Which Programming Language for Analytics?

I am in the progress of turning towards Python for analytics.

It's not a quick shift, has been going on for a while. I'm busy with my day job as a developer, studying here and there, and have historically got more involved with R. Then I should sometimes see people to avoid getting shot by a Walking Dead fan.

Someone recently told me that my preference for Python is not generally justified when it comes to analytics, to my surprise the alternative he mentioned was Java. Needless to say, I was looking at things from an analyst's perspective, who applies various methods to extract insight from data.

So... is Java Taking Over?

I wanted to double check - but I had to realize it's non-trivial to find (or I was unlucky at finding) a respective and current ranking, more interestingly, a useful visualisation of how languages perform in the analytics segment.

The other thing I realised was it wasn't that difficult to get a grip on the problem. Even if it's a less reliable grip than one stemming from a more complex methodology.

Digging on GitHub

A small code has pulled a lot of data from GitHub. Then it got cross-checked with StackOverflow data.

The (privately drawn) conclusion suggested by the charts is that Python is essentially the best all-purpose choice for analytics currently. Java is coming up fast, but it has a brutal market share anyway, so it's possibly just, as everything else, getting soaked in analytics, while not really driving the change from an end-user's (analyst's) perspective. R appears to fall out of grace slowly, but steadily - with handicaps in the Machine Learning area.

The most recent version of the full document/source code is available here, or by clicking the miniature. At the time reading, I'm probably still in the progress of making improvements to these.
So the conclusion may as well change - but odds are it's accurate. These findings should primarily be a predictor of the future share of languages among now wannabe analysts, and to be representative of the present to a smaller extent.

The Winner - in My Opinion

A summary of my considerations about Python's performance is presented below.

Aspect	Quick Assessment
Analysis	Prime Language, Increasing Share
Spark	Significant, Reducing Share
Machine Learning	Prime Language
Deep Learning	Prime Language
Big Data	Strong Choice
AWS	Strong Choice, Increasing Share
Data Science	Prime Language / Strong Choice, Increasing Share
Mining	Strong Choice
Visualization	Prime Language / Strong Choice, Increasing Share
Chart	Good Enough Choice from a Diverse Competition
Kaggle	Prime Language, Increasing Share (possibly due to R's demise <sniff/>)

For those who have attempted to follow the events in the analytics area, these segments (or the segments these keywords proxy towards) may be familiar and of high importance.

For others, machine learning and deep learning are expected to be the key players for processing data produced and archived at big data levels, and to be able to encompass models for such complex systems as human thinking.

Different Views

As final thought, the situation probably abruptly changes once it is about architects looking to choose an implementation language for some software they plan to provide for analysts. However, the versatility of Python can possibly counterbalance the efficiency trade-offs it brings into the development.

Also mind that GitHub, the (at the time writing) most used source code hosting option, is the vehicle for many courses on Coursera, which then is/was the most popular MOOC, and as such, heavily affects even a massive vehicle like GitHub (gathering data about this is in progress - but it is getting clear already that these courses are close to the heart of R's popularity on GitHub).

For those interested, I also had a quick peek at server-side languages, with the results summarized in this (continuously updating) document.

Tools are not (just) tools

Note to self

I asked someone today whether he still likes maths, and got the response that maths is just a tool.

It made me think whether tools are actually just tools, or slightly more than that.

Accidentally I was about to study innovation management, which reminded me of adoption curves.

The Rogers adoption curve starts with the innovators. These people are the first to get acquainted with an innovation, use a new gadget, try out a new service, etc. Their characteristics include trying out things l'art pour l'art, which means they don't care about any practical value of those attempts.
Just toy with them.

I had two things to note:

1. these people are those who exercise themselves with things without a particular purpose

2. this is very much an analogue of how ADD people (in my imagination) get distracted by literally anything

About 1.

Actually what purpose do we have in mind when we are kids playing with things? Some made up one I think normally drives things, but this can be so minimally targeted as 'joy', 'fun', etc.
Sounds like a good idea to me.

And just because they have the "thing" around, they can play around with it. The presence of the item allows for another degree of freedom.
And people, like a gas, fill the space they are given.

