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I would like to learn a functional language in order to broaden my horizon. I have knowledge of Python and C/C++ and I want a language to be easy to learn from someone who comes from the imperative domain of languages. I don't care if the language is powerful enough. I just want a language in order to learn the basic of functional programming and then I will try for a more difficult (and powerful language).
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I recommend pure-lang for these pedagogical ends. It's also plenty powerful. If you want something more popular / with more community support, then I'd recommend Scheme or OCaml, depending on whether you'd rather deal with unfamiliar syntax (go with Scheme) or deal with unfamiliar typing (go with OCaml) first. SML and F# are only slightly different from OCaml. Others have or will mention Clojure, Scala, and Haskell.
Clojure is a variant of Scheme, with its own idiosyncracies (e.g. no tail-call optimization), so using it would be a way of starting with Scheme. I'd expect you'd have an easier time with a less idiosyncratic Scheme implementation though. Racket is what's often used for teaching. Scala looks to be fundamentally similar to OCaml, but this is based on only casual familiarity.
Unlike Haskell, the other languages mentioned all have two advantages: (1) evaluation-order is eager by default, though you can get lazy evaluation by specifically requesting it. In Haskell's the reverse. (2) Mutation is available, though much of the libraries and code you'll see doesn't use it. I actually think it's pedagogically better to learn functional programming while at the same time having an eye on how it interacts with side-effects, and working your way to monadic-style composition somewhat down the road. So I think this is an advantage. Some will tell you that it's better to be thrown into Haskell's more-quarantined handling of mutaton first, though.
Robert Harper at CMU has some nice blog posts on teaching functional programming. As I understand, he also prefers languages like OCaml for teaching.
Among the three classes of languages I recommended (Pure, Scheme and friends, OCaml and friends), the first two have dynamic typing. The first and third have explicit reference cells (as though in Python, you restricted yourself to never reassiging a variable but you could still change what's stored at a list index). Scheme has implicit reference cells: variables themselves look mutable, as in C and Python, and the reference cell handling is done under the covers. In languages like that, you often have some form of explicit reference cell available too (as in the example I just gave in Python, or using mutable pairs/lists in Racket...in other Schemes, including the Scheme standard, those are the default pairs/lists).
One virtue Haskell does have is some textbooks are appearing for it. (I mean this sincerely, not snarkily.) What books/resources to use is another controversial issue with many wars/closed questions. SICP as others have recommended has many fans and also some critics. There seem to me to be many good choices. I won't venture further into those debates.
At first, read Structure and Implementation of Computer Programs. I recommend Lisp (for, example, it's dialect Scheme) as first functional programming language.
Another option is Clojure, which I'm given to understand is more "purely" functional than Scheme/Racket (don't ask me about the details here) and possibly similar enough to let you use it in conjunction with SICP (Structure and Interpretation of Computer Programs, a highly recommended book also suggested by another answer).
I would like to learn a functional language in order to broaden my horizon. I have knowledge of Python and C/C++ and I want a language to be easy to learn from someone who comes from the imperative domain of languages. I don't care if the language is powerful enough. I just want a language in order to learn the basic of functional programming and then I will try for a more difficult (and powerful language).
Great question!
I had done BASIC, Pascal, assembler, C and C++ before I started doing functional programming in the late 1990s. Then I started using two functional languages at about the same time, Mathematica and OCaml, and was using them exclusively within a few years. In particular, OCaml let me write imperative code which looked like the code I had been writing before. I found that valuable as a learner because it let me compare the different approaches which made the advantages of ML obvious.
However, as others have mentioned, the core benefit of Mathematica and OCaml is pattern matching and that is not technically related to functional programming. I have subsequently looked at many other functional languages but I have no desire to go back to a language that lacks pattern matching.
