The Hindley-Milner type system is one of the greatest yet least-known achievements in modern computing. Though exponential in complexity, type inference in Hindley-Milner is always fast for programs of human interest.
The type system is rich enough to allow the expression of complex structural invariants. It is so rich, in fact, that well-typed programs are often bug-free. Though niche in application, logic programming is an alternate paradigm for computational thinking. It's worth understanding logic programming for those instances where a programmer may need to emulate it within another paradigm.
Another logic language worth learning is miniKanren. This constraint has evolved an alternate style of logic programming called relational programming, and it grants properties not typically enjoyed by Prolog programs.
Scala is a well-designed fusion of functional and object-oriented programming languages. Scala is what Java should have been. Built atop the Java Virtual Machine, it is compatible with existing Java codebases, and as such, it stands out as the most likely successor to Java.
Fully exploiting laziness, Haskell comes closest to programming in pure mathematics of any major programming language. In particular, computer science majors should leave with a grasp of even template meta-programming.
Learning compilers is the best way to learn assembly, since it gives the computer scientist an intuitive sense of how high-level code will be transformed.
Computer scientists should understand generative programming macros ; lexical and dynamic scope; closures; continuations; higher-order functions; dynamic dispatch; subtyping; modules and functors; and monads as semantic concepts distinct from any specific syntax.
Computer scientists must have a solid grasp of formal logic and of proof. Proof by algebraic manipulation and by natural deduction engages the reasoning common to routine programming tasks. Proof by induction engages the reasoning used in the construction of recursive functions.
Computer scientists must be fluent in formal mathematical notation, and in reasoning rigorously about the basic discrete structures: sets, tuples, sequences, functions and power sets. Students should learn enough number theory to study and implement common cryptographic protocols. Students should certainly see the common or rare yet unreasonably effective data structures and algorithms.
But, more important than knowing a specific algorithm or data structure which is usually easy enough to look up , computer scientists must understand how to design algorithms e. At a minimum, computer scientists seeking stable long-run employment should know all of the following:. Computer scientists should be ready to implement or extend an algorithm that operates on these data structures, including the ability to search for an element, to add an element and to remove an element.
For completeness, computer scientists should know both the imperative and functional versions of each algorithm. Theory is invaluable when it provides hard boundaries on a problem or when it provides a means of circumventing what initially appear to be hard boundaries.
Computational complexity can legitimately claim to be one of the few truly predictive theories in all of computer "science. A computer scientist must know where the boundaries of tractability and computability lie.
To ignore these limits invites frustration in the best case, and failure in the worst. At the undergraduate level, theory should cover at least models of computation and computational complexity. Models of computation should cover finite-state automata, regular languages and regular expressions , pushdown automata, context-free languages, formal grammars, Turing machines, the lambda calculus, and undecidability.
The understanding of architecture should encompass the standard levels of abstraction: transistors, gates, adders, muxes, flip flops, ALUs, control units, caches and RAM.
An understanding of the GPU model of high-performance computing will be important for the foreseeable future. A good understanding of caches, buses and hardware memory management is essential to achieving good performance on modern systems. As such, computer scientists should be aware of how kernels handle system calls, paging, scheduling, context-switching, filesystems and internal resource management. A good understanding of operating systems is secondary only to an understanding of compilers and architecture for achieving performance.
Understanding operating systems which I would interpret liberally to include runtime systems becomes especially important when programming an embedded system without one. It's important for students to get their hands dirty on a real operating system. With Linux and virtualization, this is easier than ever before. Given the ubiquity of networks, computer scientists should have a firm understanding of the network stack and routing protocols within a network.
The mechanics of building an efficient, reliable transmission protocol like TCP on top of an unreliable transmission protocol like IP should not be magic to a computer scientist.
It should be core knowledge. Computer scientists must understand the trade-offs involved in protocol design--for example, when to choose TCP and when to choose UDP. Programmers need to understand the larger social implications for congestion should they use UDP at large scales as well.
Given the frequency with which the modern programmer encounters network programming, it's helpful to know the protocols for existing standards, such as:. Computer scientists should understand exponential back off in packet collision resolution and the additive-increase multiplicative-decrease mechanism involved in congestion control.
No student should ever pass an intro neworking class without sniffing their instructor's Google query off wireshark. It's probably going too far to require all students to implement a reliable transmission protocol from scratch atop IP, but I can say that it was a personally transformative experience for me as a student. The sad truth of security is that the majority of security vulnerabilities come from sloppy programming. The sadder truth is that many schools do a poor job of training programmers to secure their code.
