basic data structures/algorithms that you should become familiar with: arrays, linked lists, Binary Search Trees, balanced Binary Search Trees (ex. Red-black trees, AVL trees), heaps, sorting algorithms (insertion sort, merge sort, quick sort), and a plenty more.As for discrete math, you should be familiar with basic probability, logic, proof techniques (induction), basic number theory, set theory, automata theory/finite state machines, and basic graph theory. These are more or less tools to help you reason about computation in its various forms. They might seem a little dry/pointless if it's your first time seeing it, so it's important see motivations for certain concepts. For example, basic number theory becomes useful in constructing basic cryptographic primitives. As another example, logic becomes necessary when you reason about how programming languages are compiled.By now, you've explored most of the basics. You can write up simple programs that do what you want, and you are familiar with basic data structures to make your programs efficient, and some basic mathematics to reason about them.Some deeper topics now. None of these are required, but they are part of standard CS curriculums and as a whole instrumental to your understanding of CS.- Algorithms. Using similar resources, learn more advanced algorithms and design principles such as greedy algorithms, dynamic programming, divide and conquer, and standard graph algorithms (shortest path, max flow, MST). In learning these, it is just as important to develop your intuition for problem solving (and rigorously reasoning about their properties) as to learn the algorithms themselves (you will probably forget the exact details of an algorithm over time). For textbooks, see Introduction to Algorithm Design by Jon Kleinberg and Eva Tardos, or Introduction to Algorithms CLRS (MIT).- Computer Organization. Even if you are not so interested on the hardware side of things, it is good (and fun!) to build up your understanding of how a computer works starting all the way down from transistors and logic gates, going up to a basic CPU, picking up concepts along the way like caches, memory hierarchy, virtual memory, synchronization primitives, assembly languages, and more. Understanding this lower level will make you a better programmer and abstraction-er (and give you intellectual satisfaction). Though it is often emphasized that CS is not really about computers, it is still important to understand how a computer works.- Operating Systems. Could be as an extension of above. Usually the undergrad class on OS is infamous for the amount of coding, so this is a good place to learn good software engineering skills and experiencing bigger projects. You will learn valuable lessons in concurrency, memory management, networking, file system, and even things like security. The best way to get used to concurrency is by coding few of the classic synchronization problems (single reader multiple writer, barrier, dining philosophers) yourself in language with good concurrency support (Python, Java are okay from my experience; Go is very clean in particular). People often put up course projects from their OS on github, so you can probably use one of those if you want to build an mini OS from scratch; but this isn't necessary to move on.From this point on, you have enough basics to start exploring your specific interests. The only thing limiting you is time! Brief overview of some subareas of CS. (Note that my coverage of certain areas is more in-depth and longer only because I happened to learned about them.):More algorithms/data structures: Plenty to learn more beyond the intro class. Some suggested topics: graph theory, linear programming, convex optimization, advanced data structures (ex. persistent data structures), approximation algorithms, distributed algorithms. Not particularly relevant to practice, but many of the mathematical ideas and analysis techniques are beautiful.Theory of Computation. You learn and reason about what computation can and cannot do (mostly the latter). From more traditional topics such as (various) automata, context-free languages, complexity classes (P, NP, #P, BPP, PSPACE), PCPs, connection to randomness, to more recent topics including circuit lower bounds, arithmetic complexity, quantum complexity, communication complexity, various hardness conjectures (ex. Unique Games Conjecture and its relation to optimality of approximation algorithms). Also has many connections to cryptography.Distributed Systems: You learn about how to design large computer systems that are distributed that can deliver some goal (ex. key-value store). Definitely on the more practical side, but the theory behind is interesting as well. Turns out both in real world and in theory, it is hard to guarantee every desirable criteria such as correctness, low latency, robustness, etc. Building real systems is sort of like real life; there are many tradeoffs.DatabasesMachine learning/Artificial Intelligence: Its importance andrelevance goes without saying. ML in particular (note that there isn't a sharp boundary between ML, AI, statistics, and data mining) has become increasingly popular in the past decade due to "Big Data" and its success in many different domains (from more traditional areas like computer vision or natural language processing, to newer applications like fraud