This question is probably off-topic because it is going to result in endless language wars, but here's a general bit of advice:
There are a class of functional programming languages which are sometimes called "mostly functional", in that they permit some imperative features where you want them. Examples include Standard ML, OCaml, F#, and Scala. You might consider one of these if you want to be able to get a grip on the functional idiomatic style while still being able to achieve things in reasonably familiar ways.
I've used Standard ML extensively in the past, but if you're looking for something that has a bit less of a learning curve, I'd personally recommend Scala, which is my second-favourite programming language. The reasons for this include the prevalence of libraries, a healthy-sized community, and the availability of nice books and tutorials to help you getting started (particularly if you have ever had any dealings with Java).
One element that was not discussed is the availability of special pattern-matching syntax for algebraic datatypes, as in Haskell, all flavors of ML, and probably several of the other languages mentioned. Pattern-matching syntax tends to help the programmer see their functions as mathematical functions. Haskell's syntax is sufficiently complex, and its implementations have sufficiently poor parse error messages, that syntax is a decent reason not to choose Haskell. Scheme is probably easier to learn than most other options (and Scheme probably has the king of all macro systems), but the lack of pattern matching syntax would steer me away from it for an intro to functional programming.
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Given that a persona has extensive experience in different imperative programming languages right from C to JAVA. Whats the learning curve for functional programming languages such a Haskell ?
Before taking on Haskell you may find it easier to learn something that isn't purely functional, like a Lisp or ML. Given your Java experience, Clojure, a Lisp that runs on the JVM, might be a good place to start.
Like the OP, I came from an OOP/imperative background before jumping to functional programming around 2006ish.
I had lots of C# and Java under my belt, and wanted to learn a new language. I started on Python, picked up on Perl, and cut my teeth on C++. It was all fine and dandy, but there wasn't anything new to learn, I was basically re-learning the same language over and over again with a little different syntax. By accident, I stumbled on Paul Graham's classic article Beating the Averages, where he talks about Lisp and functional programming.
I thought it'd be fun to learn one of these "obscure" functional programming languages. So, between Erlang, Lisp, Haskell, and OCaml, I picked up OCaml for no particular reason except that camels are wicked cuteness (totally!). Lots and lots of new stuff to struggle with here:
Graaaaah, why I have to fight the compiler to get type-inference to work right?
Arrrragh, why aren't there any decent IDEs for this language? Why doesn't emacs have a GUI editor? Where's my intellisense!
Currying? Is that just a gimmick, or is it supposed to be useful?
Waaaaah, how can I do anything practical with algebraic data types?
[] -> ... | x::xs -> ..., WTF does that mean?
For me, moving from Java to Python was easy: similar architectural idioms, similar programming style, etc. The hardest part was really learning a new library, which takes all of 6 weeks to go from n00b to pro.
Moving from Java to OCaml was really really hard, because there's nothing build on, its like starting from step 0. It took about 6 months of waffling around with the language before I stopped saying to myself "now how would I do this in OCaml?"
In retrospect, I probably wouldn't have struggled as much had I started with Haskell. Optional mutability + OOP makes OCaml seem superficially like familiar territory, and monads are still a mystery to me.
Taken from link
Users of a procedural or object-oriented language like C, Java, or Python should beware, however: You'll have to forget most of what you already know about programming. Haskell is completely different from those languages, and requires a different way of thinking about programming. It's best to go into this tutorial with a blank slate and try not to compare Haskell to imperative languages, because many concepts in them (classes, functions, return) have a significantly different meaning in Haskell.
I haven't developed anything practical in functional programming languages, but I did not have very much trouble adapting to it.
Before starting, I'd suggest improving your thinking recursively, because that's what you're going to need to do, whether in a familiar or unfamiliar language.
Also, you'll need to adapt to the idea that functions are objects, you can pass them to functions, and functions can return them.
Haskell's learning curve is somewhat higher than other functional languages (like OCaml), though it is my personal favorite.
The key, IMO, is to think in terms of composition and inputs and outputs (if you come from OO, you probably think almost entirely in terms of inheritance -- this can hamper your learning).