They need to develop a sense of defensive programming--a mind for thinking about how their own code might be attacked. Security is the kind of training that is best distributed throughout the entire curriculum: each discipline should warn students of its native vulnerabilities. A few readers have pointed out that computer scientists also need to be aware of basic IT security measures, such how to choose legitimately good passwords and how to properly configure a firewall with iptables.
Computer scientists should understand and be able to implement the following concepts, as well as the common pitfalls in doing so:. Since it's a common fault in implementations of cryptosystems, every computer scientist should know how to acquire a sufficiently random number for the task at hand.
At the very least, as nearly every data breach has shown, computer scientists need to know how to salt and hash passwords for storage. Every computer scientist should have the pleasure of breaking ciphertext using pre-modern cryptosystems with hand-rolled statistical tools. RSA is easy enough to implement that everyone should do it. Every student should create their own digital certificate and set up https in apache.
It's surprisingly arduous to do this. As strictly practical matters, computer scientists should know how to use GPG; how to use public-key authentication for ssh; and how to encrypt a directory or a hard disk.
A course on software engineering can cover the basic styles of testing, but there's no substitute for practicing the art. Students don't seem to care much about developing defensive test cases, but they unleash hell when it comes to sandbagging their classmates.
User interface design or more broadly, user experience design might be the most underappreciated aspect of computer science.
There's a misconception, even among professors, that user experience is a "soft" skill that can't be taught. In reality, modern user experience design is anchored in empirically-wrought principles from human factors engineering and industrial design. If nothing else, computer scientists should know that interfaces need to make the ease of executing any task proportional to the frequency of the task multiplied by its importance.
Good visualization is about rendering data in such a fashion that humans perceive it as information. This is not an easy thing to do. The modern world is a sea of data, and exploiting the local maxima of human perception is key to making sense of it.
Or, on the other hand, you may consider accounting vs. Entrepreneurs can make millions of dollars with apps and software systems that they create themselves. If you have a true passion for computer science, it can be worth it to get a degree in the subject. It can give you a much better chance at a stable, high-paying career and teach you the difficult but necessary subjects that you need to succeed. These are all desk jobs that involve heavy computer use as you solve problems and manage projects.
Are you the creative type? Every industry needs computer experts, including those devoted to movies and video games, so you can have a fun and dynamic career as a digital marketer or mobile applications developer. You can even go the entrepreneurial route and become a freelancer who invents or designs new things. Another possibility is working with your hands.
If you combine computer science and engineering, you can become an expert in computer hardware, industrial design, or server infrastructure. You can build computers from scratch as a developer, or you can troubleshoot issues with gadgets and gizmos as an IT technician. Here are a few common jobs for computer science majors and their average pays, according to the Bureau of Labor Statistics:.
Keep in mind that these are all median salaries. Your exact pay will depend on things like education, experience, industry, job title, and geographic location. Professional organizations can be a great resource for computer science majors.
Not only can they help with schooling and skill-building, but they can also provide support, advice, mentorship, and job opportunities after you graduate. In addition to being an exciting field, computer science is also quite profitable. Next, learn more about this college major such as Science and get more career tips for internships and entry-level jobs such as How to Use a Blog to Apply for an Internship.
Next article. What is a computer science major? Is it right for me? You can start by asking yourself the following questions. While its value is evident, a computer science degree isn't the right choice for everyone, as it requires rigorous math courses and a propensity for both analysis and problem-solving. A four-year degree is also prohibitively expensive for some students, which is why alternative options — such as coding bootcamps and certificates — are increasingly popular.
Regardless of your exact goals and background, a bachelor's degree in computer science remains the industry standard and can help to launch a career that is both personally and financially rewarding. Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below. To succeed in computer science, you need to feel comfortable working with technology. This discipline also requires a combination of patience, creativity, and problem-solving.
Computer science majors should prepare to take several statistics and analysis courses. Students considering a computer science major should prepare to take several statistics and analysis courses, as computing concepts have mathematical foundations. Like any area of study, a computer science major requires dedication to learning and mastering the concepts.
If you have little to no experience working with computers, you may have to overcome a steep learning curve. Computer science is generally considered a difficult and competitive major. You can expect to spend long hours learning concepts and applying them through your own coding projects, and you may find it challenging to keep up with your peers.
Ultimately, though, as long as you're passionate about the discipline and ready to devote the necessary time and effort to your studies, you can achieve your goals. Perhaps the most alluring part of a computer science degree is the high salary outlook for entry-level computing positions.
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