From a talk that I recently attended, I learnt about a new statistical language. Now the initial question that I had before attending the talk was why do I need a new language. However even after the talk, I could not really get a good handle on the answer to that. Even though a lot of analytics professionals do not look at SAS as a statistical language, there are those among us who are quite comfortable with that idea and can live with R and SAS. So why do we need another language?
The talk by itself was relatively interesting. The language was Julia and the speaker was Viral Shah who was one of the founders of the language. Since the perspective from the founder of anything is about why they did something, it usually makes for an interesting talk. I learned interesting stuff about the different elements of rating (or evaluating) a programming language. These elements can change as hardware and technology improve. (Hence you could always see the arrival of new languages in the future.)
The first thing of interest in the talk was the fact that there exists a million (well may not be that many) languages out there. They differ from each other in some fashion or the other making them the preferred language for some and the not so preferred for others. Some like C and Fortran have history (and speed) associated with them. Others like Matlab (and Ocatave from the open source world) have some mathematical flavor in their workings. Others like S and R which have a stats flavor that have their own followers. It makes for a very different world and at some level it does not enable people to talk to each other. This is apart from the typical Stat analysis packages like SAS, TREENET, SYSSOFT etc. that people use for day to day analysis and data manipulation.
Anyways Julia is supposed to be a new paradigm in technical computing. It does have some noteworthy features including beating the crap out of other packages on key speed benchmarks and is open source but I just lost track of some of the other features. It is faster as it has a JIT compiler (to be honest, I am not sure how this helps and I am not even sure if this is why it is faster) but it does not run into interpretation challenges that R has (at least from my understanding). It is optimized for parallel computing (and I thought even R had it but now I am not sure!) There are other features I am sure but what is interesting is how the community around this is growing. They already have more than 175 packages as far as I understand in a span of less than a year going public!
It looks like there is going to be multi-core support coming soon as well as some level of support for GPU computing. The question is whether the world would have moved on since! I want to think that this is the day of everything happening online and so there will soon be a world where you do not have to download anything. You just work with your browser and you are set (which makes trying new software a cinch!)! I am not sure where that leaves me though. I am still playing with R to the extent that everyday feels like I have discovered new features of a toy (wait for my next post!)! Not sure how to make the switch!
The talk by itself was relatively interesting. The language was Julia and the speaker was Viral Shah who was one of the founders of the language. Since the perspective from the founder of anything is about why they did something, it usually makes for an interesting talk. I learned interesting stuff about the different elements of rating (or evaluating) a programming language. These elements can change as hardware and technology improve. (Hence you could always see the arrival of new languages in the future.)
The first thing of interest in the talk was the fact that there exists a million (well may not be that many) languages out there. They differ from each other in some fashion or the other making them the preferred language for some and the not so preferred for others. Some like C and Fortran have history (and speed) associated with them. Others like Matlab (and Ocatave from the open source world) have some mathematical flavor in their workings. Others like S and R which have a stats flavor that have their own followers. It makes for a very different world and at some level it does not enable people to talk to each other. This is apart from the typical Stat analysis packages like SAS, TREENET, SYSSOFT etc. that people use for day to day analysis and data manipulation.
Anyways Julia is supposed to be a new paradigm in technical computing. It does have some noteworthy features including beating the crap out of other packages on key speed benchmarks and is open source but I just lost track of some of the other features. It is faster as it has a JIT compiler (to be honest, I am not sure how this helps and I am not even sure if this is why it is faster) but it does not run into interpretation challenges that R has (at least from my understanding). It is optimized for parallel computing (and I thought even R had it but now I am not sure!) There are other features I am sure but what is interesting is how the community around this is growing. They already have more than 175 packages as far as I understand in a span of less than a year going public!
It looks like there is going to be multi-core support coming soon as well as some level of support for GPU computing. The question is whether the world would have moved on since! I want to think that this is the day of everything happening online and so there will soon be a world where you do not have to download anything. You just work with your browser and you are set (which makes trying new software a cinch!)! I am not sure where that leaves me though. I am still playing with R to the extent that everyday feels like I have discovered new features of a toy (wait for my next post!)! Not sure how to make the switch!
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