Try to separate pure (no side effects) from impure (side effect -- example IO, state, etc).
Remember that functions are first-class citizens.
And learn the trick of partial function application. It is quite handy once you get used to it.
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This is my very first question so I am a bit nervous about it because I am not sure whether I get the meaning across well enough. Anyhow, here we go....
Whenever new milestones in programming have been reached it seems they always have had one goal in common: to make it easier for programmers, well, to program.
Machine language, opcodes/mnemonics, procedures/functions, structs, classes (OOP) etc. always helped, in their time, to plan, structure and code programs in a more natural, understandable and better maintainable way.
Of course functional programming is by no means a novelty but it seems that it has experienced a sort of renaissance in recent years. I also believe that FP will get an enormous boost when Microsoft will add F# to their mainstream programming languages.
Returning to my original question, I believe that ultimately programming will be done in a natural language (English) with very few restrictions or rules. The compiler will be part of an AI/NLP system that extracts information from the code or should I say text and transforms it into an intermediate language which the compiler can compile.
So, does FP take programming closer to natural-language programming or is it rather an obstacle and mainstream OOP will lead us faster to natural-language programming?
This question should not be used to discuss the useability or feasability of natural-language programming because only the future will tell.
Sorry, I don't agree at all. Code is ultimately a blueprint for making things (objects), so it has to be very precise and rule-governed in order to function reliably. Natural language won't take over programming any sooner than sketching ideas on napkins will take over mechanical engineering.
I personally have come to the conclusion natural language programming is somewhat crack.
English is not exactly suited to be used fully as a programming language, too many abstract words that have no-correlation in programming, such as emotive terms and other abstract notions that have no place in programming, so to say programming could ever be "natural language" would follow, that "natural language" could be programming, but it isn't.
Now while I get what you're saying here, the problem is the english language has too many scrap terms and repeated names for the same things, so we'd be using something that isn't even specific to the domain of programming, for the task of programming.
I think its more suited that people understand that programming is in fact a highly specialized language, and use their brains and learn to code in a language, which is simple, declarative, and has a consistent definition, unlike English, where definition is highly subjective.
Once you learn the ins and outs of a language, and learn its schematics and behaviors, you can combine them to do new things.
Take Perl, everyone lambasts it for being line noise, but when you know many programming languages, once you get past the initial hurdles of "OMG LINE NOISE", there is a degree of intuitiveness about it where you can make stuff up you never read about and then see it magically works just as you expected.
And IMHO, domain specific languages trump spoken ones for targeted problem solving.
"So, does FP take programming closer to natural-language programming or is it rather an obstacle and mainstream OOP will lead us faster to natural-language programming?"
Neither. Both operate on the same principle that you have to be specific about what you want the computer to do. There must be no room for uncertainty, and neither paradigm has anything to do with natural languages. They tackle an entirely different problem: That of managing and structuring complex code and large codebases.
The big obstacle in natural languages is the parsing. It is impossible to unambiguously parse natural language. Even humans can't do it without a lot of context information (facial expressions, tone of voice), and even then, we still get it wrong quite often.
OOP and FP are only about what happens after parsing. Which meaning is assigned to each semantic element, once it's been identified and parsed.
Perhaps we'll one day be able to program in natural language. I doubt it'll happen within the next couple of decades, but it may happen one day. But today's programming paradigms will neither speed up this process or delay it. They simply have nothing to do with it, and won't help solving the parsing problem.
I don't think that functional programming is any closer to natural language programming than OO programming. Functional programming has a very verb-oriented syntax. When you program in Lisp or Scheme, you spend a lot of time thinking about functions and what actions you want to take on your data. In OO programming, you spend most of your time thinking about objects, hence it seems very noun-oriented. However, in Smalltalk, C++, and Java, you also have methods, which allow you to apply verbs to all of your nouns (so to speak).
