31–40 of 71 posts
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Julia is great, but IMO, it needs to reach 1.0 first before making a splash. It would be frustrating if you found your tools developed on v0.4 stopped working on v0.5 released a year later. API/ABI stability is critical to the adoption of new programming languages/libraries.
I've heard they plan to release Julia 1.0 this year. So keep an eye on it.
I'm glad to see the quick convergence on Python. I've evaluated Python every ~2 years since 2006 for "data science" tasks (machine learning, statistics, data munging, and visualization). I'd argue that Python only properly covered this full data science stack 1.5-2 years ago. R covered this stack adequately probably around 2011-2012. Matlab had this before 2005. What makes Python a superior language to Matlab and R i…
If you work in a team mixing data engineers and data scientists then python has been a superior choice for a decade as the different team members are all using the same language to build the platform and to use it. We've had a lot of success weaning people of R and MATLAB in finance (so replace "data scientist" with "quant"). Of course if you work in a field that doesn't have the libraries and can't build them in hou…
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Also notable that so many new shops are foregoing it entirely. SAS, and its price tag, is a holdover from the days when 'analytics' was an afterthought for companies looking to maximize profit. Now that much less mature companies are realizing the value of 'analytics' (I hate that word) SAS's cost doesn't really make sense.
What word do you prefer to 'analytics', if we are talking about taking data and analysing it to extract insights? This does not have to involve machine learning, and "pattern recognition" is an academic term (and might be confusing for laymen).
Business people seem determined to invent new jargon when our current vocabulary is sufficient.
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What word do you prefer to 'analytics', if we are talking about taking data and analysing it to extract insights? This does not have to involve machine learning, and "pattern recognition" is an academic term (and might be confusing for laymen).
Definitely agreed. But the word analytics means too many different things to too many different people at this point. I'm in favor of more words to differentiate each part.
- descriptive analysis (clustering, summary statistics, etc.)
- predictive analysis / forecasting
- optimization
- automation
I suppose that last one involves engineering as well.
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If you work in a team mixing data engineers and data scientists then python has been a superior choice for a decade as the different team members are all using the same language to build the platform and to use it. We've had a lot of success weaning people of R and MATLAB in finance (so replace "data scientist" with "quant"). Of course if you work in a field that doesn't have the libraries and can't build them in hou…
Off-topic, but what is a "data engineer"?
You could also look it up...
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SAS is also a huge iceberg, without much in the way of open source culture that lends itself to visibility. A lot of SAS happens behind closed doors at megacorps.
Also notable that so many new shops are foregoing it entirely. SAS, and its price tag, is a holdover from the days when 'analytics' was an afterthought for companies looking to maximize profit. Now that much less mature companies are realizing the value of 'analytics' (I hate that word) SAS's cost doesn't really make sense.
I don't actually hate SAS and I'm very productive using it but at the same time I do feel that not knowing R limits my opportunities if I ever want to change jobs and work for an outside organisation.
We don't strictly use SAS for analytics. A big part of SAS we use is the "BI" side I don't know if that acronym is still in vogue but I'm talking about ad-hoc querying reporting etc. The kind of stuff one step above what you'd use a spreadsheet for if that makes sense.
I think where SAS excels is they have made it very easy for non experts to be productive with it. Kind of similar to MATLAB in engineering world if people are familiar with that.
A lot of non statiscians and non programmers use it inside my work (my background is engineering). Accountants, Managers, mechanical engineers etc are all pretty capable of using Enterprise Guide to run adhoc queries and generate reports and the like. The only other similar tool I'm aware of is IBM's Cognos. We used to use both packages (as well as Microsoft Access) but about 10 years ago the business agreed to standardize around SAS. I've heard there is a similar tool in the R world to Enterprise Guide (R-Studio I think???) but I'm not all that familiar with it I've heard it behaves more like an IDE rather then a drag and drop way to construct queries, graphs/reports etc.
If anyone has made the transition from SAS (or Cognos) to R (especially for a large org) I'd be keen to hear what tools you'd recommend and how the business found it?
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Off-topic, but what is a "data engineer"?
I'm sure someone can come along with a better description, but it's kinda like devops/sysadmin but specifically for data storage and access. You could also look it up...
I'm surprised that the author combines "the C languages" saying that most adverts that mention any of C/C++/C# mention all three. In my experience there is a large difference between companies searching for C# developers and those searching for C/C++. After blurring this distinction he concludes that R and Python are "very different languages" while I consider them to be largely overlapping.
Agreed. So far in my search, C# leans toward Microsoft shops seeking C#/.NET whereas C/C++ has been companies searching for embedded software roles.
I'm glad to see the quick convergence on Python. I've evaluated Python every ~2 years since 2006 for "data science" tasks (machine learning, statistics, data munging, and visualization). I'd argue that Python only properly covered this full data science stack 1.5-2 years ago. R covered this stack adequately probably around 2011-2012. Matlab had this before 2005. What makes Python a superior language to Matlab and R i…
> What makes Python a superior language to Matlab and R is the ease of software development. In practice I think the trends we're seeing have more to do with the fact that most universities now teach CS and data science using Python. Given that Python is approximately as good as R, and it's becoming increasingly much easier to find good people to hire, there's very little reason not to be a Python shop.
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Definitely agreed. But the word analytics means too many different things to too many different people at this point. I'm in favor of more words to differentiate each part.
Different types of analysis: - descriptive analysis (clustering, summary statistics, etc.) - predictive analysis / forecasting - optimization - automation I suppose that last one involves engineering as well.
It's... kinda working.