So the new toy gets played with, new routine gets added to what was there before.

Tools as toys, become doors, and so is maths a door, doors get entered, and so will maths likely become part of your future path, once you've played with it.

About 2.

This is just a corollary - I would guess being overloaded with options just as well as overloaded with topics on the internet created its new addict group. Innovators are the ADD of the market.
Positively mad people. See 1. - allowing themselves options, they allow for creativity, by heading against discipline.

Now is a good time to ask - is discipline good or bad then?
Obviously, it depends.

Discipline and minimalism are just two sides of the same coin.
Capitalism and competition made us put things perhaps too much on the minimalist, the efficiency side.

You'll choose the offer for 999 coins but not for 1000 unless the cheaper is noticeably worse.
It's only the resolution of your value perception that needs to be tricked and the quality rot begins, a worse product sold for almost as high a price as the better one.

Discipline is a tool, not an objective. And an option, a freedom to choose. A tool, but once not a must, once you played with it without a need, more than a tool, too.

You can design bottom up as well as top down, and so you can create something without knowing the final design. Just because you didn't slap yourself in the face to get back in line before you'd have found another path.

We shouldn't forget to embrace freedom. And ADD makes people less controllable. A good thing, in some times.

Is education going out of fashion?

Apparently people have finally found their ways to the schools ... or what's going on? They don't seem to search for institutions that much anymore.



It could be interesting to find out what alternative terms people have started to search for.

MOOC's are a very natural first thought. And they have clearly gained interest over time...



Are they all that significant?



Indeed.

So perhaps the above does not mean education is getting out of fashion, maybe as expected, it is actually getting to play a more and more significant role - but who knows.

The interest in academic degrees seems to nearly stagnate, although there is a likely shift towards shorter degrees:

I want to be the internet

I'm in love:

http://www.theuselessweb.com/


/fav hit so far:

http://www.staggeringbeauty.com/

do not miss out on:
http://www.electricboogiewoogie.com/


but others are brilliant, too!
http://endless.horse/

Idle priority: data project contributions

I will mention two here, so that I won't forget them. One is new, that sounds more interesting (of course, new things, always ...)

Amnesty Decoders (update: false trail...)

"Join a global network of digital volunteers helping us research and expose human rights violations."

This is actually something that deceived me big time:

https://decoders.amnesty.org/

They want you! To click on sections of satellite imagery where villages (artificial structures) are present. Hm... I would have thought people (with some proficiency) can contribute to the image analysis with code :(
I wonder why these projects don't end up on Kaggle. Or do they?
This task is absolutely crying out for automatisation ...

... and another one, which I actually started doing something with, from the past:

Open Corporates

https://opencorporates.com/

It is a large database, commercially available for companies - that's how they make their living. It is backed by paid coders as well as enthusiasts, their meetup if someone wants a bit of hacking for good (they do/did provide free means of accessing the data as well, especially for contribution) is at:

http://www.meetup.com/London-Open-Data-Missions/

I never found the time again, so my bot is still 'booting', but would have been nice to. Should.

The task is to create bots, in Python, which do the web scraping (in my days, it happened with BeautifulSoup), and crack up the data (through multiple steps) to extract relevant attributes.

Their stuff is helpful for those doing data journalism, and there are hopes it can help to cut back and/or eliminate corruption hidden behind the ownership graphs.

Why or why not Go [draft]

Gnack, language is a matter of choice, but the choice is a matter of the market, etc.; so this year as usual:
-- Away from me, Pascal!

So what language?

Starts to be apparent that some companies just do choose newer technologies, despite the safe player masses, although it's not always obvious when looking at the overall trend. Workforce performance (I mean code * quality / dev_hours or something similar) increase? I wonder. Secrets, never told.

One example: I've been trying to find the reasons pro and con for learning Go programming, as I'm not a massive C++ fan, but I know I like Python and, after a quick assessment, it didn't seem to be adding a lot over it. Still I doubted my judgement on this (being a master of neither of the two), so I looked at Google Trends and didn't find anything promising. Even Google didn't market it too well :)



However, just today I got reminded by a lecture of ItJobsWatch's charts, and see these UK-wide statistics! (Numbers at the front are approximately correct as of 8/10/2016)

1%: Go on ITJobsWatch

That's a small outbreak :) Mind that, my earlier investments, R and Python have gone way way up, too.