I don't think that OO programming will necessarily lead us to natural language programming, but from my point of view it's a little bit closer than functional programming. Functional programming, to me, seems a little bit closer to math than to natural language. That's not such a bad thing, since maybe math is the language we should be thinking in when we program anyway.
Just FYI, Inform 7 is probably the closest anyone has gotten to natural-language programming. It is a language for a very specific domain: writing interactive fiction, the kind of software that began with "adventure games".
The current spurt of interest in Functional Programming result primarily of C# 3.0's cool new features is basically to enable parallelism and denotes a shift towards multi-core computing. IMHO, I don't think we can consider this a next step towards 'natural language programming'
If you are looking for the next evolution in programming languages, I would look to DSLs. DSL allows for highly customized languages that enable sophisticated biz users to configure a system without having to worry about coding details such as datatypes, threads and UI widgets.
Functional languages will have their place in "highly parallel processing" space.
Do you think subjective questions will get this here order for "Windows Internals the 5th Element" added to the database and shipped to my address? If so, natural language programming will be very close to functional programming, since I asked my question in a somewhat functional manner. If not, then natural language programming won't get my order shipped, will it? Functional programming can work because it still has nothing to do with natural languages.
No. Functional programming will take us closer to proving compilers. That is compilers that prove more assertions about your code. The more compilers can prove for us, the closer software development comes to be engineering rather than art.
A NLP programming language is probably more of a "do what I mean not what I say" style language. That is probably the opposite of the direction functional languages go.
"All programming languages are converging towards LISP."
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I've heard many times that all programming is really a subset of math. Some suggest that OO, at its roots, is mathematically based, but I don't get the connection, aside from some obvious examples:
using induction to prove a recursive algorithm,
formal correctness proofs,
functional languages,
lambda calculus,
asymptotic complexity,
DFAs, NFAs, Turing Machines, and theoretical computation in general,
and the fact that everything on the box is binary.
I know math is very important to programming, but I struggle with this "subset" view. In what ways is programming a subset of math?
I'm looking for an explanation that might have relevance to enterprise/OO development, if there is a strong enough connection, that is.
It's math in the sense that it requires abstract thought about algorithms etc.
It's engineering when it involves planning schedules, deliverables, testing.
It's art when you have no idea how it's going to eventually turn out.
Programming is one of the most difficult branches of applied mathematics; the poorer mathematicians had better remain pure mathematicians.
--E. W. Dijkstra
Overall, remember that mathematics is a formal codification of logic, which is also what we do in software.
The list of topics in your question is loaded with mathematical problems. We are able to do programming on a fairly high level of abstraction, so the raw mathematics may not be staring you in the face. For example, you mentioned DFAs.. you can use a regular expression in your programs without knowing any math, but you'll find more of a need for mathematics when you want to design a good regular expression engine.
I think you've hit on an interesting point. Programming is an art and a science. There are a lot of "tools of the trade", and you don't necessarily sit down and do a lot of high-level mathematics in order to simply write a program. In fact, when you're programming, you many not really being doing much mathematics or computer science.
It's when we start to solve difficult problems in computer science that mathematics shows up. The deeper you go, the more it will flesh itself out.. often in lower levels of abstraction.
There are also some realms of programming that you don't necessarily have to work in, but they involve more math. For example, while you can certainly learn a language and write some apps without any formal mathematics, you won't get very far in algorithm analysis without some applied math.
OK, I was a math and CS major in college. I would say that if the set A is Math and the set B is CS, then A intersects B. It's not a subset.
It's no doubt that many of the fathers and mothers of computer science were Mathematicians like Turing and Dykstra. Most of the founders of the internet were either Phd's in Math, Physics, or Engineering. Most of the core concepts of computer science come from math, but the act of programming isn't really math. Math helps us in our daily lives, but the two aren't the same.
But there is no doubt that the original reasoning behind the computer was to well, compute things. We have come a long way from there in such a short time.
Doesn't mention programming, but idea is still relevant.
