Python or R on the side?

Hi Guys,

I am new to data mining and I was wondering should I be learning Python on the side as I learn RapidMiner? Is there really a benefit to me as a business person to know all the details behind every operation/algorithm. I am trying to find a perfect mix between time spent learning and practice. I feel like RapidMiner provides more then enough operators to apply to most problems, why do I need to learn the code? All the courses on data science I look at are all Python/R related. Should I focus more on statistics? Anyways any advice is highly appreciated. Thank you!

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Thomas_Ott

@asav_yu you sound a lot like where I was a few years ago. My suggestion is start with RapidMiner and learn this platform. You can do >95% of all types of use cases right inside RM without the coding in Python or R.

If you still want to learn a language, I'm very partial to Python. I like it cause I come from an Engineering background and it reminds me of that way of thinking. I can write R scripts if I really have too but it's not my first choice. That's me.

sgenzer

@Thomas_Ott I think that is the best "newbie data scientist" advice I have read in a long time. Well said.

Scott

SGolbert

Hi @asav_yu,

From a business analyst perspective, I think that knowing about machine learning is important. For example having a basic idea on how your classificators or regressors work, and what to do to improve them. A lot of this information you can get in this site (look for the Training menu)!

As you start building more complex processes, you will find that scripting IS necessary. You will be spending most of the time integrating different data sources and transforming data, where tools other than RapidMiner are common. Not everyone uses RM, so it's completely understandable.

That being said, a good course that teaches machine learning and uses Python/R as support is a good investment (you can apply this knowledge on RM). But doing courses focused only on programming may be a waste of your time.

Regards,

Sebastian

kayman

Depends on the purpose. I used both in the past but currently there is hardly anything that I can do with R that I could not do with standard Rapidminer components.

Granted, R allows you to tune and tweak to a deeper level, but same goes for Python.

Python on the other hand has a lot more to offer as R, if you look beyond pure machine learning libraries. Specifically looking at ETL processes, data migration or third party API integration, there is little to imagin or someone wrote a python port for it.