It is a common question asked in data science or data analysis forums if one should use Python or R one’s data work. So far, I myself have managed not to learn Python. I have managed to ward off the urge to do so. Now I have learned plenty of programming languages and have actual work experience in the following: C,C++,Java,Perl, PHP, Tcl, VBA. If I look further back, I should mention COBOL, Fortran, Assembler, Algol and BPL – ancient Burroughs programming language based on Algol hence, BPL. In fact, I should name Scheme/Lisp and Ocaml (see older posts) as one of the languages I can code and program. Currently, I am playing around Clojure . I can really learn Python if I wanted to. However, I don’t.
Oh please, not another language to learn!
Why? Because for statistical type of work R is enough, yes, I can even use R for data cleansing and munging, where Python could probably help. However for that type of task, R has so many functions I can avail of without touching Python. Anyway, which one is close to statistics? Python or R? It is R and if I want to do any general purpose computation I can do it all in R because of those functions. Lastly, the nice thing is that R takes some of those functional programming insights into its philosophy, it took its inspiration from Scheme.
You can get a copy of this textbook here.
Your interest and comments will be most welcomed.
I am working to release it at Amazon, CreatSpace and in Kindle.
A few days ago, I went to a seminar conducted by one of my former professors on the Internet of Things and I learned how we now have plenty of sensors which can publish data into the Internet. Name it what you will, it can be traffic cameras, or weather stations etc, they can all tap into the Net.
During the seminar, my mind wondered off and I started imagining the movie Terminator. The reality of Skynet may no longer be confined to the movie franchise. Then today I stumbled on this article of Elon Musk.
Read it and let me know what you think.
The first draft of my booklet called Theoremus is ready for review – if anyone is interested. I will gratefully thank you in the acknowledgment section for any critiques you may have of it. It is not a thick booklet – it is only 70 pages long and targets for an A5 size print. So an expert can finish reviewing it in a couple of days.
It’s aim is to coach a First Year In Maths student on how to make their proofs more rigorous and thus convincing. That is the broad theme of this booklet – the idea of being rigorous.
After all, why would teachers want you to prove theorems if they do not want you to prove them a bit more rigorously? Surely that should be preferred.
The booklet is suitable for any first year university student who will likely be required to put more solid maths in their course. For example, Comp Sci and Engineering students, like those doing Software and Systems Engineering. It should also be suitable for students of Economics, Finance, Physics, Linguistics, Chemistry and perhaps Biology. Well, of course, it should be for the Maths and Stats students too :-)
Please let me know if you have an interest in reviewing it – your suggestions will be welcomed no matter what angle they come from. Just drop me a line in the comments side of this post and I will send you the draft in PDF. Thank you in advance.
Let the LISP language. Let a programming language different from , i.e. . Let time. Then .
Trivial, just look at the extensions they are making on Python, Ruby, Java, C# and C++11.
It has been said that the smallest piece of code in the world is the C code shown below for copying strings.
while (*d++ = *s++);
That is just a whopping 17 characters long! Really?
I’ll do that in Scheme/Lisp:
(set! d s)
There 9 characters long.