Einstein was known in 1917 as a famous mathematician. It wasn't until Hiroshima that the general public finally came around to the realization that physics is not just applied mathematics.
When people don't understand something, they try to understand it as a type of something that they do understand. They think by analogy. Programming has been described as a field of math, engineering, science, art, craft, construction... None of these are completely false; it borrows from all of these. The real issue is that the field of programming is only about 50 years old. People have not integrated it into their mental taxonomies.
There's a lot of confusion here.
First of all, "programming" does not (currently) equal "computer science." When Dijkstra called himself a "programmer" (more or less inventing the title), he was not pumping out CRUD applications, but actually doing applied computer science. Let's not let that confuse us-- today, there is a vast difference between what most programmers in a business setting do and computer science.
Now, the argument can be made that computer science is a branch of mathematics; but, as Knuth points out (in his paper "Computer Science and its Relation to Mathematics", collected in his Selected Papers on Computer Science) it can also be argued that mathematics is a branch of computer science.
In fact, I'd strongly recommend this paper to anyone thinking about the relationship between mathematics and computer science, as Knuth lays out the territory nicely.
But, to return to your original question: to a practitioner, "enterprise/OO development" is pretty far removed from mathematics-- but that's largely because most of the serious mathematics involved at the lower levels of operation have been abstracted away (by compilers, operating systems, instruction sets, etc.). Similarly, advanced knowledge of the physics of the internal combustion engine are not required for driving a car. Naturally, if you want to design a more efficient car....
if your definition of math includes all forms of formal logic, and programming is defined only by the logic and calculations extant in the code, then programming is a subset of math QED ;-)
but this is like saying that painting is merely putting colored pigments on a surface - it completely igores the art, the insight, the intuition, the entire creative process
one could argue that music is a subset of math by the same reasoning
so i'd have to say no, programming is not a subset of math. Programming uses a subset of math, but requires non-math skills/talent as well [much like music composition]
Disclaimer: I work as an IT consultant and develop mainly portals and Architecture stuff. I have a Psychology degree. I never studied Maths in University. And i get my job done. And usually well. Why? Because I don't think you need to know Maths (as in 'heavy' Maths stuff) to write code. You need analytical thinking, problem-solving skills, and a high level of abstraction. But Maths does not give you that. It's just another discipline that requires similar skills. My studies in Psychology also apply to my daily work when dealing with usability issues and data storage. Linguistics and Semiotics also play a part.
But wait, just don't flame me yet. I'm not saying Maths are not needed at all for computers - obviously, you need real Math skills when designing encryption algorithms and hardware and etc -- but if, as lots of programmers, you just work an a mid/low level language (like C) or higher level stuff (like C# or java), consuming mostly pre-built frameworks and APIs, you don't really need to understand the mathematical principles behind Fourier transforms or Huffman trees or Moebius strips... let someone else handle that, and let me build value on top of it. I am not stupid. I know the difference between linear and exponential algorithms and data structures and etc. I just don't have the interest to rewrite quicksort or a spiffy new video compression technique.
Well, aside from all that...!
Math is used for many aspects of programming such as
Creating efficient and smart algorithms
Understanding Big O notation
Security (such as RSA)
Many more...
I think that programming needs math to survive. But I wouldn't call it a subset. It's just like blowing glass uses properties of physics, but those artists don't call themselves physicists.
The foundation of everything we do is math.
Luckily, we don't need to be good at math itself to do it. Just like you don't need to understand physics to drive a car or even fly a plane.
The difference between programming and pure mathematics is the concept of state.
Have a look at http://en.wikipedia.org/wiki/Dynamic_logic_(modal_logic). It's a way of mathematically analyzing things changing through time. Also, Hoare triples is a way of formalizing the input-output behavior of programs. By having some axioms dealing with sequential composition of programs and how assignment works, you can perfectly well deal with state changing over time in a mathematically rigorous way.