detection, spam filtering, and content recommendation). There are a lot of interesting theory behind ML, with heavy connections to more classical fields like statistics. AI will be an interesting and open field for as long as there are robots aren't as good as humans.Subareas of above that are more focused on a particular niche: computer vision, natural language processing (NLP), robotics, computational biologyProgramming Languages (PL): This field is more than what you might guess from the name. A good place to start is to explore new paradigms such as functional programming. The theoretical foundations of PL are fascinating, and is closely related to logic. If you are more of an algebraist than an analyst in terms of mathematical preference, you might find PL theory more suited to your tastes than algorithms/complexity. Even areas of math that are traditionally known to be very abstract such as category theory surprisingly has its use in PL Some of the numerous applications of PL theory include theorem provers, verified computation, compilers, type systems, concurrency, language based security, software defined networking, and even foundations of mathematics (see HTT)--pretty much any application where you can benefit from having more well-defined semantics.Compilers. With an application of lot of neat technical ideas in well-defined stages, you can turn a program in a high-level language into machine code that can executed by your computer--how cool is that? It is best to learn compilers by building one; I think this is an invaluable experience if you are considering careers involving software engineering.Computer Graphics. You can build things that you can actually show to your friends. Interesting sub-topics: physically based animations (ex. simulating fluids), computational geometry, computational photographyCryptography: the more mathematical side of building secure systems. It is interesting to see how to rigorously define various notions of security, how to base cryptographic primitives on computationally hard problems.Security: the more practical side of above.Human Computer Interaction: the name says itFor additional resources such as Coursera, edX, MIT OpenCourseWare, and course websites from top CS departments (Stanford, MIT, CMU, UC Berkeley, Cornell, UW, etc.)Most of the topics I mentioned so far, though important for foundations, are very academic. I recommend having this academic background because it is often missing in self-taught programmers, and more importantly it exposes to you a lot of fundamental design and problem solving choices in Computer Science. If you want more practical experience (ie. the kind of skills more directly applicable to traditional software engineering role), explore the following (I'm not an expert here so please be forgiving if my list lacks coverage or is outdated, and feel free suggest edits in the comments):- UNIX. Download Ubuntu (or any distro) and start playing around with it. There are also free courses online (ex. edX) to learn more about it.- web development. I don't have much to say here from lack of experience but explore Javascript, HTML, CSS, php, Ruby on Rails etc.- mobile development (iOS, Android)- backend technologies (MySQL, MongoDB, Hadoop, etc.)- various open source projects- various other new web technologies hot at the time- anything else that intrigues youThough learning on your own will miss out on some aspects of a college curriculum (having professors/TAs to talk to, peers to share ideas with, a more organization, feedback through exams), I think it's certainly doable nowadays if you have sufficient self-motivation, given the amount of resources available online. Learning on your own is probably harder for more purely theoretical subjects (you have to force yourself to work through problems to really learn) than the more programming/building oriented areas, but I guess that also depends on your background and strengths. Make sure you also get a lot interaction with other people (through online forums), and to work on big projects and assignments instead of just learning the material in your head. Like they say, learn by doing it. Also, it often helps to discuss your problems with others.Push yourself to work on challenging (but doable) problems (if you're stuck, reach out to people on StackExchange/Overflow or Quora, they are nice (usually) and very knowledgable) and collaborating with others on larger projects since those are the experiences that self-schooled people will miss out on.Some additional notes:- Start with an imperative language, but you should experience a functional language (Haskell, Lisp, ML family including OCaml) at some point, as it is a paradigm shift in the way you think about programs and data structures.- I definitely agree with Chris's remark that "underneath many languages are similar." As you learn and write programs more and more, you will come to the understanding that they are all similar in some way. In that sense, it doesn't really matter which language you learn first, but it's just that some are easier than others for starters and for establishing solid foundations. As you get more specialized however, you will find that some more more suited for certain tasks.- If you want to get further with/spice up learning basic data structures and algorithms, competitive programming might be fun to try. I learned most common data structures and algorithms in high school through USACO Training gateway, after reading a competitive programming problem online and finding it intriguing. Most involve solving an algorithmic or mathematical puzzle using an efficient algorithm, usually codeable within one page in length, to produce correct outputs in limited time. Topcoder, Codeforces, Project Euler, IOI, Google Code Jam, ACM ICPC and others are all very good places to start.That was a bit long, but hopefully you can find something you like in there. If you need any clarification, comment below.Source: based on my experiences as a CS major