1.5% R on ITJobsWatch

14%: Python on ITJobsWatch

This is how it works! Or rather when it works.

Then add that Docker is written in Go, that CloudFoundry and RabbitMQ and that things people write for people to use, have started to recognize the power of this language, the result starts to get much nicer.

UPDATE:
On Quora they found that the Golang expression statistics show a nice, steep upward Google trend. I'd add that it's never guaranteed that it's a sign of success, while people realize that this expression works, the apparent explosion is possibly just a manifestation of the overtaking of term search numbers from other, related expressions (sort of a cannibalisation). "Golang" to date is backed by fraction of the searches for "Go (programming language)" and "Google go".

The Bug #1: Windows not that much love Go

However, there's this little bug, which still prevents (at the time writing) building Windows DLL's...

So then it doesn't seem like a "platform-neutral" development attempt, but on the other hand, something that has a brand starting "accidentally" like that of Google, forever, and not supporting full scale Windows development. Hm :) Me? Not suspecting a thing. Good question: who knows when?

TODO: Would be nice to have a time vs. number of comments/total length of comments :) when the hell is it going to get closed?

Update: The Bug #2 - unfriendly again, Python would love GoLang if...

So as "Bug #1" says, you'll have to think before building Windows DLL's with Go. Although from the epic talk on the bug page it seems it's only affecting multi-threaded code via the Windows TLS support. However, something like that should work, I guess ... especially if you use Go which prides itself of its multi-threading support.

So (or without noticing why) on SO they compromise on building .so's for Linux.
http://stackoverflow.com/questions/12443203/writing-a-python-extension-in-go-golang

But who wants to really create a *nix-only Python package, or one that may not be extensible at some point - further from internal use? (Think of Anaconda - I guess it's a blocker for more official python distros.)

Then there's gopy also mentioned, for making importing trivial. However, it's still not compatible with go >= 1.6. Even if it's a very good start to creep in to commonplace use as an extension language first.

And Linux is still not even nearly everything.

So seems like there's a little longer while to wait for the ecosystem to get ready for broader market penetration. Exciting moments anyway.

Surely more promising already on the server side!

TODO: Mention fun language - intrinsic motivation - creativity association, weakness in analytics.
TODO: Mention recently created Go on GitHub charts? Create new ones?

An entropy paradox

Entropy/diversity is personally one of my favourite brain-wasting topics (I have my reasons for that, good ones :) ). Here's a short line of thoughts that illustrates the tricky nature of these notions. At the end, I'll probably not connect this back to naive everyday thinking which everyone is taking for granted, as it would be either embarrassing for many or worse even (in case I'm wrong), very embarrassing, but only for me alone :) But beware I think I could (make a fool out of myself)...

So the opening thought is that when we are young, we are more similar to both of our parents, and as we turn older, we will exhibit stuff related to the matching gender parent, i.e. specialize.

Now let's describe this by something that behaves like the Herfindahl index (any entropy measure does the job in one way or another) over a pair of similarity metrics. We'll find that from a pair: (mom_similarity, dad_similarity) closer to (0.5, 0.5) the individual's stats move closer to either (1, 0.0) or (0, 1.0), and that this means this index will increase, suggesting a less diverse individual.

Now let's take a look at this on the macro level, multiply up the aforementioned individual so that it becomes a population (of roughly indetically aged, random gender people which is growing up)!

From a series similar to [(0.5, 0.5), ..., (0.5, 0.5)] we observe a transition towards (assuming equal probabilities for the genders) a series that is more like [(1.0, 0.0), (0.0, 1.0), ...] etc.

What we then find is that the diversity on the macro level did exactly the opposite - a population of randomly grown ups is more diverse than that of babies. Actually that was quite trivial without the numbers already, but the joy and the words with the weird spelling ... :)

So yes, almost paradoxically, micro and macro level entropy may work against each other - the level of abstraction does matter a lot!



P.S.: Don't calm down. I look forward to distribute similarly useless thoughts in the future.