If the math you know is insufficient, "invent" some new math to deal with what you want to analyze. Newton and Leibniz did it for analysis (aka calculus, I think). No reason to not do it for computation and programming.
I don't believe I've heard that programming is a subset of math. Even the link you provide is simply a proposed approach to programming (not claiming it's a subset of mathematics) and the wiki page has plenty of disagreements in it as well.
Programming requires (at least some) applied mathematics. Mathematics can be used to help describe and analyze programs and program fragments. Programming has a very close relationship with math and uses it and concepts from it heavily. But subset? no.
I'd love to see someone actually claim that it is one with some clear reasoning. I don't think I ever have
Just because you can use mathematics
to reason about something does not
imply that it is, ipso facto, a
mathematical object. Mathematics is
used to reason about internal
combustion engines, radioactive decay
and juggling patterns. Using
mathematics is not doing mathematics.
I would say...
It's partly math, especially at the theoretical level. Imagine designing efficient searching/sorting/clustering/allocating/fooifying algorithms, that's all math... running the gamut from number theory to statistics.
It's partly engineering. Complex systems can rarely achieve ideal levels of performance and reliability, and software is no exception. A lot of software development is about achieving robustness in the face of unreliable hardware and (ahem) humans.
And it's partly art. Creative and idiosyncratic software design often comes up with great new ideas... like assembly language, multitasking operating systems, graphical user interfaces, dynamic languages, and the web.
Just my 2¢...
Math + art + logic
You can actually argue that math, in the form of logical proofs, is analogous to programming --
Check out the Curry-Howard correspondence. It's probably more the way a mathematician would look at things, but I think this is hitting the proverbial nail on the head.
Programming may have originally started as a quasi-subset of math, but the increasing complexity of the field over time has led to programming being the art and science of creating good abstractions for information processing and computation.
Programming does involve math, engineering, and an aesthetic sense for good design and implementation. Algorithms are an extension of mathematics, and the systems engineering side overlaps with other engineering disciplines to some degree. However, neither mathematics nor other engineering fields have the same level of need for complex, flexible, and yet understandable abstractions that can be used and adapted at so many different levels to solve new and evolving problems.
It is the need for useful, flexible, and dynamic abstractions which led first to the creation of function libraries, then class/component libraries, and in more recent years design patterns and service-oriented architectures. Although the latter have more of a design focus, they are a reaction to the increasing need to build high-level abstractional bridges between programming problems and solutions.
For all of these reasons, programming is neither a subset nor a superset of math. It is simply yet another field which uses math that has deeper roots in it than others do.
The topics you listed are topics in Theoretical Computer Science, and THAT is a branch of Pure Mathematics. Programming is an applied science which uses theoretical computer science. Programming itself isn't a branch of mathematics but the Lambda Calculus/theory of computation/formal logic/set theory etc that programming languages are based on is.
Also I completely disagree with Dijkstra. It's either self-congratulatory or Dijkstra is being misquoted/quoted out of context. Pure mathematics is a very very very difficult field. It is so enormously abstract that no branch of applied mathematics is comparable in difficulty. It is one field that requires enormous leaps of imagination. I did my first degree in computer science where I focused a lot on theoretical CS and applied areas like programming, OS, compilers. I also did a degree in Electrical Engineering - arguably the most difficult branch of engineering - and worked on difficult areas of applied mathematics like Maxwell's equations, control theory and partial differential equations in general.
I've also done research in applied and pure mathematics, and to this day I find applied far easier. As for the pure mathematicians, they're a whole different breed.
Now there's a tendency for someone to study an year or two of calculus unhinged from application and conclude that pure mathematics is easy. They have no idea what they're talking about. Studying calculus or even topology unhinged from application does not give you any inkling of what a pure mathematician does. The task of actually proving those theorems are so profoundly difficult that I will defer to a computer scientist to point out the distinction:
"If P = NP, then the world would be a profoundly different place than we usually assume it to be. There would be no special value in 'creative leaps,' no fundamental gap between solving a problem and recognizing the solution once it’s found. Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss..." —Scott Aaronson, (Theoretical Computer Scientist, MIT)
I think mathematics provides a set of tools for programmers which they use at abstract level
to solve real world problems.