Different people might have a different perception of starting from 0 to get very good at competitive programming. I respect all their opinions. Today, I would be sharing my point of view. You might pick-up the points that you find helpful.Please follow this order sequentiallyFollow the following topics on Quora:Competitive ProgrammingAlgorithmsComputer ProgrammingSubscribe the following YouTube channels:Tushar Roy - Coding Made SimplemycodeschoolGaurav SenRachit Jaincode_reportAbdul BariStudy the style guide before starting to write the code.It ensures excellent and clean coding practice. Always keep it along with you while writing the code, or memorize it. If you skip this step, later on, you will learn it the hard way. Pick any one of the guides and stick with it.C++ Style Guide: by Google or The C++ Core GuidelinesJava: by GooglePython: by Googleor you can choose your own, with which you are comfortable.Install Visual Studio Code. It is completely free and one of the best IDEs that I have come across. Most high rated programmers code in this IDE. It has lots of custom features, specifically carved for programmers.Refer to online resources for its installation and usage guidelines.Complete the free GeeksForGeeks Fork series depending on your language of preference. If you code in C++, I recommend you to complete both C and C++ tracks.LinksCourse | Fundamentals of Programming with CCourse | Fork CPPCourse | Fork JavaCourse | Fork PythonThe time required: 1-month per courseInstall Competitive Companion Google Chrome extension and learn how to use it.LINK: Setting up Competitive Companion on Windows 7/10 - C++ 11Learn to prepare your custom template and snippets. I recommend storing your custom snippets on G-Drive, as MS-Visual Code does not have a backup and restore feature.NOTE: As you have done some basic coding, you must be familiar with the syntax of the language of your preference. I recommend you to make a custom template, rather than copy-pasting from somewhere.Start participating in Codechef long challenges and unrated contests.Please don’t jump into rated contents apart from Codechef long; it might hamper your momentum.Parallelly, solve questions from any one of the following platforms:CodeChef BeginnerCodeforces LadderLeetCodeSPOJGeeksForGeeks PracticeSolve at least 100 of them before progressing further.You will get familiar with the coding environment, IDEs, discussion forums, tags, editorials, etc. Also, you will get a basic idea about input-output format, data types, memory issues, overflow issues, modulus handling, time complexity, different types of errors (RUNTIME, Compilation, TLE, etc.) and best practices in coding.Refer to editorials as a last resort. Try solving questions on your own.The time required: 120 days (at max)Participate in the ZCO practice contest and try to solve as many questions as possible.This contest is for school students, so knowing this, you might get a headstart. There are lots of blogs and tutorials available online for each of these questions.The time required: 1 month (at max)Now, you are all set to participate in rated contests. Participate in as many contests as possible. Participating in contests help you solve more questions in less time. Add this extension and choose your favorite platforms.NOTE: Always UpsolveRegister on InterviewBit and complete all levels. There are around 300 questions in total; solving all of them will make you a master of fundamental algorithms. The best part is, everything is free.The time required: 6 months (at max)Participate and Solve all the questions of the AtCoder DP Contest. It will make you a master of Dynamic Programming. The Contest covers every nook and cranny of Dynamic Programming questions. No question of DP can be of the concept out of this contest.Complete the HackerRank Interview Preparation Kit. It is a collection of 69 questions arranged topic-wise.The time required: 3 months (at max)Read CLRS.Softcopy, solutions, everything is available online for free. Also, lots of resources related to that are available.The time required: while(1) {repeat;}This book is the bible of competitive programming. It never gets old. The more you read, the better you get.The difference between a good programmer and an awesome programmer:A good Programmer: works really hardAn awesome programmer: UpsolvesList of resources:Blog by Triveni MahathaCompetitive Programmer’s HandbookMain Page - Competitive Programming AlgorithmsQuoraWhat is a list of data structures that a competitive programmer must know?How do I learn competitive programming as a beginner?What is the best strategy to improve my skills in competitive programming in C++ in 2-3 months?How can I become good at competitive programming?What is competitive programming?What are some good coding competition/practice sites?What is the best way to progress through practice problems on CodeChef, SPOJ, TopCoder, etc.?CodeforcesAn awesome list for competitive programming!StopStalk: Tool to maintain your algorithmic progressIf you ask me how to improve your algorithm competition skill, I will give you the link of this blog.