I would say that programming is less about math than it used to be as we move up to 4th Generation Languages. Assembly is very much about math, C#, not so much. Thoughts?
If you just want the design specs handed out to you by your boss, then it's not much math but such a work isn't fun at all... However, coming up with how to do things does require mathematical ideas, at least things like abstraction, graphs, sometimes number theory stuffs and depending on the problems, calculus. Personally, more I've been involved with programming, more I see the mathematical side to it. However, most of the times IMO, you can just pick up the book from library and look up the basics of the thing you need to do but that requires some mathematical grasp upfront.
You really can't design "good" algorithms without understanding the maths behind it. Searching in google takes you only so far.
Programming is a too wide subject. Good software based not only on math (logic) but also on psychology, linguistics etc. Algorithms are part of math, but there are many other programming-related things besides algorithms.
As a mathematician, it is clear to me that Math is not equal to Programming but that the process which is used to solve problems in either discipline is extremely similar.
Solving a higher level mathematics questions requires analytical thinking, a toolbox of possible ways of solving problems, experience with the field, and some formalized ways of constructing your answer so that other mathematicians agree. If you find a particularly clever, abstract, or elegant way of solving a problem, you get Kudos from your fellow mathematicians. For particularly difficult math problems, you may solve the problem in stages, and codify your stage arguments using things called conjectures and proofs.
I think programming involves the same set of skills. In programming, the same set of principles applies to the solving and presenting of solutions to problems. When you have a partial solution to a programming dilemna, you include it as part of your personal library and use it as part of another bigger problem later. These skills seem very similar to the skills used in mathematics.
The major difference between Math and Programming is the latter has a lot more in common between different disciplines of programming than Math does. Two fields of mathematics can be very, very different in presentation and what is used to communicate the field. By contrast, programming structures, to me at least, look very similar in many different languages.
The difference between programming and pure mathematics is the concept of state. A program is a state machine that uses logic (maths) to transition between states. The actual logic used to transition between states is usually very simple, which is why being a math genius doesn't necessarily help you all that much as a programmer.
Part of the reason I'm a programmer is because I don't like math. I have no problem with math itself, and I'm fine with it conceptually, I just don't like doing calculations by hand. When I found I could tell a computer what the math problem is and let it do the calculating for me, a life-long passion and career was born.
To answer the question, according to my alma mater, math == programming since they allowed me to take Intro to C++ to fulfill my math requirement.
Edit: I should mention my degree is in telecommunications which, at the time, had only the standard liberal arts math requirement of one semester.
Math is the purest form of truth. Everything inherits from math.
Amen.
It's interesting to compare programming with music too. In UK, anyway, there are computing based undergrad university courses that will accept applicants on the bases of music qualifications as supposed to computing due to the logic, patterns, etc. involved.
Maths is powerful, programming is powerful, if maths is a subset of programming then it is equally true to state that programming is a subset of maths.
Maths is described using language, often written down. Therefore is maths a subset of writing too?
Historicly maths came before computer programming, but then lists and processes probably preceded maths, both of which could be equally thought of as mathematical or do with programming.
Cirtainly programming can be represented using maths, so there is some bases for it being true that programming is a sub-set of maths. However a computer program could also implement maths, representing information symbolically, as maths typically does when done on paper, including the infinite and only somewhat defined, from the fundamental axioms, as well as allowing higher level structures to be defined that use each other and other sorts of relationships beyond composition, supporting the drawing of diagrams and allowing the system to be expanded. Maths is equally a subset of programming.
While maths can represent structures such as words, maths is by design about numbers. Strings for example are more programmatic than mathematic.
It's half math, half man speak, duh.