[Tutorial] A way to Practice Competitive Programming: From Rating 1000 to 2400+Competitive Programmer's Handbook — a new book on competitive programmingThe 'science' of training in competitive programmingYouTube Channels for Competitive ProgrammersRadewoosh’s blogTopic-wise Coding ResourcesCODEFORCES for BEGINNERS...Competitive Programming Community Discord (5800+ Members): Discussions hub!CodeChefGetting StartedData Structures and AlgorithmsLearn Data Structures and AlgorithmsCompetitive programming best resourceThe CodeChef WikiTutorialsStopStalk: Tool to maintain your algorithmic progressMediumHow to prepare for competitive programming?My Competitive Programming Journey To GoogleHackerEarthThe Complete Reference to Competitive ProgrammingProgramming Tutorials and Practice ProblemsHackerEarth trending notesGeeksForGeeksHow to begin with Competitive Programming?Tips and Tricks for Competitive Programmers | Set 1 (For Beginners) - GeeksforGeeksHow to become a master in competitive programming?TopcoderCompetitive Programming TutorialsCoding Blocks Blog — Sites and Tools for Competitive Programmingredgreencode - How do I get better at competitive programming?Guru99 - Competitive Programming for Beginnersblogspot - Algorithms Live!Online CoursesedXHow to Win Coding Competitions: Secrets of ChampionsMIT OCWIntroduction to AlgorithmsDesign and Analysis of AlgorithmsAdvanced AlgorithmsCMU 15-451 (Algorithms), Fall 2010USC - Data Structures and Object-Oriented DesignStanford - CS 97SI: Introduction to Programming ContestsHarvard - CS 224: Advanced AlgorithmsList of competitive programming sitesCodechefCodeforcesHackerrankGeeksForGeeksInterviewBitPrepBytesHackerearthAtCoderTopcoderOnline Judges / Problem ArchivesSpojUVa OnlineProject EulerTimus Online JudgeA2 Online JudgeGoogle's Coding Competitions ArchivesACM ICPC Problem ArchivesPanda Online JudgeZhejiang University Online Judge (ZOJ)CodecademyMy Code SchoolfreeCodeCampCodilityCoderbyteCodecupCodeGroundCodeJamCodewarsDoSelect for developersGeekyPrep.comKattisLeetCodeCodingBatProgramming HubTechGigTechie DelightThe Python ChallengeCodingame: Coding Games and Programming Challenges to Code BetterHackerBlocksAlgoExpertPlacementSagaTools for Programming ContestsOnline IDE'sIdeoneRextesterCodeforcesCodeChefCoding BlocksCSAcademyHackerRankNotable CompetitionsACM-ICPCIEEEXTREMETCS CodeVitaGoogle Code JamGoogle Kick StartGoogle Hash CodeCodeChef SnackDownMicrosoft Imagine CupFacebook Hacker CupTopcoder Open (TCO)Yandex AlgorithmHewlett Packard (HP) CodewarsBooksCLRSCompetitive Programming 3Competitive Programmer’s HandbookThe Art of Computer ProgrammingIf you made it till here, please feel free to suggest corrections or addition of resources.

Thanks for the multiple A2A!Some thoughts:Many successful CS students, software developers, and computer science researchers and educators are not white or Asian men. And no, it’s not just Grace Hopper. Margot Lee Shetterly’s book Hidden Figures, about the Black women who were critical in getting men into orbit and to the moon and back, is being made into a movie. This is important to know even (or especially) for white and Asian men; don’t make assumptions about other people (for example, when picking partners for team work).Success in CS depends on hard work. I’ve had brilliant students fail out, and I’ve had not-so-brilliant students graduate. They maybe didn’t have the highest GPA, but they finished and have the credential and professional careers and better futures. To me this is completely just: you don’t choose how intelligent you are, but you do choose how you employ your intelligence.CS is not “about programming” any more than history is “about writing”, and many CS students are not good programmers. They don’t (obviously) become software developers, but most CS graduates do not go into software development programming roles. Others go into business analysis, QA, system/database/network admins, devops, or support. Some go in other directions; in the US, a CS degree qualifies you to take the patent bar and become a patent agent (which is a form of intellectual property law that doesn’t require law school), and they’re often supported through this process by employers who have unofficial roles for them to play until they become patent agents. How do these students get through all of the programming? See #2.CS is not about math or a form of math. There are some computer scientists who believe that the only true field of study in CS is the theory of computation and/or algorithms. They’re a tiny minority. Many CS majors don’t think they’re “good at math.” How do they get through math? See #2, and realize that not all CS programs require the same math (we only offer a BA and only require Calc I and discrete).Your professional career is often limited (especially in the long term) by your so-called “soft skills”. Communicating, working in collaboration, leading and following, persuading, and adaptability are crucial to success. I recommend students minor in either a social science or one of the humanities. No one has ever come back and told me I was wrong (because I’m not). Even research computer scientists need good writing skills. We require our students to take an additional writing course beyond freshman comp.There’s a reason that multiple generations of computer scientists swear by Unix/Unix-like operating systems, command-line interfaces, and text editors (vi and emacs). Most of us love developing software in that environment, and many have turned their Unix skills into careers in system administration.You don’t need a fast computer. It’s doubtful you’re going to do anything that requires a powerful computer. If you have an Intel-based ChromeBook (not ARM-based, and you might have to do some hunting to find which is which), you can have full access to Linux and most Linux tools. Some of these cost $200 and are decent laptops for many purposes, and since they don’t have lots of power and discrete graphics, you can’t play the most absorbing video games. It’s fine to play video games, but I’ve seen students fail out of college largely as a result of obsessively playing. Avoid temptation. Recovering cocaine addicts aren’t usually encouraged to sit a room full of cocaine.You can learn about X without having a class on it. Right now the most popular X that doesn’t take multiple semesters to learn the basics of is cloud computing. Google is opening up free credits for their cloud services next month. Amazon Web Services (AWS) offers credit for students (although your department needs to support this and you still need a credit card) and I’m told Microsoft Azure is largely free for students, although I haven’t used it. All of these allow you to configure and use Linux servers running web servers and database servers and anything else you can imagine. You can also do massive distributed computation projects.Programming is not like riding a bike. Late every fall I tell my students to do some kind of programming, even just redoing a homework assignment they already did in the semester, over the break. Every time they come back, those who did are far less stressed and do better in the first couple of weeks. Now magnify this several times, and see what happens if you do no programming over the summer. Not even a lot. I make my students tell me about the project that they’re going to do over the summer; I don’t make them do it, but I make them at least think about it.It’s easy to write and release open-source code, and more and more employers like to see Github pages with publicly visible projects. It doesn’t matter if they aren’t the greatest programs on the planet. You also get experience using version control, which (sadly) is not taught to many students, despite it being fairly easy and absolutely critical to software development in the large.Yes, you have a laptop. Don’t use it to take notes. Take them by hand. You think you have bad handwriting? Ask my students about mine (writing on a board is hard). You’ll take better notes and be able to draw and move around. The tendency with typed notes is to write the professor’s lecture as verbatim as possible, but writing is slower, so you have to process what your prof is saying.You don’t have to go to MIT, Stanford, or CMU to do well. I know many successful people at top companies who went to good (but not “top”) liberal arts colleges and/or public universities. Yeah, there are some millionaires in there. If there’s an internship or job you want, apply for it. You won’t hear back from most people, and most you hear back from will reject you. It sucks. It’s frustrating. It’s the way things are. It may happen less to Stanford students, but it still happens. Also, Larry Page and Sergey Brin met at Stanford as PhD students, but both did their undergrad degrees at public schools, and only Brin ever finished a degree at Stanford (an MS).It’s not natural to listen to criticism and be able to take it in without feeling hurt. Getting good takes patience and practice.Go talk to your professors and TAs in office hours. Ask questions about your reading. Ask about anything that you’re struggling with or you find interesting. Good things happen. Some profs will be annoyed if you come in to ask something that shows you didn’t show up to class or do the reading, but most will respond eagerly if you are struggling with the material. When I was an undergrad, I had to read a book about publicani, but I had been sick for week and had missed all of the lectures, and the book didn’t actually say what the publicani were (they were tax collectors), and my dictionary didn’t describe them, either. Two minutes with the TA solved the problem.Similarly, use tutors. I tell my students: here are the tutoring hours, the tutors work in the programming lab, you know you need to spend several hours each week programming, so go while the tutors are there. That way, if you need help, it’s there. If you don’t, no harm, right? Also, it will give you a schedule.Your professors aren’t trained on the latest advances in software development. We have to do many things, and we’re not professional software developers. We therefore may not know the easiest way to do something particular with a language (although we’ll know a way). It doesn’t hurt to, ya know, Google.Not everything on Stack Overflow is accurate or current. A lot is.Google. Got a weird error? Google it. Have an infinite recursion in Haskell and you can’t find it? Google “haskell infinite recursion common problems” or something like that.If you don’t find what you want on a Google search, there’s a magic trick: scroll down to the bottom and you’ll see there are additional pages you can look at! Shocking, I know. Similarly, there are suggested alternative searches.Don’t reinvent the wheel. In your intro classes and in algorithms and data structures, we (your profs) make you build stuff by hand, despite the fact that it’s been implemented. However, you should never ever ever actually do this in real programming. Use built-in libraries or find libraries. My second semester students make audio players, despite not knowing the math or decompression algorithms.If something seems absurdly hard, take a step back every once in a while and see if you are heading down the right path. I had a student spend about four weeks working on a problem, and I typed in precisely the words they used, and instantly found 4 methods to do what they wanted, all of which would have worked. Complex computer vision algorithms didn’t solve the problem, but some simple math did. See “Don’t reinvent the wheel”.Most CS textbooks are terrible. We know this. They’re always compromises. If there was a good one, trust us, we would use it. Don’t feel like you have to rely on the textbook. You have Google. I wish I had Google when I took my first CS class (just as my predecessors wish they had 486 processors and multiple megabytes of RAM).Don’t start work last minute. You end up stressed, and you get a lower grade.There are three reasons why people work all night. The number one cause (especially with frosh/sophomores) is that they left the work until the last minute. The second is that “I’m just gonna fix one more bug,” or “As soon as I get this test to pass,” this is OK as long as you are getting regular sleep. The third reason is that many of us are night owls; again, you still need regular sleep and you need to attend classes. When I was in the startup world, if I was at work at 8am, I hadn’t arrived that day; I was still there. I could do this until maybe age 30. I never pulled an all-nighter in my MS or PhD, and it’s not because I’m a genius.When I was paid large amounts of money to write software, I always wrote unit tests before doing anything else. In basically all of my MS coursework, I wrote unit tests. When I was working on the code that would make or break my dissertation, I wrote unit tests. You can do as you wish, but you can see what I consider the most efficient way of writing good code.Don’t call it “coding”, call it “programming”. This will help you and others remember that it’s not cryptic. Code is complicated, but it’s not cryptic. It’s far, far more readily decipherable than Shakespeare (not to mention Derrida).If you assume all majors are hard, you won’t be as frustrated when your major is hard. You also won’t be a jerk to other people. The world doesn’t need many jerks. I think we currently have our quota of jerks, along with some extra. If you need a more venal reason, realize that these people may help you get a job in the future or something.Programming is not a linear task, but it does require some linear effort. You can’t just wait for inspiration to strike; go work for a couple of hours, then go get something to eat, or go for a walk. Let you subconscious do the hard work.Avoid the holy wars. Do you like Windows? OK, but don’t tell people with Macs that they’re stupid or bought something just for the brand name. Love Linux? Fine, but that person using Windows may just not be interested in the things that interest you. Think Macs are awesome? You… you’re correct. However, still be nice. ;) Vi vs. emacs, LaTeX vs. MS Word vs. Google Docs, how to pronounce LaTeX, Java vs. C++… I’ve used just about everything, and everything has its merits and drawbacks in specific contexts.You don’t have to ask for permission. I had a student ask “Can I make a video game?” I responded (somewhat snidely, but correctly and with a point), “How could I stop you?”Many PhDs didn’t have stellar grades as undergrads and did work on something of interest to them, sometimes to the exclusion of work they needed to do well on for a good grade. However, every PhD ultimately passed all of the required classes and most had undergrad GPAs above 3.0.No, this isn’t as slick as customizing your Linux desktop. However, these are the keys to short- and long-term success. Students who generally follow these things tend to be happier, be less stressed, have higher grades, and have far more free time than those who don’t. It’s all about efficiency. When you take your architecture/OS, you’ll learn about the cost of context switching and the benefit of scheduling; just because that’s stuff that happens in microseconds doesn’t mean it doesn’t apply to larger time frames.Oh, OK, one cool trick. In Google:related:[URL]sorelated:coca-cola.comwill give you sites similar to the one that of Coca-Cola, so you’ll see Pepsi and RC Cola. Want to find schools similar to yours? Try related:yourschoolname.edu (you may need the www.). There are other keywords: site:[domain name] limits results to URLs in that domain, info:[URL] will give you info about a URL (including links to pages that link to that URL), and cache:[URL] will show you what Google has cached for that URL, which may not be what’s there.